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{"appState":{"pageLoadApiCallsStatus":true},"categoryState":{"relatedCategories":{"headers":{"timestamp":"2025-01-31T04:01:07+00:00"},"categoryId":33578,"data":{"title":"Big Data","slug":"big-data","image":{"src":null,"width":0,"height":0},"breadcrumbs":[{"name":"Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33512"},"slug":"technology","categoryId":33512},{"name":"Information Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33572"},"slug":"information-technology","categoryId":33572},{"name":"Data Science","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33577"},"slug":"data-science","categoryId":33577},{"name":"Big Data","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33578"},"slug":"big-data","categoryId":33578}],"parentCategory":{"categoryId":33577,"title":"Data Science","slug":"data-science","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33577"}},"childCategories":[],"description":"What's the biggest dataset you can imagine? 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So, it’s not an accident that data orchestration is the term that brings everything together.\r\n\r\nBringing all your data together shares similarities with conducting an orchestra. Instead of combining the violin, oboe, and cello, this brand of orchestration combines distributed data types from different places, platforms, and locations working as a cohesive entity presented to applications or users anywhere. That’s because historically, accessing high-performance data outside of your computer network was inefficient. Because the storage infrastructure existed in a silo, systems like HPC Parallel (which lets users store and access shared data across multiple networked storage nodes), Enterprise NAS (which allows large-scale storage and access to other networks), and Global Namespace (virtually simplifies network file systems) were limited when it came to sharing. Because each operated independently, the data within each system was siloed making it a problem collaborating with data sets over multiple locations.\r\n\r\nCollaboration was possible, but too often you lost the ability to have high performance. This Boolean logic decreased potential because having an IT architecture that supported both high performance and collaboration with data sets from different storage silos typically became an either/or decision: You were forced to choose one but never both.\r\n<h2 id=\"tab1\" >What is data orchestration?</h2>\r\nData orchestration is the automated process of taking siloed data from multiple data storage systems and locations, combining and organizing it into a single namespace. Then a high-performance file system can place data in the edge service, data center, or cloud service most optimal for the workload.\r\n\r\nThe recent rise of data analytic applications and artificial intelligence (AI) capabilities has accelerated the use of data across different locations and even different organizations. In the next data cycle, organizations will need both high-performance and agility with their data to compete and thrive in a competitive environment.\r\n\r\nThat means data no longer has a 1:1 relationship with the applications and compute environment that generated it. 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Applications and AI models can access data stored in remote locations while using automated orchestration tools to provide high-performance local access when needed for processing. Organizations can grow their talent pools with access to team members no matter where they reside.\r\n<h2 id=\"tab2\" >Decentralizing the data center</h2>\r\nData collection has become more prominent, and the traditional system of centralized data management has limitations. Issues of centralized data storage can limit the amount of data available to applications. Then, there are the high infrastructure costs when multiple applications are needed to manage and move data, multiple copies of data are retained in different storage systems, and more headcount is needed to manage the complex, disconnected infrastructure environment. Such setbacks suggest that the data center is no longer the center of data and storage system constraints should no longer define data architectures.\r\n\r\nHammerspace specializes in decentralized environments, where data may need to span two or more sites and possibly one or more cloud providers and regions, and/or where a remote workforce needs to collaborate in real time. It enables a global data environment by providing a unified, parallel global file system.\r\n<h2 id=\"tab3\" >Enabling a global data environment</h2>\r\nHammerspace completely revolutionizes previously held notions of how unstructured data architectures should be designed, delivering the performance needed across distributed environments to\r\n<ul>\r\n \t<li>Free workloads from data silos.</li>\r\n \t<li>Eliminate copy proliferation.</li>\r\n \t<li>Provide direct data access through local metadata to applications and users, no matter where the data is stored.</li>\r\n</ul>\r\nThis technology allows organizations to take full advantage of the performance capabilities of any server, storage system, and network anywhere in the world. This capability enables a unified, fast, and efficient global data environment for the entire workflow, from data creation to processing, collaboration, and archiving across edge devices, data centers, and public and private clouds.\r\n\r\nThe days of enterprises struggling with a siloed, distributed, and inefficient data environment are over. It’s time to start expecting more from data architectures with automated data orchestration. Find out how by downloading <em>Unstructured Data Orchestration For Dummies,</em> Hammerspace Special Edition, <a class=\"bookSponsor-btn\" href=\"//hammerspace.com/for-dummies/\" target=\"_blank\" rel=\"noopener\" data-testid=\"bookSponsorDownloadButton\">here</a>.","description":"Getting the most out of your unstructured data is an essential task for any organization these days, especially when considering the disparate storage systems, applications, and user locations. 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The phenomenon is very real and it’s producing concrete benefits in so many different areas – particularly in business. Here you will get to the heart of big data as a business owner or manager: You will take a look at the key terminology you need to understand the crucial big data skills for businesses, ten steps to using big data to make better decisions, and tips for communicating insights from data to your colleagues.","description":"Big data makes big headlines, but it’s much more than just a buzz phrase or the latest business fad. The phenomenon is very real and it’s producing concrete benefits in so many different areas – particularly in business. 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He has advised the Bank of England, Barclays, BP, Fujitsu, HSBC, Mars and others.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9052"}}],"primaryCategoryTaxonomy":{"categoryId":33578,"title":"Big Data","slug":"big-data","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33578"}},"secondaryCategoryTaxonomy":{"categoryId":34253,"title":"General Small Business","slug":"general-small-business","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/34253"}},"tertiaryCategoryTaxonomy":{"categoryId":0,"title":null,"slug":null,"_links":null},"trendingArticles":null,"inThisArticle":[],"relatedArticles":{"fromBook":[{"articleId":140207,"title":"10 Big Data Predictions for the Future","slug":"10-big-data-predictions-for-the-future","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/140207"}},{"articleId":140196,"title":"Big Data: Starting with Strategy","slug":"big-data-starting-with-strategy","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/140196"}},{"articleId":140195,"title":"Overcoming the Big Data Skills Shortage","slug":"overcoming-the-big-data-skills-shortage","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/140195"}},{"articleId":140190,"title":"Understanding Big Data and the Internet of Things","slug":"understanding-big-data-and-the-internet-of-things","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/140190"}},{"articleId":140156,"title":"6 Key Big Data Skills Every Business Needs","slug":"6-key-big-data-skills-every-business-needs","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/140156"}}],"fromCategory":[{"articleId":207996,"title":"Big Data For Dummies Cheat Sheet","slug":"big-data-for-dummies-cheat-sheet","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/207996"}},{"articleId":207478,"title":"Statistics for Big Data For Dummies Cheat Sheet","slug":"statistics-for-big-data-for-dummies-cheat-sheet","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/207478"}},{"articleId":168988,"title":"Integrate Big Data with the Traditional Data Warehouse","slug":"integrate-big-data-with-the-traditional-data-warehouse","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/168988"}},{"articleId":168987,"title":"Best Practices for Big Data Integration","slug":"best-practices-for-big-data-integration","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/168987"}},{"articleId":168985,"title":"How to Analyze Big Data to Get Results","slug":"how-to-analyze-big-data-to-get-results","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/168985"}}]},"hasRelatedBookFromSearch":false,"relatedBook":{"bookId":281550,"slug":"big-data-for-small-business-for-dummies","isbn":"9781119027034","categoryList":["technology","information-technology","data-science","big-data"],"amazon":{"default":"//www.amazon.com/gp/product/1119027039/ref=as_li_tl?ie=UTF8&tag=wiley01-20","ca":"//www.amazon.ca/gp/product/1119027039/ref=as_li_tl?ie=UTF8&tag=wiley01-20","indigo_ca":"//www.tkqlhce.com/click-9208661-13710633?url=//www.chapters.indigo.ca/en-ca/books/product/1119027039-item.html&cjsku=978111945484","gb":"//www.amazon.co.uk/gp/product/1119027039/ref=as_li_tl?ie=UTF8&tag=wiley01-20","de":"//www.amazon.de/gp/product/1119027039/ref=as_li_tl?ie=UTF8&tag=wiley01-20"},"image":{"src":"//coursofppt.com/wp-content/uploads/big-data-for-small-business-for-dummies-cover-9781119027034-203x255.jpg","width":203,"height":255},"title":"Big Data For Small Business For Dummies","testBankPinActivationLink":"","bookOutOfPrint":false,"authorsInfo":"<p><b data-author-id=\"9052\">Bernard Marr</b> helps companies to better manage, measure, report and analyse performance. 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He has advised the Bank of England, Barclays, BP, Fujitsu, HSBC, Mars and others.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9052"}}],"_links":{"self":"//dummies-api.coursofppt.com/v2/books/"}},"collections":[],"articleAds":{"footerAd":"<div class=\"du-ad-region row\" id=\"article_page_adhesion_ad\"><div class=\"du-ad-unit col-md-12\" data-slot-id=\"article_page_adhesion_ad\" data-refreshed=\"false\" \r\n data-target = \"[{&quot;key&quot;:&quot;cat&quot;,&quot;values&quot;:[&quot;technology&quot;,&quot;information-technology&quot;,&quot;data-science&quot;,&quot;big-data&quot;]},{&quot;key&quot;:&quot;isbn&quot;,&quot;values&quot;:[&quot;9781119027034&quot;]}]\" id=\"du-slot-63221b38b4d7b\"></div></div>","rightAd":"<div class=\"du-ad-region row\" id=\"article_page_right_ad\"><div class=\"du-ad-unit col-md-12\" data-slot-id=\"article_page_right_ad\" data-refreshed=\"false\" \r\n data-target = \"[{&quot;key&quot;:&quot;cat&quot;,&quot;values&quot;:[&quot;technology&quot;,&quot;information-technology&quot;,&quot;data-science&quot;,&quot;big-data&quot;]},{&quot;key&quot;:&quot;isbn&quot;,&quot;values&quot;:[&quot;9781119027034&quot;]}]\" id=\"du-slot-63221b38b56d8\"></div></div>"},"articleType":{"articleType":"Cheat Sheet","articleList":[{"articleId":140154,"title":"Understanding Big Data Jargon","slug":"understanding-big-data-jargon","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/140154"}},{"articleId":140156,"title":"6 Key Big Data Skills Every Business Needs","slug":"6-key-big-data-skills-every-business-needs","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/140156"}},{"articleId":140157,"title":"10 Steps to Using Data to Improve Business Decisions","slug":"10-steps-to-using-data-to-improve-business-decisions","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/140157"}},{"articleId":140155,"title":"How to Communicate Insights from Big Data","slug":"how-to-communicate-insights-from-big-data","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/140155"}}],"content":[{"title":"Understanding big data jargon","thumb":null,"image":null,"content":"<p>The technical jargon surrounding big data can seem a little daunting at first. The key phrases and terms you’re likely to come across, with easy-to-understand definitions for each, follow:</p>\n<ul class=\"level-one\">\n<li>\n<p class=\"first-para\"><b>Big data: </b>Increasingly, everything you do leaves a digital trace (or data), which you (and others) can use and analyse. The phrase <i>big data</i> refers to that data being collected and the ability to make use of it.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Big data analytics:</b><i> </i>This is the process of collecting, processing and analysing data to generate insights that inform fact-based decision making. In many cases it involves software-based analysis using algorithms.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Algorithm: </b>A mathematical formula or statistical process run by software to analyse data. It usually involves multiple calculation steps and can be used to automatically process data or solve problems.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Cloud computing:</b><i> </i>Software or data running on remote servers, rather than locally. So instead of storing or computing things on your own machine, you can use other computers that are connected to your computer via a network (such as the Internet).</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Structured data:</b><i> </i>Any data or information located in a fixed field within a defined record or file, such as a database or spreadsheet. Its inherent structure makes it quick, easy and cheap to analyse.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Unstructured data:</b><i> </i>All the data not easily stored and indexed in traditional formats or databases. It includes email conversations, social media posts, video content, photos, voice recordings, sounds and so on. Its lack of structure makes it more difficult to analyse using traditional computer programs.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Semi-structured data:</b><i> </i>You guessed it, this is a cross between unstructured and structured data. It’s data that may have some structure that can be used for analysis but lacks the strict structure found in databases or spreadsheets. For example, a Facebook post can be categorised by author, date, length and even sentiment, but the content is generally unstructured.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Internal data: </b>This accounts for all the data your business currently has or could potentially access or generate in future. It could be structured in format (for example, a customer database) or it could be unstructured (conversational data from customer service calls).</p>\n</li>\n<li>\n<p class=\"first-para\"><b>External data: </b>Put simply, this is the infinite array of information that exists outside your business. It can be publically available or privately held and it can also be structured or unstructured in format.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>The Internet of Things:</b><i> </i>A network that connects devices (the <i>things</i> referred to in the name) so that they can communicate with each other. This encompasses technology like smart televisions, smart phones, and sensors, and it’s all possible thanks to the massive increase in connectivity between devices, systems and services.</p>\n</li>\n</ul>\n"},{"title":"6 key big data skills every business needs","thumb":null,"image":null,"content":"<p>What are the key skills required to use big data successfully? The list here includes six key skills that all businesses should develop, either through recruiting data scientists who match these attributes, or by developing these skills in existing employees:</p>\n<ul class=\"level-one\">\n<li>\n<p class=\"first-para\"><b>Analytics:</b> This involves determining which data is relevant to the question you’re hoping to answer and interpreting the data in order to derive those answers. Key skills include a knack for spotting patterns and establishing links, the ability to make sense of a range of data (both structured and unstructured) and a sound knowledge of industry-standard analytics packages like SAS Analytics and Oracle Data Mining.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Creativity:</b> Anyone can be formulaic – you need to aim for innovation that will set your business apart from the pack. Creativity is especially important for any business hoping to make sense of <i>unstructured data</i> – data that doesn’t fit comfortably into tables and charts. Valuable creative skills include a knack for problem solving (perhaps even spotting problems others aren’t yet aware of) and the ability to come up with new ways of gathering and interpreting data.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Maths and statistics: </b>People with a strong background in maths or statistics have a good grounding for big data-related work. You’re looking for at least a basic grasp of statistics and the ability to wrangle messy data into figures that can be quantified so that you can draw conclusions from them.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Computer science:</b> This very broad category covers a whole range of subfields, such as machine learning, databases and cloud computing. It may cover everything from plugging together the cables to creating sophisticated machine learning and natural language processing algorithms. Key skills include a solid understanding of database technology and a firm grasp of technologies such as Hadoop, Java and Python.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Business acumen: </b>People who work with big data need a firm grasp of the company’s goals and objectives, as well as an understanding of whether the business is heading in the right direction. This includes understanding what makes the company tick, what makes it thrive and why it stands out from its competitors (and if it’s not thriving, why it’s not).</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Communication: </b>You can have the best analytical skills in the world, but unless you’re able to present findings in a clear way and demonstrate how they can help to improve performance and drive success, all that analysis will go to waste. Great interpersonal and written communication skills are vital, as is the ability to add value to data through insights and analysis. A knack for storytelling and being able to bring data to life through visualization techniques will also help immensely.</p>\n</li>\n</ul>\n"},{"title":"10 steps to using data to improve business decisions","thumb":null,"image":null,"content":"<p>Data should be at the heart of strategic decision making in business, whether you run a huge multinational or a small family-run business. Big data can provide insights that help you answer your key business questions, such as ‘How can I improve customer satisfaction?’. Data leads to insights; business owners and managers can turn those insights into decisions and actions that improve the business.</p>\n<p class=\"Tip\">Use this ten-step process for making data-based decisions:</p>\n<ol class=\"level-one\">\n<li>\n<p class=\"first-para\">Start with strategy.</p>\n<p class=\"child-para\">Instead of starting with what data you could or should access, start by working out what your business is looking to achieve. In a nutshell, you need to work out what your strategic goals are, for example, increasing your customer base.</p>\n</li>\n<li>\n<p class=\"first-para\">Hone in on the business area; identify your strategic objectives.</p>\n<p class=\"child-para\">Identify the areas most important to achieving your overall strategy. For most businesses, the customer, finance and operations areas are key.</p>\n</li>\n<li>\n<p class=\"first-para\">Identify unanswered questions.</p>\n<p class=\"child-para\">Work out which questions you need to answer in order to achieve those goals. By working out exactly what you need to know, you can focus on the data that you really need.</p>\n</li>\n<li>\n<p class=\"first-para\">Find the data that will help answer those questions.</p>\n<p class=\"child-para\">Focus on identifying the ideal data for you – the data that could help you answer your most pressing questions and deliver on your strategic objectives.</p>\n</li>\n<li>\n<p class=\"first-para\">Identify what data you already have or have access to.</p>\n<p class=\"child-para\">After you identify the data you need, it makes sense to see if you’re already sitting on some of that information, even if it isn’t immediately obvious.</p>\n</li>\n<li>\n<p class=\"first-para\">Work out if the costs and effort are justified.</p>\n<p class=\"child-para\">Only after you know the costs can you work out if the tangible benefits outweigh those costs. In this respect, you should treat data like any other key business investment. You need to make a clear case for the investment that outlines the long-term value of data to the business strategy.</p>\n</li>\n<li>\n<p class=\"first-para\">Collect the data.</p>\n<p class=\"child-para\">Much of this step comes down to setting up the processes and people to gather and manage your data. You may be buying access to an analysis-ready data set, in which case there’s no need to collect data as such. But, in reality, many data projects require some amount of data collection.</p>\n</li>\n<li>\n<p class=\"first-para\">Analyze the data.</p>\n<p class=\"child-para\">You need to analyze the data in order to extract meaningful and useful business insights. After all, there’s no point coming this far if you don’t then discover something new from the data.</p>\n</li>\n<li>\n<p class=\"first-para\">Present and distribute the insights.</p>\n<p class=\"child-para\">Unless the results are presented to the right people at the right time in a meaningful way, then the size of the data sets or the sophistication of the analytics tools don’t really matter. You need to make sure the insights gained from your data are used to inform decision making and, ultimately, improve performance.</p>\n</li>\n<li>\n<p class=\"first-para\">Incorporate the learning into the business.</p>\n<p class=\"child-para\">Finally, you need to apply the insights from the data to your decision making, making the decisions that will transform your business for the better – and then acting on those decisions. For me, this is the most rewarding part of the data journey: turning data into action.</p>\n</li>\n</ol>\n"},{"title":"How to communicate insights from big data","thumb":null,"image":null,"content":"<p>Big data can help you gain insight. Businesses gain competitive advantage when the <i>right information</i> is delivered to the <i>right people</i> at the <i>right time.</i> This means extracting insights and information from data and communicating them to decision makers in a way they’ll easily understand. After all, people are less likely to act if they have to work hard to understand the information in front of them.</p>\n<p class=\"Tip\">Make sure your insights shine through with these top tips:</p>\n<ul class=\"level-one\">\n<li>\n<p class=\"first-para\"><b>Identify your target audience. </b>Who your audience is depends on your strategic questions. The audience may be you if you’re the business owner, or it could be your human resources team, your marketing team or a combination. Ask yourself who’s going to see these results. What do they already know about the issues being discussed? What do they need and want to know? And, what will they do with the information?</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Customise the information for your audience.</b> Be prepared to customise your information to meet the specific requirements of each decision maker.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Remember what you’re trying to achieve.</b> Try not to get distracted by interesting insights that have nothing to do with answering your strategic questions and achieving your business goals. There may be scope to revisit those other insights in future but, for now, focus on what you set out to achieve.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Avoid creating a wall of text.</b> Remember that data can be presented as a number, a short written narrative, a table, a graph or a chart. In fact, the best approach is likely to involve a combination of these formats.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Use data visualisation techniques.</b> Visuals are great for conveying information because they’re quick and direct, they’re (usually) easy to understand, they’re memorable and they add interest, being much more likely to hold the reader’s attention than a full page of text.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>But don’t neglect the text. </b>Numbers, charts and visuals may only give a snapshot; narrative allows you to embellish on key points. Use short narratives to introduce what you’re showing and highlight the key insights.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Use clear headings to make the important points stand out.</b> This way, even at a quick glance, the key points will be obvious.</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Link the information to your strategy. </b>If you’re presenting information that directly answers a strategic business question, such as ‘How do we reduce staff turnover by ten per cent?’, include that question in the opening narrative and maybe even the headline.</p>\n</li>\n</ul>\n"}],"videoInfo":{"videoId":null,"name":null,"accountId":null,"playerId":null,"thumbnailUrl":null,"description":null,"uploadDate":null}},"sponsorship":{"sponsorshipPage":false,"backgroundImage":{"src":null,"width":0,"height":0},"brandingLine":"","brandingLink":"","brandingLogo":{"src":null,"width":0,"height":0},"sponsorAd":"","sponsorEbookTitle":"","sponsorEbookLink":"","sponsorEbookImage":{"src":null,"width":0,"height":0}},"primaryLearningPath":"Advance","lifeExpectancy":"One year","lifeExpectancySetFrom":"2023-04-12T00:00:00+00:00","dummiesForKids":"no","sponsoredContent":"no","adInfo":"","adPairKey":[]},"status":"publish","visibility":"public","articleId":207432},{"headers":{"creationTime":"2017-03-27T16:47:03+00:00","modifiedTime":"2023-03-10T20:12:30+00:00","timestamp":"2023-09-14T18:19:23+00:00"},"data":{"breadcrumbs":[{"name":"Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33512"},"slug":"technology","categoryId":33512},{"name":"Information Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33572"},"slug":"information-technology","categoryId":33572},{"name":"Data Science","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33577"},"slug":"data-science","categoryId":33577},{"name":"Big Data","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33578"},"slug":"big-data","categoryId":33578}],"title":"Statistics for Big Data For Dummies Cheat Sheet","strippedTitle":"statistics for big data for dummies cheat sheet","slug":"statistics-for-big-data-for-dummies-cheat-sheet","canonicalUrl":"","搜素快速搜所平台改善":{"metaDescription":"Overview of the three types of statistical measures: those of central tendency, central dispersion and association.","noIndex":0,"noFollow":0},"content":"Summary statistical measures represent the key properties of a sample or population as a single numerical value. This has the advantage of providing important information in a very compact form. It also simplifies comparing multiple samples or populations. Summary statistical measures can be divided into three types: measures of central tendency, measures of central dispersion, and measures of association.","description":"Summary statistical measures represent the key properties of a sample or population as a single numerical value. This has the advantage of providing important information in a very compact form. It also simplifies comparing multiple samples or populations. Summary statistical measures can be divided into three types: measures of central tendency, measures of central dispersion, and measures of association.","blurb":"","authors":[{"authorId":9080,"name":"Alan Anderson","slug":"alan-anderson","description":" <p><b>Alan Anderson</b>, PhD is a teacher of finance, economics, statistics, and math at Fordham and Fairfield universities as well as at Manhattanville and Purchase colleges. Outside of the academic environment he has many years of experience working as an economist, risk manager, and fixed income analyst. Alan received his PhD in economics from Fordham University, and an M.S. in financial engineering from Polytechnic University.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9080"}},{"authorId":9081,"name":"David Semmelroth","slug":"david-semmelroth","description":" <p><b>David Semmelroth</b> has two decades of experience translating customer data into actionable insights across the financial services, travel, and entertainment industries. 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Three of the most commonly used measures of central tendency are the mean, median, and mode.</p>\n<h2>Mean</h2>\n<p><i>Mean </i>is another word for average. Here is the formula for computing the mean of a sample:</p>\n<p><img loading=\"lazy\" src=\"//coursofppt.com/wp-content/uploads/484219.image0.jpg\" alt=\"image0.jpg\" width=\"77\" height=\"68\" /></p>\n<p>With this formula, you compute the sample mean by simply adding up all the elements in the sample and then dividing by the number of elements in the sample.</p>\n<p>Here is the corresponding formula for computing the mean of a population:</p>\n<p><img loading=\"lazy\" src=\"//coursofppt.com/wp-content/uploads/484220.image1.jpg\" alt=\"image1.jpg\" width=\"73\" height=\"68\" /></p>\n<p>Although the notation is slightly different, the procedure for computing a population mean is the same as the procedure for computing a sample mean.</p>\n<p class=\"Tip\">Greek letters are used to describe populations, whereas Roman letters are used to describe samples.</p>\n<h2>Median</h2>\n<p>The <i>median</i> of a data set is a value that divides the data into two equal halves. In other words, half of the elements of a data set are <i>less than </i>the median, and the remaining half are <i>greater than </i>the median. The procedure for computing the median is the same for both samples and populations.</p>\n<h2>Mode</h2>\n<p>The mode of a data set is the most commonly observed value in the data set. You determine the mode in the same way for a sample and a population.</p>\n"},{"title":"Measures of central dispersion","thumb":null,"image":null,"content":"<p>Measures of central dispersion show how &#8220;spread out&#8221; the elements of a data set are from the mean. Three of the most commonly used measures of central dispersion include the following:</p>\n<ul class=\"level-one\">\n<li>\n<p class=\"first-para\">Range</p>\n</li>\n<li>\n<p class=\"first-para\">Variance</p>\n</li>\n<li>\n<p class=\"first-para\">Standard deviation</p>\n</li>\n</ul>\n<h2>Range</h2>\n<p>The <i>range</i> of a data set is the difference between the largest value and the smallest value. You compute it the same way for both samples and populations.</p>\n<h2>Variance</h2>\n<p>You can think of the variance as the average <i>squared</i> difference between the elements of a data set and the mean. The formulas for computing a sample variance and a population variance are slightly different.</p>\n<p>Here is the formula for computing sample variance:</p>\n<p><img loading=\"lazy\" src=\"//coursofppt.com/wp-content/uploads/484223.image0.jpg\" alt=\"image0.jpg\" width=\"130\" height=\"68\" /></p>\n<p>And here is the formula for computing population variance:</p>\n<p><img loading=\"lazy\" src=\"//coursofppt.com/wp-content/uploads/484224.image1.jpg\" alt=\"image1.jpg\" width=\"130\" height=\"68\" /></p>\n<h2>Standard deviation</h2>\n<p>The standard deviation is simply the square root of the variance. It&#8217;s more commonly used as a measure of dispersion than the variance because it&#8217;s measured in the same units as the elements of the data set, whereas the variance is measured in <i>squared </i>units.</p>\n"},{"title":"Measures of association","thumb":null,"image":null,"content":"<p>Measures of association quantify the strength and the direction of the relationship between two data sets. Here are the two most commonly used measures of association:</p>\n<ul class=\"level-one\">\n<li>\n<p class=\"first-para\">Covariance</p>\n</li>\n<li>\n<p class=\"first-para\">Correlation</p>\n</li>\n</ul>\n<p>Both measures are used to show how closely two data sets are related to each other. The main difference between them is the units in which they are measured. The correlation measure is defined to assume values between –1 and 1, which makes interpretation very easy.</p>\n<h2>Covariance</h2>\n<p>The <i>covariance</i> between two samples is computed as follows:</p>\n<p><img loading=\"lazy\" src=\"//coursofppt.com/wp-content/uploads/484213.image0.jpg\" alt=\"image0.jpg\" width=\"189\" height=\"68\" /></p>\n<p>The covariance between two populations is computed as follows:</p>\n<p><img loading=\"lazy\" src=\"//coursofppt.com/wp-content/uploads/484214.image1.jpg\" alt=\"image1.jpg\" width=\"208\" height=\"68\" /></p>\n<h2>Correlation</h2>\n<p>The <i>correlation</i> between two samples is computed like this:</p>\n<p><img loading=\"lazy\" src=\"//coursofppt.com/wp-content/uploads/484215.image2.jpg\" alt=\"image2.jpg\" width=\"84\" height=\"44\" /></p>\n<p>The correlation between two populations is computed like this:</p>\n<p><img loading=\"lazy\" src=\"//coursofppt.com/wp-content/uploads/484216.image3.jpg\" alt=\"image3.jpg\" width=\"94\" height=\"44\" /></p>\n"}],"videoInfo":{"videoId":null,"name":null,"accountId":null,"playerId":null,"thumbnailUrl":null,"description":null,"uploadDate":null}},"sponsorship":{"sponsorshipPage":false,"backgroundImage":{"src":null,"width":0,"height":0},"brandingLine":"","brandingLink":"","brandingLogo":{"src":null,"width":0,"height":0},"sponsorAd":"","sponsorEbookTitle":"","sponsorEbookLink":"","sponsorEbookImage":{"src":null,"width":0,"height":0}},"primaryLearningPath":"Advance","lifeExpectancy":"Five years","lifeExpectancySetFrom":"2023-03-10T00:00:00+00:00","dummiesForKids":"no","sponsoredContent":"no","adInfo":"","adPairKey":[]},"status":"publish","visibility":"public","articleId":207478},{"headers":{"creationTime":"2017-03-27T16:49:35+00:00","modifiedTime":"2023-02-09T18:46:11+00:00","timestamp":"2023-09-14T18:19:05+00:00"},"data":{"breadcrumbs":[{"name":"Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33512"},"slug":"technology","categoryId":33512},{"name":"Information Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33572"},"slug":"information-technology","categoryId":33572},{"name":"Data Science","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33577"},"slug":"data-science","categoryId":33577},{"name":"Big Data","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33578"},"slug":"big-data","categoryId":33578}],"title":"Big Data For Dummies Cheat Sheet","strippedTitle":"big data for dummies cheat sheet","slug":"big-data-for-dummies-cheat-sheet","canonicalUrl":"","搜素快速搜所平台改善":{"metaDescription":"Learn the essentials of managing big data, including unstructured data, infrastructure, what Hadoop is, and how to develop a strategy.","noIndex":0,"noFollow":0},"content":"To stay competitive today, companies must find practical ways to deal with big data — that is, to learn new ways to capture and analyze growing amounts of information about customers, products, and services.\r\n\r\nData is becoming increasingly complex in structured and unstructured ways. New sources of data come from machines, such as sensors; social business sites; and website interaction, such as click-stream data. Meeting these changing business requirements demands that the right information be available at the right time.","description":"To stay competitive today, companies must find practical ways to deal with big data — that is, to learn new ways to capture and analyze growing amounts of information about customers, products, and services.\r\n\r\nData is becoming increasingly complex in structured and unstructured ways. New sources of data come from machines, such as sensors; social business sites; and website interaction, such as click-stream data. Meeting these changing business requirements demands that the right information be available at the right time.","blurb":"","authors":[{"authorId":9411,"name":"Judith S. 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Alan Nugent has extensive experience in cloud-based big data solutions. Dr. Fern Halper specializes in big data and analytics. 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Hurwitz","slug":"judith-s-hurwitz","description":" <p><b>Daniel Kirsch,</b> Managing Director of Hurwitz &#38; Associates, is a thought leader, researcher, author, and consultant in cloud, AI, and security. <b>Judith Hurwitz,</b> President of Hurwitz &#38; Associates, is a consultant, thought leader, and coauthor of 10 books including <i>Augmented Intelligence, Cognitive Computing and Big Data Analytics,</i> and <i>Hybrid Cloud for Dummies</i> ","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/34961"}},{"authorId":9412,"name":"Marcia Kaufman","slug":"marcia-kaufman","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9412"}}],"_links":{"self":"//dummies-api.coursofppt.com/v2/books/"}},"collections":[],"articleAds":{"footerAd":"<div class=\"du-ad-region row\" id=\"article_page_adhesion_ad\"><div class=\"du-ad-unit col-md-12\" data-slot-id=\"article_page_adhesion_ad\" data-refreshed=\"false\" \r\n data-target = \"[{&quot;key&quot;:&quot;cat&quot;,&quot;values&quot;:[&quot;technology&quot;,&quot;information-technology&quot;,&quot;data-science&quot;,&quot;big-data&quot;]},{&quot;key&quot;:&quot;isbn&quot;,&quot;values&quot;:[&quot;9781118504222&quot;]}]\" id=\"du-slot-63221b19ad645\"></div></div>","rightAd":"<div class=\"du-ad-region row\" id=\"article_page_right_ad\"><div class=\"du-ad-unit col-md-12\" data-slot-id=\"article_page_right_ad\" data-refreshed=\"false\" \r\n data-target = \"[{&quot;key&quot;:&quot;cat&quot;,&quot;values&quot;:[&quot;technology&quot;,&quot;information-technology&quot;,&quot;data-science&quot;,&quot;big-data&quot;]},{&quot;key&quot;:&quot;isbn&quot;,&quot;values&quot;:[&quot;9781118504222&quot;]}]\" id=\"du-slot-63221b19ae0eb\"></div></div>"},"articleType":{"articleType":"Cheat Sheet","articleList":[{"articleId":167843,"title":"Defining Big Data: Volume, Velocity, and Variety","slug":"defining-big-data-volume-velocity-and-variety","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/167843"}},{"articleId":167841,"title":"Understanding Unstructured Data","slug":"understanding-unstructured-data","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/167841"}},{"articleId":167840,"title":"The Role of Traditional Operational Data in the Big Data Environment","slug":"the-role-of-traditional-operational-data-in-the-big-data-environment","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/167840"}},{"articleId":167839,"title":"Basics of Big Data Infrastructure","slug":"basics-of-big-data-infrastructure","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/167839"}},{"articleId":167835,"title":"Managing Big Data with Hadoop: HDFS and MapReduce","slug":"managing-big-data-with-hadoop-hdfs-and-mapreduce","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/167835"}},{"articleId":167834,"title":"Laying the Groundwork for Your Big Data Strategy","slug":"laying-the-groundwork-for-your-big-data-strategy","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/167834"}}],"content":[{"title":"Defining big data: Volume, Velocity, and Variety","thumb":null,"image":null,"content":"<p>Big data enables organizations to store, manage, and manipulate vast amounts of disparate data at the right speed and at the right time. To gain the right insights, big data is typically broken down by three characteristics:</p>\n<ul class=\"level-one\">\n<li>\n<p class=\"first-para\"><b>Volume:</b> How much data</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Velocity:</b> How fast data is processed</p>\n</li>\n<li>\n<p class=\"first-para\"><b>Variety:</b> The various types of data</p>\n</li>\n</ul>\n<p>While it is convenient to simplify big data into the three Vs, it can be misleading and overly simplistic. For example, you may be managing a relatively small amount of very disparate, complex data or you may be processing a huge volume of very simple data. That simple data may be all structured or all unstructured.</p>\n<p class=\"Remember\">Even more important is the fourth V, <i>veracity.</i> How accurate is that data in predicting business value? Do the results of a big data analysis actually make sense? Data must be able to be verified based on both accuracy and context. An innovative business may want to be able to analyze massive amounts of data in real time to quickly assess the value of that customer and the potential to provide additional offers to that customer. It is necessary to identify the right amount and types of data that can be analyzed in real time to impact business outcomes.</p>\n<p>Big data incorporates all the varieties of data, including structured data and unstructured data from e-mails, social media, text streams, and so on. This kind of data management requires companies to leverage both their structured and unstructured data.</p>\n"},{"title":"Understanding unstructured data","thumb":null,"image":null,"content":"<p>Unstructured data is different than structured data in that its structure is unpredictable. Examples of unstructured data include documents, e-mails, blogs, digital images, videos, and satellite imagery. It also includes some data generated by machines or sensors. In fact, unstructured data accounts for the majority of data that&#8217;s on your company&#8217;s premises as well as external to your company in online private and public sources such as Twitter and Facebook.</p>\n<p>In the past, most companies weren&#8217;t able to either capture or store this vast amount of data. It was simply too expensive or too overwhelming. Even if companies were able to capture the data, they didn&#8217;t have the tools to easily analyze the data and use the results to make decisions. Very few tools could make sense of these vast amounts of data. The tools that did exist were complex to use and did not produce results in a reasonable time frame.</p>\n<p>In the end, those who really wanted to go to the enormous effort of analyzing this data were forced to work with snapshots of data. This has the undesirable effect of missing important events because they were not in a particular snapshot.</p>\n<p class=\"Remember\">One approach that is becoming increasingly valued as a way to gain business value from unstructured data is <i>text analytics,</i> the process of analyzing unstructured text, extracting relevant information, and transforming it into structured information that can then be leveraged in various ways. The analysis and extraction processes take advantage of techniques that originated in computational linguistics, statistics, and other computer science disciplines.</p>\n"},{"title":"The role of traditional operational data in the big data environment","thumb":null,"image":null,"content":"<p>Knowing what data is stored and where it is stored are critical building blocks in your big data implementation. It&#8217;s unlikely that you&#8217;ll use RDBMSs for the core of the implementation, but it&#8217;s very likely that you&#8217;ll need to rely on the data stored in RDBMSs to create the highest level of value to the business with big data.</p>\n<p class=\"TechnicalStuff\">Most large and small companies probably store most of their important operational information in relational database management systems (RDBMSs), which are built on one or more relations and represented by tables. These tables are defined by the way the data is stored.The data is stored in database objects called tables — organized in rows and columns. RDBMSs follow a consistent approach in the way that data is stored and retrieved.</p>\n<p>To get the most business value from your real-time analysis of unstructured data, you need to understand that data in context with your historical data on customers, products, transactions, and operations. In other words, you will need to integrate your unstructured data with your traditional operational data.</p>\n"},{"title":"Basics of big data infrastructure","thumb":null,"image":null,"content":"<p>Big data is all about high velocity, large volumes, and wide data variety, so the physical infrastructure will literally &#8220;make or break&#8221; the implementation. Most big data implementations need to be highly available, so the networks, servers, and physical storage must be resilient and redundant.</p>\n<p class=\"Remember\">Resiliency and redundancy are interrelated. An infrastructure, or a system, is resilient to failure or changes when sufficient redundant resources are in place ready to jump into action. Resiliency helps to eliminate single points of failure in your infrastructure. For example, if only one network connection exists between your business and the Internet, you have no network redundancy, and the infrastructure is not resilient with respect to a network outage.</p>\n<p>In large data centers with business continuity requirements, most of the redundancy is in place and can be leveraged to create a big data environment. In new implementations, the designers have the responsibility to map the deployment to the needs of the business based on costs and performance.</p>\n"},{"title":"Managing big data with Hadoop: HDFS and MapReduce","thumb":null,"image":null,"content":"<p>Hadoop, an open-source software framework, uses HDFS (the Hadoop Distributed File System) and MapReduce to analyze big data on clusters of commodity hardware—that is, in a distributed computing environment.</p>\n<p>The Hadoop Distributed File System (HDFS) was developed to allow companies to more easily manage huge volumes of data in a simple and pragmatic way. Hadoop allows big problems to be decomposed into smaller elements so that analysis can be done quickly and cost effectively. HDFS is a versatile, resilient, clustered approach to managing files in a big data environment.</p>\n<p>HDFS is not the final destination for files. Rather it is a data &#8220;service&#8221; that offers a unique set of capabilities needed when data volumes and velocity are high.</p>\n<p>MapReduce is a software framework that enables developers to write programs that can process massive amounts of unstructured data in parallel across a distributed group of processors. MapReduce was designed by Google as a way of efficiently executing a set of functions against a large amount of data in batch mode.</p>\n<p>The &#8220;map&#8221; component distributes the programming problem or tasks across a large number of systems and handles the placement of the tasks in a way that balances the load and manages recovery from failures. After the distributed computation is completed, another function called &#8220;reduce&#8221; aggregates all the elements back together to provide a result. An example of MapReduce usage would be to determine how many pages of a book are written in each of 50 different languages.</p>\n"},{"title":"Laying the groundwork for your big data strategy","thumb":null,"image":null,"content":"<p>Companies are swimming in big data. The problem is that they often don&#8217;t know how to pragmatically use that data to be able to predict the future, execute important business processes, or simply gain new insights. The goal of your big data strategy and plan should be to find a pragmatic way to leverage data for more predictable business outcomes.</p>\n<p>Begin your big data strategy by embarking on a discovery process. You need to get a handle on what data you already have, where it is, who owns and controls it, and how it is currently used. For example, what are the third-party data sources that your company relies on? This process can give you a lot of insights:</p>\n<ul class=\"level-one\">\n<li>\n<p class=\"first-para\">You can determine how many data sources you have and how much overlap exists.</p>\n</li>\n<li>\n<p class=\"first-para\">You can identify gaps exist in knowledge about those data sources.</p>\n</li>\n<li>\n<p class=\"first-para\">You might discover that you have lots of duplicate data in one area of the business and almost no data in another area.</p>\n</li>\n<li>\n<p class=\"first-para\">You might ascertain that you are dependent on third-party data that isn&#8217;t as accurate as it should be.</p>\n</li>\n</ul>\n<p class=\"Remember\">Spend the time you need to do this discovery process because it will be the foundation for your planning and execution of your big data strategy.</p>\n"}],"videoInfo":{"videoId":null,"name":null,"accountId":null,"playerId":null,"thumbnailUrl":null,"description":null,"uploadDate":null}},"sponsorship":{"sponsorshipPage":false,"backgroundImage":{"src":null,"width":0,"height":0},"brandingLine":"","brandingLink":"","brandingLogo":{"src":null,"width":0,"height":0},"sponsorAd":"","sponsorEbookTitle":"","sponsorEbookLink":"","sponsorEbookImage":{"src":null,"width":0,"height":0}},"primaryLearningPath":"Advance","lifeExpectancy":"One year","lifeExpectancySetFrom":"2023-02-09T00:00:00+00:00","dummiesForKids":"no","sponsoredContent":"no","adInfo":"","adPairKey":[]},"status":"publish","visibility":"public","articleId":207996},{"headers":{"creationTime":"2017-03-26T15:32:00+00:00","modifiedTime":"2017-03-26T15:32:00+00:00","timestamp":"2023-09-14T18:05:35+00:00"},"data":{"breadcrumbs":[{"name":"Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33512"},"slug":"technology","categoryId":33512},{"name":"Information Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33572"},"slug":"information-technology","categoryId":33572},{"name":"Data Science","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33577"},"slug":"data-science","categoryId":33577},{"name":"Big Data","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33578"},"slug":"big-data","categoryId":33578}],"title":"Integrate Big Data with the Traditional Data Warehouse","strippedTitle":"integrate big data with the traditional data warehouse","slug":"integrate-big-data-with-the-traditional-data-warehouse","canonicalUrl":"","搜素快速搜所平台改善":{"metaDescription":"While the worlds of big data and the traditional data warehouse will intersect, they are unlikely to merge anytime soon. Think of a data warehouse as a system o","noIndex":0,"noFollow":0},"content":"<p>While the worlds of big data and the traditional data warehouse will intersect, they are unlikely to merge anytime soon. Think of a data warehouse as a system of record for business intelligence, much like a customer relationship management (CRM) or accounting system. These systems are highly structured and optimized for specific purposes. In addition, these systems of record tend to be highly centralized.</p>\n<p>The diagram shows a typical approach to data flows with warehouses and marts:</p>\n<p><img src=\"//coursofppt.com/wp-content/uploads/362967.image0.jpg\" width=\"400\" height=\"289\" alt=\"image0.jpg\"/></p>\n<p>Organizations will inevitably continue to use data warehouses to manage the type of structured and operational data that characterizes systems of record. These data warehouses will still provide business analysts with the ability to analyze key data, trends, and so on. However, the advent of big data is both challenging the role of the data warehouse and providing a complementary approach.</p>\n<p class=\"Remember\">Think of the relationship between the data warehouse and big data as merging to become a hybrid structure. In this hybrid model, the highly structured optimized operational data remains in the tightly controlled data warehouse, while the data that is highly distributed and subject to change in real time is controlled by a Hadoop-based (or similar NoSQL) infrastructure.</p>\n<p>It's inevitable that operational and structured data will have to interact in the world of big data, where the information sources have not (necessarily) been cleansed or profiled. Increasingly, organizations are understanding that they have a business requirement to be able to combine traditional data warehouses with their historical business data sources with less structured and vetted big data sources. A hybrid approach supporting traditional and big data sources can help to accomplish these business goals.</p>","description":"<p>While the worlds of big data and the traditional data warehouse will intersect, they are unlikely to merge anytime soon. Think of a data warehouse as a system of record for business intelligence, much like a customer relationship management (CRM) or accounting system. These systems are highly structured and optimized for specific purposes. In addition, these systems of record tend to be highly centralized.</p>\n<p>The diagram shows a typical approach to data flows with warehouses and marts:</p>\n<p><img src=\"//coursofppt.com/wp-content/uploads/362967.image0.jpg\" width=\"400\" height=\"289\" alt=\"image0.jpg\"/></p>\n<p>Organizations will inevitably continue to use data warehouses to manage the type of structured and operational data that characterizes systems of record. These data warehouses will still provide business analysts with the ability to analyze key data, trends, and so on. However, the advent of big data is both challenging the role of the data warehouse and providing a complementary approach.</p>\n<p class=\"Remember\">Think of the relationship between the data warehouse and big data as merging to become a hybrid structure. In this hybrid model, the highly structured optimized operational data remains in the tightly controlled data warehouse, while the data that is highly distributed and subject to change in real time is controlled by a Hadoop-based (or similar NoSQL) infrastructure.</p>\n<p>It's inevitable that operational and structured data will have to interact in the world of big data, where the information sources have not (necessarily) been cleansed or profiled. Increasingly, organizations are understanding that they have a business requirement to be able to combine traditional data warehouses with their historical business data sources with less structured and vetted big data sources. A hybrid approach supporting traditional and big data sources can help to accomplish these business goals.</p>","blurb":"","authors":[{"authorId":9411,"name":"Judith S. Hurwitz","slug":"judith-hurwitz","description":"Judith Hurwitz is president and CEO of Hurwitz & Associates, specializing in cloud computing, service management, information management, and business strategy.","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9411"}},{"authorId":9727,"name":"Alan Nugent","slug":"alan-nugent","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9727"}},{"authorId":9413,"name":"Fern Halper","slug":"fern-halper","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9413"}},{"authorId":9412,"name":"Marcia Kaufman","slug":"marcia-kaufman","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9412"}}],"primaryCategoryTaxonomy":{"categoryId":33578,"title":"Big 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Alan Nugent has extensive experience in cloud-based big data solutions. Dr. Fern Halper specializes in big data and analytics. 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Hurwitz","slug":"judith-s-hurwitz","description":" <p><b>Daniel Kirsch,</b> Managing Director of Hurwitz &#38; Associates, is a thought leader, researcher, author, and consultant in cloud, AI, and security. <b>Judith Hurwitz,</b> President of Hurwitz &#38; Associates, is a consultant, thought leader, and coauthor of 10 books including <i>Augmented Intelligence, Cognitive Computing and Big Data Analytics,</i> and <i>Hybrid Cloud for Dummies</i> ","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/34961"}},{"authorId":9412,"name":"Marcia Kaufman","slug":"marcia-kaufman","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9412"}}],"_links":{"self":"//dummies-api.coursofppt.com/v2/books/"}},"collections":[],"articleAds":{"footerAd":"<div class=\"du-ad-region row\" id=\"article_page_adhesion_ad\"><div class=\"du-ad-unit col-md-12\" data-slot-id=\"article_page_adhesion_ad\" data-refreshed=\"false\" \r\n data-target = \"[{&quot;key&quot;:&quot;cat&quot;,&quot;values&quot;:[&quot;technology&quot;,&quot;information-technology&quot;,&quot;data-science&quot;,&quot;big-data&quot;]},{&quot;key&quot;:&quot;isbn&quot;,&quot;values&quot;:[&quot;9781118504222&quot;]}]\" id=\"du-slot-632217efae5d1\"></div></div>","rightAd":"<div class=\"du-ad-region row\" id=\"article_page_right_ad\"><div class=\"du-ad-unit col-md-12\" data-slot-id=\"article_page_right_ad\" data-refreshed=\"false\" \r\n data-target = \"[{&quot;key&quot;:&quot;cat&quot;,&quot;values&quot;:[&quot;technology&quot;,&quot;information-technology&quot;,&quot;data-science&quot;,&quot;big-data&quot;]},{&quot;key&quot;:&quot;isbn&quot;,&quot;values&quot;:[&quot;9781118504222&quot;]}]\" id=\"du-slot-632217efaee69\"></div></div>"},"articleType":{"articleType":"Articles","articleList":null,"content":null,"videoInfo":{"videoId":null,"name":null,"accountId":null,"playerId":null,"thumbnailUrl":null,"description":null,"uploadDate":null}},"sponsorship":{"sponsorshipPage":false,"backgroundImage":{"src":null,"width":0,"height":0},"brandingLine":"","brandingLink":"","brandingLogo":{"src":null,"width":0,"height":0},"sponsorAd":"","sponsorEbookTitle":"","sponsorEbookLink":"","sponsorEbookImage":{"src":null,"width":0,"height":0}},"primaryLearningPath":"Advance","lifeExpectancy":null,"lifeExpectancySetFrom":null,"dummiesForKids":"no","sponsoredContent":"no","adInfo":"","adPairKey":[]},"status":"publish","visibility":"public","articleId":168988},{"headers":{"creationTime":"2017-03-26T15:31:59+00:00","modifiedTime":"2017-03-26T15:31:59+00:00","timestamp":"2023-09-14T18:05:35+00:00"},"data":{"breadcrumbs":[{"name":"Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33512"},"slug":"technology","categoryId":33512},{"name":"Information Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33572"},"slug":"information-technology","categoryId":33572},{"name":"Data Science","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33577"},"slug":"data-science","categoryId":33577},{"name":"Big Data","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33578"},"slug":"big-data","categoryId":33578}],"title":"Best Practices for Big Data Integration","strippedTitle":"best practices for big data integration","slug":"best-practices-for-big-data-integration","canonicalUrl":"","搜素快速搜所平台改善":{"metaDescription":"Many companies are exploring big data problems and coming up with some innovative solutions. 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Don't expect to be able to control data quality when you do your initial analysis on huge volumes of data. However, when you narrow down your big data to identify a subset that is most meaningful to your organization, this is when you need to focus on data quality. </p>\n<p class=\"child-para\">Ultimately, data quality becomes important if you want your results to be understood n context with your historical data. As your company relies more and more on analytics as a key planning tool, data quality can mean the difference between success and failure.</p>\n </li>\n <li><p class=\"first-para\"><b>Consider real-time data requirements.</b> Big data will bring streaming data to the forefront. Therefore, you will have to have a clear understanding of how you integrate data in motion into your environment for predictable analysis.</p>\n </li>\n <li><p class=\"first-para\"><b>Don't create new silos of information. </b>While so much of the emphasis around big data is focused on Hadoop and other unstructured and semi-structured sources, remember that you have to manage this data in context with the business. You will therefore need to integrate these sources with your line of business data and your data warehouse. </p>\n </li>\n</ul>","description":"<p>Many companies are exploring big data problems and coming up with some innovative solutions. Now is the time to pay attention to some <i>best practices,</i> or basic principles, that will serve you well as you begin your big data journey.</p>\n<p class=\"Remember\">In reality, big data integration fits into the overall process of integration of data across your company. Therefore, you can't simply toss aside everything you have learned from data integration of traditional data sources. The same rules apply whether you are thinking about traditional data management or big data management. </p>\n<p>Keep these key issues at the top of your priority list for big data integration:</p>\n<ul class=\"level-one\">\n <li><p class=\"first-para\"><b>Keep data quality in perspective. </b>Your emphasis on<b> </b>data quality depends on the stage of your big data analysis. Don't expect to be able to control data quality when you do your initial analysis on huge volumes of data. However, when you narrow down your big data to identify a subset that is most meaningful to your organization, this is when you need to focus on data quality. </p>\n<p class=\"child-para\">Ultimately, data quality becomes important if you want your results to be understood n context with your historical data. As your company relies more and more on analytics as a key planning tool, data quality can mean the difference between success and failure.</p>\n </li>\n <li><p class=\"first-para\"><b>Consider real-time data requirements.</b> Big data will bring streaming data to the forefront. Therefore, you will have to have a clear understanding of how you integrate data in motion into your environment for predictable analysis.</p>\n </li>\n <li><p class=\"first-para\"><b>Don't create new silos of information. </b>While so much of the emphasis around big data is focused on Hadoop and other unstructured and semi-structured sources, remember that you have to manage this data in context with the business. You will therefore need to integrate these sources with your line of business data and your data warehouse. </p>\n </li>\n</ul>","blurb":"","authors":[{"authorId":9411,"name":"Judith S. Hurwitz","slug":"judith-hurwitz","description":"Judith Hurwitz is president and CEO of Hurwitz & Associates, specializing in cloud computing, service management, information management, and business strategy.","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9411"}},{"authorId":9727,"name":"Alan Nugent","slug":"alan-nugent","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9727"}},{"authorId":9413,"name":"Fern Halper","slug":"fern-halper","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9413"}},{"authorId":9412,"name":"Marcia Kaufman","slug":"marcia-kaufman","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9412"}}],"primaryCategoryTaxonomy":{"categoryId":33578,"title":"Big 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Data For Dummies","testBankPinActivationLink":"","bookOutOfPrint":false,"authorsInfo":"<p><b data-author-id=\"34961\">Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy. Alan Nugent has extensive experience in cloud-based big data solutions. Dr. Fern Halper specializes in big data and analytics. Marcia Kaufman specializes in cloud infrastructure, information management, and analytics.</p>","authors":[{"authorId":9727,"name":"Alan Nugent","slug":"alan-nugent","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9727"}},{"authorId":9413,"name":"Fern Halper","slug":"fern-halper","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9413"}},{"authorId":34961,"name":"Judith S. Hurwitz","slug":"judith-s-hurwitz","description":" <p><b>Daniel Kirsch,</b> Managing Director of Hurwitz &#38; Associates, is a thought leader, researcher, author, and consultant in cloud, AI, and security. <b>Judith Hurwitz,</b> President of Hurwitz &#38; Associates, is a consultant, thought leader, and coauthor of 10 books including <i>Augmented Intelligence, Cognitive Computing and Big Data Analytics,</i> and <i>Hybrid Cloud for Dummies</i> ","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/34961"}},{"authorId":9412,"name":"Marcia Kaufman","slug":"marcia-kaufman","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9412"}}],"_links":{"self":"//dummies-api.coursofppt.com/v2/books/"}},"collections":[],"articleAds":{"footerAd":"<div class=\"du-ad-region row\" id=\"article_page_adhesion_ad\"><div class=\"du-ad-unit col-md-12\" data-slot-id=\"article_page_adhesion_ad\" data-refreshed=\"false\" \r\n data-target = \"[{&quot;key&quot;:&quot;cat&quot;,&quot;values&quot;:[&quot;technology&quot;,&quot;information-technology&quot;,&quot;data-science&quot;,&quot;big-data&quot;]},{&quot;key&quot;:&quot;isbn&quot;,&quot;values&quot;:[&quot;9781118504222&quot;]}]\" id=\"du-slot-632217ef9e8c2\"></div></div>","rightAd":"<div class=\"du-ad-region row\" id=\"article_page_right_ad\"><div class=\"du-ad-unit col-md-12\" data-slot-id=\"article_page_right_ad\" data-refreshed=\"false\" \r\n data-target = \"[{&quot;key&quot;:&quot;cat&quot;,&quot;values&quot;:[&quot;technology&quot;,&quot;information-technology&quot;,&quot;data-science&quot;,&quot;big-data&quot;]},{&quot;key&quot;:&quot;isbn&quot;,&quot;values&quot;:[&quot;9781118504222&quot;]}]\" id=\"du-slot-632217ef9f133\"></div></div>"},"articleType":{"articleType":"Articles","articleList":null,"content":null,"videoInfo":{"videoId":null,"name":null,"accountId":null,"playerId":null,"thumbnailUrl":null,"description":null,"uploadDate":null}},"sponsorship":{"sponsorshipPage":false,"backgroundImage":{"src":null,"width":0,"height":0},"brandingLine":"","brandingLink":"","brandingLogo":{"src":null,"width":0,"height":0},"sponsorAd":"","sponsorEbookTitle":"","sponsorEbookLink":"","sponsorEbookImage":{"src":null,"width":0,"height":0}},"primaryLearningPath":"Advance","lifeExpectancy":null,"lifeExpectancySetFrom":null,"dummiesForKids":"no","sponsoredContent":"no","adInfo":"","adPairKey":[]},"status":"publish","visibility":"public","articleId":168987},{"headers":{"creationTime":"2017-03-26T15:31:59+00:00","modifiedTime":"2017-03-26T15:31:59+00:00","timestamp":"2023-09-14T18:05:35+00:00"},"data":{"breadcrumbs":[{"name":"Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33512"},"slug":"technology","categoryId":33512},{"name":"Information Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33572"},"slug":"information-technology","categoryId":33572},{"name":"Data Science","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33577"},"slug":"data-science","categoryId":33577},{"name":"Big Data","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33578"},"slug":"big-data","categoryId":33578}],"title":"Big Data Planning Stages","strippedTitle":"big data planning stages","slug":"big-data-planning-stages","canonicalUrl":"","搜素快速搜所平台改善":{"metaDescription":"Four stages are part of the planning process that applies to big data. As more businesses begin to use the cloud as a way to deploy new and innovative services ","noIndex":0,"noFollow":0},"content":"<p>Four stages are part of the planning process that applies to big data. As more businesses begin to use the cloud as a way to deploy new and innovative services to customers, the role of data analysis will explode. Therefore, consider another part of your planning process and add three more stages to your data cycle.</p>\n<ul class=\"level-one\">\n <li><p class=\"first-para\"><b>Stage 1: Planning with data: </b>The only way to make sure that business leaders are taking a balanced perspective on all the elements of the business is to have a clear understanding of how data sources are related. The business needs a road map for determining what data is needed to plan for new strategies and new directions.</p>\n </li>\n <li><p class=\"first-para\"><b>Stage 2: Doing the analysis:</b> Executing on big data analysis requires learning a set of new tools and new skills. Many organizations will need to hire some big data scientists who can understand how to take this massive amount of disparate data and begin to understand how all the data elements relate in the context of the business problem or opportunity.</p>\n </li>\n <li><p class=\"first-para\"><b>Stage 3: Checking the results: </b>Make sure you aren’t relying on data sources that will take you in the wrong direction. Many companies will use third-party data sources and may not take the time to vet the quality of the data, but you have to make sure that you are on a strong foundation.</p>\n </li>\n <li><p class=\"first-para\"><b>Stage 4: Acting on the plan:</b> Each time a business initiates a new strategy, it is critical to constantly create a big data business evaluation cycle. This approach of acting based on results of big data analytics and then testing the results of executing business strategy is the key to success. </p>\n </li>\n <li><p class=\"first-para\"><b>Stage 5: Monitoring in real time:</b> Big data analytics enables you to monitor data in near real time proactively. This can have a profound impact on your business. If you are a pharmaceutical company conducting a clinical trial, you may be able to adjust or cancel a trial to avoid a lawsuit. </p>\n </li>\n <li><p class=\"first-para\"><b>Stage 6: Adjusting the impact: </b>When your company has the tools to monitor continuously, it is possible to adjust processes and strategy based on data analytics. Being able to monitor quickly means that a process can be changed earlier and result in better overall quality.</p>\n </li>\n <li><p class=\"first-para\"><b>Stage 7: Enabling experimentation:</b> Combining experimentation with real-time monitoring and rapid adjustment can transform a business strategy. You have less risk with experimentation because you can change directions and outcomes more easily if you are armed with the right data.</p>\n </li>\n</ul>\n<p class=\"Remember\">The greatest challenge for the business is to be able to look into the future and anticipate what might change and why. Companies want to be able to make informed decisions in a faster and more efficient manner. The business wants to apply that knowledge to take action that can change business outcomes. Leaders also need to understand the nuances of the business impacts that are across product lines and their partner ecosystem. The best businesses take a holistic approach to data.</p>","description":"<p>Four stages are part of the planning process that applies to big data. As more businesses begin to use the cloud as a way to deploy new and innovative services to customers, the role of data analysis will explode. Therefore, consider another part of your planning process and add three more stages to your data cycle.</p>\n<ul class=\"level-one\">\n <li><p class=\"first-para\"><b>Stage 1: Planning with data: </b>The only way to make sure that business leaders are taking a balanced perspective on all the elements of the business is to have a clear understanding of how data sources are related. The business needs a road map for determining what data is needed to plan for new strategies and new directions.</p>\n </li>\n <li><p class=\"first-para\"><b>Stage 2: Doing the analysis:</b> Executing on big data analysis requires learning a set of new tools and new skills. Many organizations will need to hire some big data scientists who can understand how to take this massive amount of disparate data and begin to understand how all the data elements relate in the context of the business problem or opportunity.</p>\n </li>\n <li><p class=\"first-para\"><b>Stage 3: Checking the results: </b>Make sure you aren’t relying on data sources that will take you in the wrong direction. Many companies will use third-party data sources and may not take the time to vet the quality of the data, but you have to make sure that you are on a strong foundation.</p>\n </li>\n <li><p class=\"first-para\"><b>Stage 4: Acting on the plan:</b> Each time a business initiates a new strategy, it is critical to constantly create a big data business evaluation cycle. This approach of acting based on results of big data analytics and then testing the results of executing business strategy is the key to success. </p>\n </li>\n <li><p class=\"first-para\"><b>Stage 5: Monitoring in real time:</b> Big data analytics enables you to monitor data in near real time proactively. This can have a profound impact on your business. If you are a pharmaceutical company conducting a clinical trial, you may be able to adjust or cancel a trial to avoid a lawsuit. </p>\n </li>\n <li><p class=\"first-para\"><b>Stage 6: Adjusting the impact: </b>When your company has the tools to monitor continuously, it is possible to adjust processes and strategy based on data analytics. Being able to monitor quickly means that a process can be changed earlier and result in better overall quality.</p>\n </li>\n <li><p class=\"first-para\"><b>Stage 7: Enabling experimentation:</b> Combining experimentation with real-time monitoring and rapid adjustment can transform a business strategy. You have less risk with experimentation because you can change directions and outcomes more easily if you are armed with the right data.</p>\n </li>\n</ul>\n<p class=\"Remember\">The greatest challenge for the business is to be able to look into the future and anticipate what might change and why. Companies want to be able to make informed decisions in a faster and more efficient manner. The business wants to apply that knowledge to take action that can change business outcomes. Leaders also need to understand the nuances of the business impacts that are across product lines and their partner ecosystem. The best businesses take a holistic approach to data.</p>","blurb":"","authors":[{"authorId":9411,"name":"Judith S. 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Marcia Kaufman specializes in cloud infrastructure, information management, and analytics.</p>","authors":[{"authorId":9727,"name":"Alan Nugent","slug":"alan-nugent","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9727"}},{"authorId":9413,"name":"Fern Halper","slug":"fern-halper","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9413"}},{"authorId":34961,"name":"Judith S. Hurwitz","slug":"judith-s-hurwitz","description":" <p><b>Daniel Kirsch,</b> Managing Director of Hurwitz &#38; Associates, is a thought leader, researcher, author, and consultant in cloud, AI, and security. <b>Judith Hurwitz,</b> President of Hurwitz &#38; Associates, is a consultant, thought leader, and coauthor of 10 books including <i>Augmented Intelligence, Cognitive Computing and Big Data Analytics,</i> and <i>Hybrid Cloud for Dummies</i> ","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/34961"}},{"authorId":9412,"name":"Marcia Kaufman","slug":"marcia-kaufman","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9412"}}],"_links":{"self":"//dummies-api.coursofppt.com/v2/books/"}},"collections":[],"articleAds":{"footerAd":"<div class=\"du-ad-region row\" id=\"article_page_adhesion_ad\"><div class=\"du-ad-unit col-md-12\" data-slot-id=\"article_page_adhesion_ad\" data-refreshed=\"false\" \r\n data-target = \"[{&quot;key&quot;:&quot;cat&quot;,&quot;values&quot;:[&quot;technology&quot;,&quot;information-technology&quot;,&quot;data-science&quot;,&quot;big-data&quot;]},{&quot;key&quot;:&quot;isbn&quot;,&quot;values&quot;:[&quot;9781118504222&quot;]}]\" id=\"du-slot-632217ef96a23\"></div></div>","rightAd":"<div class=\"du-ad-region row\" id=\"article_page_right_ad\"><div class=\"du-ad-unit col-md-12\" data-slot-id=\"article_page_right_ad\" data-refreshed=\"false\" \r\n data-target = \"[{&quot;key&quot;:&quot;cat&quot;,&quot;values&quot;:[&quot;technology&quot;,&quot;information-technology&quot;,&quot;data-science&quot;,&quot;big-data&quot;]},{&quot;key&quot;:&quot;isbn&quot;,&quot;values&quot;:[&quot;9781118504222&quot;]}]\" id=\"du-slot-632217ef972c5\"></div></div>"},"articleType":{"articleType":"Articles","articleList":null,"content":null,"videoInfo":{"videoId":null,"name":null,"accountId":null,"playerId":null,"thumbnailUrl":null,"description":null,"uploadDate":null}},"sponsorship":{"sponsorshipPage":false,"backgroundImage":{"src":null,"width":0,"height":0},"brandingLine":"","brandingLink":"","brandingLogo":{"src":null,"width":0,"height":0},"sponsorAd":"","sponsorEbookTitle":"","sponsorEbookLink":"","sponsorEbookImage":{"src":null,"width":0,"height":0}},"primaryLearningPath":"Advance","lifeExpectancy":null,"lifeExpectancySetFrom":null,"dummiesForKids":"no","sponsoredContent":"no","adInfo":"","adPairKey":[]},"status":"publish","visibility":"public","articleId":168986},{"headers":{"creationTime":"2017-03-26T15:31:59+00:00","modifiedTime":"2017-03-26T15:31:59+00:00","timestamp":"2023-09-14T18:05:35+00:00"},"data":{"breadcrumbs":[{"name":"Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33512"},"slug":"technology","categoryId":33512},{"name":"Information Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33572"},"slug":"information-technology","categoryId":33572},{"name":"Data Science","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33577"},"slug":"data-science","categoryId":33577},{"name":"Big Data","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33578"},"slug":"big-data","categoryId":33578}],"title":"How to Analyze Big Data to Get Results","strippedTitle":"how to analyze big data to get results","slug":"how-to-analyze-big-data-to-get-results","canonicalUrl":"","搜素快速搜所平台改善":{"metaDescription":"Big data is most useful if you can do something with it, but how do you analyze it? Companies like Amazon and Google are masters at analyzing big data. And they","noIndex":0,"noFollow":0},"content":"<p>Big data is most useful if you can do something with it, but how do you analyze it? Companies like Amazon and Google are masters at analyzing big data. And they use the resulting knowledge to gain a competitive advantage.</p>\n<p>Just think about Amazon's recommendation engine. The company takes all your buying history together with what it knows about you, your buying patterns, and the buying patterns of people like you to come up with some pretty good suggestions. It's a marketing machine, and its big data analytics capabilities have made it extremely successful.</p>\n<p class=\"Remember\">The ability to analyze big data provides unique opportunities for your organization as well. You'll be able to expand the kind of analysis you can do. Instead of being limited to sampling large data sets, you can now use much more detailed and complete data to do your analysis. However, analyzing big data can also be challenging. Changing algorithms and technology, even for basic data analysis, often has to be addressed with big data.</p>\n<p>The first question that you need to ask yourself before you dive into big data analysis is what problem are you trying to solve? You may not even be sure of what you are looking for. You know you have lots of data that you think you can get valuable insight from. And certainly, patterns can emerge from that data before you understand why they are there.</p>\n<p>If you think about it though, you're sure to have an idea of what you're interested in. For instance, are you interested in predicting customer behavior to prevent churn? Do you want to analyze the driving patterns of your customers for insurance premium purposes? Are you interested in looking at your system log data to ultimately predict when problems might occur? The kind of high-level problem is going to drive the analytics you decide to use. </p>\n<p>Alternately, if you're not exactly sure of the business problem you're trying to solve, maybe you need to look at areas in your business that need improvement. Even an analytics-driven strategy — targeted at the right area — can provide useful results with big data. When it comes to analytics, you might consider a range of possible kinds, which are briefly outlined in the table.</p>\n<table>\n<tr>\n<th>Analysis Type</th>\n<th>Description</th>\n</tr>\n<tr>\n<td>Basic analytics for insight</td>\n<td>Slicing and dicing of data, reporting, simple visualizations,\nbasic monitoring.</td>\n</tr>\n<tr>\n<td>Advanced analytics for insight</td>\n<td>More complex analysis such as predictive modeling and other\npattern-matching techniques.</td>\n</tr>\n<tr>\n<td>Operationalized analytics</td>\n<td>Analytics become part of the business process.</td>\n</tr>\n<tr>\n<td>Monetized analytics</td>\n<td>Analytics are utilized to directly drive revenue.</td>\n</tr>\n</table>","description":"<p>Big data is most useful if you can do something with it, but how do you analyze it? Companies like Amazon and Google are masters at analyzing big data. And they use the resulting knowledge to gain a competitive advantage.</p>\n<p>Just think about Amazon's recommendation engine. The company takes all your buying history together with what it knows about you, your buying patterns, and the buying patterns of people like you to come up with some pretty good suggestions. It's a marketing machine, and its big data analytics capabilities have made it extremely successful.</p>\n<p class=\"Remember\">The ability to analyze big data provides unique opportunities for your organization as well. You'll be able to expand the kind of analysis you can do. Instead of being limited to sampling large data sets, you can now use much more detailed and complete data to do your analysis. However, analyzing big data can also be challenging. Changing algorithms and technology, even for basic data analysis, often has to be addressed with big data.</p>\n<p>The first question that you need to ask yourself before you dive into big data analysis is what problem are you trying to solve? You may not even be sure of what you are looking for. You know you have lots of data that you think you can get valuable insight from. And certainly, patterns can emerge from that data before you understand why they are there.</p>\n<p>If you think about it though, you're sure to have an idea of what you're interested in. For instance, are you interested in predicting customer behavior to prevent churn? Do you want to analyze the driving patterns of your customers for insurance premium purposes? Are you interested in looking at your system log data to ultimately predict when problems might occur? The kind of high-level problem is going to drive the analytics you decide to use. </p>\n<p>Alternately, if you're not exactly sure of the business problem you're trying to solve, maybe you need to look at areas in your business that need improvement. Even an analytics-driven strategy — targeted at the right area — can provide useful results with big data. When it comes to analytics, you might consider a range of possible kinds, which are briefly outlined in the table.</p>\n<table>\n<tr>\n<th>Analysis Type</th>\n<th>Description</th>\n</tr>\n<tr>\n<td>Basic analytics for insight</td>\n<td>Slicing and dicing of data, reporting, simple visualizations,\nbasic monitoring.</td>\n</tr>\n<tr>\n<td>Advanced analytics for insight</td>\n<td>More complex analysis such as predictive modeling and other\npattern-matching techniques.</td>\n</tr>\n<tr>\n<td>Operationalized analytics</td>\n<td>Analytics become part of the business process.</td>\n</tr>\n<tr>\n<td>Monetized analytics</td>\n<td>Analytics are utilized to directly drive revenue.</td>\n</tr>\n</table>","blurb":"","authors":[{"authorId":9411,"name":"Judith S. 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Hurwitz","slug":"judith-s-hurwitz","description":" <p><b>Daniel Kirsch,</b> Managing Director of Hurwitz &#38; Associates, is a thought leader, researcher, author, and consultant in cloud, AI, and security. <b>Judith Hurwitz,</b> President of Hurwitz &#38; Associates, is a consultant, thought leader, and coauthor of 10 books including <i>Augmented Intelligence, Cognitive Computing and Big Data Analytics,</i> and <i>Hybrid Cloud for Dummies</i> ","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/34961"}},{"authorId":9412,"name":"Marcia Kaufman","slug":"marcia-kaufman","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9412"}}],"_links":{"self":"//dummies-api.coursofppt.com/v2/books/"}},"collections":[],"articleAds":{"footerAd":"<div class=\"du-ad-region row\" id=\"article_page_adhesion_ad\"><div class=\"du-ad-unit col-md-12\" data-slot-id=\"article_page_adhesion_ad\" data-refreshed=\"false\" \r\n data-target = \"[{&quot;key&quot;:&quot;cat&quot;,&quot;values&quot;:[&quot;technology&quot;,&quot;information-technology&quot;,&quot;data-science&quot;,&quot;big-data&quot;]},{&quot;key&quot;:&quot;isbn&quot;,&quot;values&quot;:[&quot;9781118504222&quot;]}]\" id=\"du-slot-632217efa689a\"></div></div>","rightAd":"<div class=\"du-ad-region row\" id=\"article_page_right_ad\"><div class=\"du-ad-unit col-md-12\" data-slot-id=\"article_page_right_ad\" data-refreshed=\"false\" \r\n data-target = \"[{&quot;key&quot;:&quot;cat&quot;,&quot;values&quot;:[&quot;technology&quot;,&quot;information-technology&quot;,&quot;data-science&quot;,&quot;big-data&quot;]},{&quot;key&quot;:&quot;isbn&quot;,&quot;values&quot;:[&quot;9781118504222&quot;]}]\" id=\"du-slot-632217efa7111\"></div></div>"},"articleType":{"articleType":"Articles","articleList":null,"content":null,"videoInfo":{"videoId":null,"name":null,"accountId":null,"playerId":null,"thumbnailUrl":null,"description":null,"uploadDate":null}},"sponsorship":{"sponsorshipPage":false,"backgroundImage":{"src":null,"width":0,"height":0},"brandingLine":"","brandingLink":"","brandingLogo":{"src":null,"width":0,"height":0},"sponsorAd":"","sponsorEbookTitle":"","sponsorEbookLink":"","sponsorEbookImage":{"src":null,"width":0,"height":0}},"primaryLearningPath":"Advance","lifeExpectancy":null,"lifeExpectancySetFrom":null,"dummiesForKids":"no","sponsoredContent":"no","adInfo":"","adPairKey":[]},"status":"publish","visibility":"public","articleId":168985},{"headers":{"creationTime":"2017-03-26T15:31:58+00:00","modifiedTime":"2017-03-26T15:31:58+00:00","timestamp":"2023-09-14T18:05:35+00:00"},"data":{"breadcrumbs":[{"name":"Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33512"},"slug":"technology","categoryId":33512},{"name":"Information Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33572"},"slug":"information-technology","categoryId":33572},{"name":"Data Science","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33577"},"slug":"data-science","categoryId":33577},{"name":"Big Data","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33578"},"slug":"big-data","categoryId":33578}],"title":"Ten Hot Big Data Trends","strippedTitle":"ten hot big data trends","slug":"ten-hot-big-data-trends","canonicalUrl":"","搜素快速搜所平台改善":{"metaDescription":"As you enter the world of big data, you'll need to absorb many new types of database and data-management technologies. Here are the top-ten big data trends: Had","noIndex":0,"noFollow":0},"content":"<p>As you enter the world of big data, you'll need to absorb many new types of database and data-management technologies. Here are the top-ten big data trends:</p>\n<ul class=\"level-one\">\n <li><p class=\"first-para\"><b>Hadoop is becoming the underpinning for distributed big data management. </b>Hadoop is a distributed file system that can be used in conjunction with MapReduce to process and analyze massive amounts of data, enabling the big data trend. Hadoop will be tightly integrated into data warehousing technologies so that structured and unstructured data can be integrated more effectively.</p>\n </li>\n <li><p class=\"first-para\"><b>Big data makes it possible to leverage data from sensors to change business outcomes. </b>More and more businesses are using highly sophisticated sensors on the equipment that runs their operations. New innovations in big data technology are making it possible to analyze all this data to get advanced notification of problems that can be fixed to protect the business.</p>\n </li>\n <li><p class=\"first-para\"><b>Big data can help a business initiative become a real-time action to increase revenue. </b>Companies in markets such as retail are using real-time streaming data analytics to keep track of customer actions and offer incentives to increase revenue per customer.</p>\n </li>\n <li><p class=\"first-para\"><b>Big data can be integrated with historical data warehouses to transform planning.</b> Big data can provide a company with a better understanding of massive amounts of data about their business. This information about the current state of the business can be combined with historical data to get a full view of the context for business change.</p>\n </li>\n <li><p class=\"first-para\"><b>Big data can change the way diseases are managed by adding predictive analytics.</b> Increasingly, healthcare practitioners are looking to big data solutions to gain insights into disease by compare symptoms and test results to databases of results from hundreds of thousands of other cases. This allows practitioners to more quickly predict outcomes and save lives.</p>\n </li>\n <li><p class=\"first-para\"><b>Cloud computing will transform the way that data will be managed in the future.</b> Cloud computing is invaluable as a tool to support the expansion of big data. Increasingly, cloud services that are optimized for data will mean that many more services and delivery models will make big data more practical for companies of all sizes.</p>\n </li>\n <li><p class=\"first-para\"><b>Security and governance will be the difference between success and failure of businesses leveraging big data. </b>Big data can be a huge benefit, but it isn't risk-free. Companies will discover that if they are not careful, it is possible to expose private information through big data analysis. Companies need to balance the need to analyze results with best practices for security and governance.</p>\n </li>\n <li><p class=\"first-para\"><b>Veracity, or truthfulness, of big data will become the most important issue for the coming year.</b> Many companies can get carried away with the ability to analyze massive amounts of data and get back compelling results that predict business outcomes. Therefore, companies will find that the truthfulness of the data must become a top priority or decision making will suffer.</p>\n </li>\n <li><p class=\"first-para\"><b>As big data moves out of the experimental stage, more packaged offerings will be developed. </b>Most big data projects initiated over the past few years have been experimental. Companies are cautiously working with new tools and technology. Now big data is about to enter the mainstream. Lots of packaged big data offerings will flood the market.</p>\n </li>\n <li><p class=\"first-para\"><b>Use cases and new innovative ways to apply big data will explode.</b> Early successes with big data in different industries such as manufacturing, retail, and healthcare will lead to many more industries looking at ways to leverage massive amounts of data to transform their industries.</p>\n </li>\n</ul>","description":"<p>As you enter the world of big data, you'll need to absorb many new types of database and data-management technologies. Here are the top-ten big data trends:</p>\n<ul class=\"level-one\">\n <li><p class=\"first-para\"><b>Hadoop is becoming the underpinning for distributed big data management. </b>Hadoop is a distributed file system that can be used in conjunction with MapReduce to process and analyze massive amounts of data, enabling the big data trend. Hadoop will be tightly integrated into data warehousing technologies so that structured and unstructured data can be integrated more effectively.</p>\n </li>\n <li><p class=\"first-para\"><b>Big data makes it possible to leverage data from sensors to change business outcomes. </b>More and more businesses are using highly sophisticated sensors on the equipment that runs their operations. New innovations in big data technology are making it possible to analyze all this data to get advanced notification of problems that can be fixed to protect the business.</p>\n </li>\n <li><p class=\"first-para\"><b>Big data can help a business initiative become a real-time action to increase revenue. </b>Companies in markets such as retail are using real-time streaming data analytics to keep track of customer actions and offer incentives to increase revenue per customer.</p>\n </li>\n <li><p class=\"first-para\"><b>Big data can be integrated with historical data warehouses to transform planning.</b> Big data can provide a company with a better understanding of massive amounts of data about their business. This information about the current state of the business can be combined with historical data to get a full view of the context for business change.</p>\n </li>\n <li><p class=\"first-para\"><b>Big data can change the way diseases are managed by adding predictive analytics.</b> Increasingly, healthcare practitioners are looking to big data solutions to gain insights into disease by compare symptoms and test results to databases of results from hundreds of thousands of other cases. This allows practitioners to more quickly predict outcomes and save lives.</p>\n </li>\n <li><p class=\"first-para\"><b>Cloud computing will transform the way that data will be managed in the future.</b> Cloud computing is invaluable as a tool to support the expansion of big data. Increasingly, cloud services that are optimized for data will mean that many more services and delivery models will make big data more practical for companies of all sizes.</p>\n </li>\n <li><p class=\"first-para\"><b>Security and governance will be the difference between success and failure of businesses leveraging big data. </b>Big data can be a huge benefit, but it isn't risk-free. Companies will discover that if they are not careful, it is possible to expose private information through big data analysis. Companies need to balance the need to analyze results with best practices for security and governance.</p>\n </li>\n <li><p class=\"first-para\"><b>Veracity, or truthfulness, of big data will become the most important issue for the coming year.</b> Many companies can get carried away with the ability to analyze massive amounts of data and get back compelling results that predict business outcomes. Therefore, companies will find that the truthfulness of the data must become a top priority or decision making will suffer.</p>\n </li>\n <li><p class=\"first-para\"><b>As big data moves out of the experimental stage, more packaged offerings will be developed. </b>Most big data projects initiated over the past few years have been experimental. Companies are cautiously working with new tools and technology. Now big data is about to enter the mainstream. Lots of packaged big data offerings will flood the market.</p>\n </li>\n <li><p class=\"first-para\"><b>Use cases and new innovative ways to apply big data will explode.</b> Early successes with big data in different industries such as manufacturing, retail, and healthcare will lead to many more industries looking at ways to leverage massive amounts of data to transform their industries.</p>\n </li>\n</ul>","blurb":"","authors":[{"authorId":9411,"name":"Judith S. Hurwitz","slug":"judith-hurwitz","description":"Judith Hurwitz is president and CEO of Hurwitz & Associates, specializing in cloud computing, service management, information management, and business strategy.","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9411"}},{"authorId":9727,"name":"Alan Nugent","slug":"alan-nugent","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9727"}},{"authorId":9413,"name":"Fern Halper","slug":"fern-halper","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9413"}},{"authorId":9412,"name":"Marcia Kaufman","slug":"marcia-kaufman","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9412"}}],"primaryCategoryTaxonomy":{"categoryId":33578,"title":"Big Data","slug":"big-data","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33578"}},"secondaryCategoryTaxonomy":{"categoryId":0,"title":null,"slug":null,"_links":null},"tertiaryCategoryTaxonomy":{"categoryId":0,"title":null,"slug":null,"_links":null},"trendingArticles":null,"inThisArticle":[],"relatedArticles":{"fromBook":[{"articleId":207996,"title":"Big Data For Dummies Cheat Sheet","slug":"big-data-for-dummies-cheat-sheet","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/207996"}},{"articleId":168988,"title":"Integrate Big Data with the Traditional Data Warehouse","slug":"integrate-big-data-with-the-traditional-data-warehouse","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/168988"}},{"articleId":168987,"title":"Best Practices for Big Data Integration","slug":"best-practices-for-big-data-integration","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/168987"}},{"articleId":168985,"title":"How to Analyze Big Data to Get Results","slug":"how-to-analyze-big-data-to-get-results","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/168985"}},{"articleId":168986,"title":"Big Data Planning Stages","slug":"big-data-planning-stages","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/168986"}}],"fromCategory":[{"articleId":207996,"title":"Big Data For Dummies Cheat Sheet","slug":"big-data-for-dummies-cheat-sheet","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/207996"}},{"articleId":207478,"title":"Statistics for Big Data For Dummies Cheat Sheet","slug":"statistics-for-big-data-for-dummies-cheat-sheet","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/207478"}},{"articleId":207432,"title":"Big Data for Small Business For Dummies Cheat Sheet","slug":"big-data-for-small-business-for-dummies-cheat-sheet","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/207432"}},{"articleId":168988,"title":"Integrate Big Data with the Traditional Data Warehouse","slug":"integrate-big-data-with-the-traditional-data-warehouse","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/168988"}},{"articleId":168986,"title":"Big Data Planning Stages","slug":"big-data-planning-stages","categoryList":["technology","information-technology","data-science","big-data"],"_links":{"self":"//dummies-api.coursofppt.com/v2/articles/168986"}}]},"hasRelatedBookFromSearch":false,"relatedBook":{"bookId":281637,"slug":"big-data-for-dummies","isbn":"9781118504222","categoryList":["technology","information-technology","data-science","big-data"],"amazon":{"default":"//www.amazon.com/gp/product/1118504224/ref=as_li_tl?ie=UTF8&tag=wiley01-20","ca":"//www.amazon.ca/gp/product/1118504224/ref=as_li_tl?ie=UTF8&tag=wiley01-20","indigo_ca":"//www.tkqlhce.com/click-9208661-13710633?url=//www.chapters.indigo.ca/en-ca/books/product/1118504224-item.html&cjsku=978111945484","gb":"//www.amazon.co.uk/gp/product/1118504224/ref=as_li_tl?ie=UTF8&tag=wiley01-20","de":"//www.amazon.de/gp/product/1118504224/ref=as_li_tl?ie=UTF8&tag=wiley01-20"},"image":{"src":"//coursofppt.com/wp-content/uploads/big-data-for-dummies-cover-9781118504222-203x255.jpg","width":203,"height":255},"title":"Big Data For Dummies","testBankPinActivationLink":"","bookOutOfPrint":false,"authorsInfo":"<p><b data-author-id=\"34961\">Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy. Alan Nugent has extensive experience in cloud-based big data solutions. Dr. Fern Halper specializes in big data and analytics. Marcia Kaufman specializes in cloud infrastructure, information management, and analytics.</p>","authors":[{"authorId":9727,"name":"Alan Nugent","slug":"alan-nugent","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9727"}},{"authorId":9413,"name":"Fern Halper","slug":"fern-halper","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9413"}},{"authorId":34961,"name":"Judith S. Hurwitz","slug":"judith-s-hurwitz","description":" <p><b>Daniel Kirsch,</b> Managing Director of Hurwitz &#38; Associates, is a thought leader, researcher, author, and consultant in cloud, AI, and security. <b>Judith Hurwitz,</b> President of Hurwitz &#38; Associates, is a consultant, thought leader, and coauthor of 10 books including <i>Augmented Intelligence, Cognitive Computing and Big Data Analytics,</i> and <i>Hybrid Cloud for Dummies</i> ","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/34961"}},{"authorId":9412,"name":"Marcia Kaufman","slug":"marcia-kaufman","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9412"}}],"_links":{"self":"//dummies-api.coursofppt.com/v2/books/"}},"collections":[],"articleAds":{"footerAd":"<div class=\"du-ad-region row\" id=\"article_page_adhesion_ad\"><div class=\"du-ad-unit col-md-12\" data-slot-id=\"article_page_adhesion_ad\" data-refreshed=\"false\" \r\n data-target = \"[{&quot;key&quot;:&quot;cat&quot;,&quot;values&quot;:[&quot;technology&quot;,&quot;information-technology&quot;,&quot;data-science&quot;,&quot;big-data&quot;]},{&quot;key&quot;:&quot;isbn&quot;,&quot;values&quot;:[&quot;9781118504222&quot;]}]\" id=\"du-slot-632217ef8e625\"></div></div>","rightAd":"<div class=\"du-ad-region row\" id=\"article_page_right_ad\"><div class=\"du-ad-unit col-md-12\" data-slot-id=\"article_page_right_ad\" data-refreshed=\"false\" \r\n data-target = \"[{&quot;key&quot;:&quot;cat&quot;,&quot;values&quot;:[&quot;technology&quot;,&quot;information-technology&quot;,&quot;data-science&quot;,&quot;big-data&quot;]},{&quot;key&quot;:&quot;isbn&quot;,&quot;values&quot;:[&quot;9781118504222&quot;]}]\" id=\"du-slot-632217ef8eedb\"></div></div>"},"articleType":{"articleType":"Articles","articleList":null,"content":null,"videoInfo":{"videoId":null,"name":null,"accountId":null,"playerId":null,"thumbnailUrl":null,"description":null,"uploadDate":null}},"sponsorship":{"sponsorshipPage":false,"backgroundImage":{"src":null,"width":0,"height":0},"brandingLine":"","brandingLink":"","brandingLogo":{"src":null,"width":0,"height":0},"sponsorAd":"","sponsorEbookTitle":"","sponsorEbookLink":"","sponsorEbookImage":{"src":null,"width":0,"height":0}},"primaryLearningPath":"Advance","lifeExpectancy":null,"lifeExpectancySetFrom":null,"dummiesForKids":"no","sponsoredContent":"no","adInfo":"","adPairKey":[]},"status":"publish","visibility":"public","articleId":168984},{"headers":{"creationTime":"2017-03-26T15:31:57+00:00","modifiedTime":"2017-03-26T15:31:57+00:00","timestamp":"2023-09-14T18:05:35+00:00"},"data":{"breadcrumbs":[{"name":"Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33512"},"slug":"technology","categoryId":33512},{"name":"Information Technology","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33572"},"slug":"information-technology","categoryId":33572},{"name":"Data Science","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33577"},"slug":"data-science","categoryId":33577},{"name":"Big Data","_links":{"self":"//dummies-api.coursofppt.com/v2/categories/33578"},"slug":"big-data","categoryId":33578}],"title":"Explore the Big Data Stack","strippedTitle":"explore the big data stack","slug":"explore-the-big-data-stack","canonicalUrl":"","搜素快速搜所平台改善":{"metaDescription":"To understand big data, it helps to see how it stacks up — that is, to lay out the components of the architecture. A big data management architecture must inclu","noIndex":0,"noFollow":0},"content":"<p>To understand big data, it helps to see how it stacks up — that is, to lay out the components of the architecture. A big data management architecture must include a variety of services that enable companies to make use of myriad data sources in a fast and effective manner.</p>\n<img src=\"//coursofppt.com/wp-content/uploads/362970.image0.jpg\" width=\"400\" height=\"262\" alt=\"image0.jpg\"/>\n<p>Here's a closer look at what's in the image and the relationship between the components:</p>\n<ul class=\"level-one\">\n <li><p class=\"first-para\"><b>Interfaces and feeds: </b>On either side of the diagram are indications of interfaces and feeds into and out of both internally managed data and data feeds from external sources. To understand how big data works in the real world, start by understanding this necessity. </p>\n<p class=\"child-para Remember\">What makes big data big is that it relies on picking up lots of data from lots of sources. Therefore, open application programming interfaces (APIs) will be core to any big data architecture.</p>\n<p class=\"child-para\">In addition, keep in mind that interfaces exist at every level and between every layer of the stack. Without integration services, big data can't happen.</p>\n </li>\n <li><p class=\"first-para\"><b>Redundant physical infrastructure: </b>The supporting physical infrastructure is fundamental to the operation and scalability of a big data architecture. Without the availability of robust physical infrastructures, big data would probably not have emerged as such an important trend. </p>\n<p class=\"child-para\">To support an unanticipated or unpredictable volume of data, a physical infrastructure for big data has to be different than that for traditional data. The physical infrastructure is based on a distributed computing model. This means that data may be physically stored in many different locations and can be linked together through networks, the use of a distributed file system, and various big data analytic tools and applications.</p>\n </li>\n <li><p class=\"first-para\"><b>Security infrastructure:</b> The more important big data analysis becomes to companies, the more important it will be to secure that data. For example, if you are a healthcare company, you will probably want to use big data applications to determine changes in demographics or shifts in patient needs.</p>\n<p class=\"child-para\">This data about your constituents needs to be protected both to meet compliance requirements and to protect the patients' privacy. You will need to take into account who is allowed to see the data and under what circumstances they are allowed to do so. You will need to be able to verify the identity of users as well as protect the identity of patients.</p>\n </li>\n <li><p class=\"first-para\"><b>Operational data sources:</b> When you think about big data, understand that you have to incorporate all the data sources that will give you a complete picture of your business and see how the data impacts the way you operate your business.</p>\n<p class=\"child-para\">Traditionally, an operational data source consisted of highly structured data managed by the line of business in a relational database. But as the world changes, it is important to understand that operational data now has to encompass a broader set of data sources.</p>\n </li>\n</ul>","description":"<p>To understand big data, it helps to see how it stacks up — that is, to lay out the components of the architecture. A big data management architecture must include a variety of services that enable companies to make use of myriad data sources in a fast and effective manner.</p>\n<img src=\"//coursofppt.com/wp-content/uploads/362970.image0.jpg\" width=\"400\" height=\"262\" alt=\"image0.jpg\"/>\n<p>Here's a closer look at what's in the image and the relationship between the components:</p>\n<ul class=\"level-one\">\n <li><p class=\"first-para\"><b>Interfaces and feeds: </b>On either side of the diagram are indications of interfaces and feeds into and out of both internally managed data and data feeds from external sources. To understand how big data works in the real world, start by understanding this necessity. </p>\n<p class=\"child-para Remember\">What makes big data big is that it relies on picking up lots of data from lots of sources. Therefore, open application programming interfaces (APIs) will be core to any big data architecture.</p>\n<p class=\"child-para\">In addition, keep in mind that interfaces exist at every level and between every layer of the stack. Without integration services, big data can't happen.</p>\n </li>\n <li><p class=\"first-para\"><b>Redundant physical infrastructure: </b>The supporting physical infrastructure is fundamental to the operation and scalability of a big data architecture. Without the availability of robust physical infrastructures, big data would probably not have emerged as such an important trend. </p>\n<p class=\"child-para\">To support an unanticipated or unpredictable volume of data, a physical infrastructure for big data has to be different than that for traditional data. The physical infrastructure is based on a distributed computing model. This means that data may be physically stored in many different locations and can be linked together through networks, the use of a distributed file system, and various big data analytic tools and applications.</p>\n </li>\n <li><p class=\"first-para\"><b>Security infrastructure:</b> The more important big data analysis becomes to companies, the more important it will be to secure that data. For example, if you are a healthcare company, you will probably want to use big data applications to determine changes in demographics or shifts in patient needs.</p>\n<p class=\"child-para\">This data about your constituents needs to be protected both to meet compliance requirements and to protect the patients' privacy. You will need to take into account who is allowed to see the data and under what circumstances they are allowed to do so. You will need to be able to verify the identity of users as well as protect the identity of patients.</p>\n </li>\n <li><p class=\"first-para\"><b>Operational data sources:</b> When you think about big data, understand that you have to incorporate all the data sources that will give you a complete picture of your business and see how the data impacts the way you operate your business.</p>\n<p class=\"child-para\">Traditionally, an operational data source consisted of highly structured data managed by the line of business in a relational database. But as the world changes, it is important to understand that operational data now has to encompass a broader set of data sources.</p>\n </li>\n</ul>","blurb":"","authors":[{"authorId":9411,"name":"Judith S. Hurwitz","slug":"judith-hurwitz","description":"Judith Hurwitz is president and CEO of Hurwitz & Associates, specializing in cloud computing, service management, information management, and business strategy.","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9411"}},{"authorId":9727,"name":"Alan Nugent","slug":"alan-nugent","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business strategy.</p> <p><b>Alan Nugent</b> has extensive experience in cloud-based big data solutions.</p> <p><b>Dr. Fern Halper</b> specializes in big data and analytics.</p> <p><b>Marcia Kaufman</b> specializes in cloud infrastructure, information management, and analytics.</p>","hasArticle":false,"_links":{"self":"//dummies-api.coursofppt.com/v2/authors/9727"}},{"authorId":9413,"name":"Fern Halper","slug":"fern-halper","description":" <p><b>Judith Hurwitz</b> is an expert in cloud computing, information management, and business 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