![\"big](\"https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2015\/01\/big-data-and-analytic-trends-2015.jpg?resize=608%2C456&ssl=1\")
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Happy New Year! Here’s a list of what I find are the most interesting<\/em> trends in\u00a0analytics in 2015.<\/p>\n Why the italics? because most of what will happen this year\u00a0can be summarized with a single\u00a0word: more<\/strong>.\u00a0Yes, there will be more data, more mobile analytics, more cloud analytics, more data discovery, more visualization, etc.—but these are the trends\u00a0that I personally will be paying closer attention to over the year:<\/p>\n Arthur C. Clark famously said that “Any sufficiently advanced technology is indistinguishable from magic.” The analytics industry has recently seen big advances in technology, but it hasn’t yet turned into magic—<\/em>tools and interfaces that “just work.”<\/p>\n Today, people\u00a0are required\u00a0to shepherd every step of the analytics process, determining what data is available, how it should be joined,\u00a0how it should stored, and how it should be analyzed and visualized.<\/p>\n But the new power of advanced analytics and machine learning is now being applied to the process of analytics itself—so that\u00a0more of the\u00a0process can\u00a0be\u00a0automated.<\/p>\n We should be able to point our tools at the data, and\u00a0let the algorithms figure out it how it should be joined and cleansed, propose complementary data, and optimize how it should be stored (e.g. between cost-effective “cold” storage and operations-optimized “hot” storage).\u00a0We should\u00a0be able to\u00a0let our tools identify outliers, find statistically-valid correlations, and propose the right types of visualization.<\/p>\n Today,\u00a0companies like SAP offer\u00a0Smart Data Access<\/a> to connect data seamlessly between Hadoop\/Spark and new in-memory analytics systems. And the\u00a0SAP Lumira<\/a>\u00a0data discovery tool uses\u00a0advanced statistics to\u00a0automatically generate\u00a0Related Visualizations<\/a> based on the data sets being viewed. 2015 will see more advanced automation based on these capabilities.<\/p>\n Datafication is what happens\u00a0when\u00a0technology reveals previously invisible processes—which\u00a0can then be tracked and\u00a0optimized. This isn’t a new trend<\/a>, but it’s gathering speed as real-time operational analytics systems<\/a> become available and the price of gathering data\u00a0continues to plummet.<\/p>\n Connected devices are the highlight of this year’s CES conference<\/a>. Beyond the dozens of fitness tracking devices already available, there are now\u00a0chips that stop you slouching<\/a>, and sensor-enabled soccer balls<\/a>,\u00a0basketballs<\/a> and\u00a0tennis rackets<\/a>\u00a0to help you improve your game. Sensor tags can even help you find your keys<\/a>.<\/p>\n The key insight is that even simple data can lead to big insights. For example, sensor-equipped carpets promise to\u00a0help seniors stay independent longer<\/a>—not because the sensors themselves are complex, but because powerful pattern-detection algorithms\u00a0can learn a resident’s normal gait and sound an alert if it starts to deteriorate. And who would have thought that fitness devices could locate the epicenter of an earthquake<\/a>?!<\/p>\n And of course all this applies to commercial\u00a0uses. Shoppers can be tracked with beacons<\/a>, Inventory can be tracked via drones<\/a>.\u00a0You can spot process bottlenecks<\/a>,\u00a0optimize beer sales<\/a>,\u00a0and track real-time purchases<\/a>.<\/p>\n Here’s an instant business opportunity for 2015: find a process\u00a0that is\u00a0poorly tracked. Install\u00a0simple sensors along the process and feed\u00a0the collected real-time data to the cloud. Then use sophisticated analytics to\u00a0feed actionable insights back to business people using mobile interfaces. For bonus points, add complementary third-party data sets, offer industry benchmarking, and encourage\u00a0community best-practice sharing.<\/p>\n The layer-cake best-practice model of analytics (operational systems and external data feeding data marts and a data warehouse, with BI tools as the cherry on the top) is rapidly becoming obsolete.<\/p>\n It’s being replaced by a new, multi-polar model where data is collected and analyzed in multiple places, according to the type of data and analysis required:<\/p>\n There are clear overlaps with each of these systems, and they will converge over time,\u00a0but each is a powerful hub that is not going to be replaced by the others any time soon.<\/p>\n In 2015 we will see the\u00a0development of more best-practice guidance for how to get the most out of this\u00a0pragmatic—but complex—collection of analysis hubs. This will involve both\u00a0regular data feeds between\u00a0poles and federated analysis\u00a0to provide a connected view across the enterprise (including, hopefully, some more “magic”—see point 1).<\/p>\n Questions that enterprise architects will have to answer for different uses include:<\/p>\n Analytic infrastructures have been too brittle. With\u00a0the right setup, they have\u00a0provided powerful, flexible analytics—but implementing systems takes\u00a0too long and it has been a challenge to keep up with the changing needs of the organization.<\/p>\n The latest analytics technologies allow for fluid analytics that adapt more gracefully to\u00a0changing needs, with better support for one-off analysis and the analytics lifecycle:<\/p>\n Analytics is no longer under the control of\u00a0well-meaning central IT\u00a0dictatorships<\/a>. As decisions about IT spending increasingly move to business units, analytics projects have to have\u00a0the consent of the governed<\/a>—and this means big changes to every aspect of analytics organization.<\/p>\n 2015 will see the further development of the\u00a0community governance\u00a0of analytics<\/a>. Analytics leads will have to develop the skills they need to build and\u00a0nurture internal social networks that will\u00a0set priorities\u00a0and put\u00a0pressure on\u00a0maverick BI projects. To do this, they have to behave more\u00a0like\u00a0politicians, paying closer attention to the needs of their electorate and cajoling\u00a0everybody to play their part for the good of the community as a whole.<\/p>\n In a logical extension of datafication within an organization, 2015 will see more\u00a0analytics across\u00a0business networks, helping optimize\u00a0processes between\u00a0the participants of\u00a0an ecosystem. Some examples include:<\/p>\n Data privacy laws and processes are now lag far\u00a0behind the power of available technology. Serious abuses have already come to light<\/a>\u00a0and\u00a0there are probably many others that haven’t yet been revealed.<\/p>\n 2015 will see some welcome advances\u00a0in the default use of encryption<\/a>, but more sweeping changes\u00a0are required to control how\u00a0people combine and access\u00a0personal data sets. Ultimately this is a problem than can only be fixed by society, laws, and cultural changes—and\u00a0unfortunately, those changes will probably only come about after much pain and suffering.<\/p>\n An equivalent analogy might be the use of\u00a0asbestos<\/a> in construction. Because it\u00a0had many useful\u00a0qualities, including affordability, sound absorption, and\u00a0resistance to fire, it was widely used around the world, despite concerns over toxicity<\/a>. \u00a0The asbestos industry and governments played down the dangers until the deadly consequences could no longer be denied. Government intervention came only after many people had suffered—and the US still\u00a0lags behind other developed countries<\/a> that have banned its use. The new controls mean that changes to existing buildings can be very expensive.<\/p>\n As you’re building your big data solutions,\u00a0make sure you do it with proper data controls in place, and don’t abuse people’s expectations of how their data will be used, whether or not you have a legal right to do so today. Making the right choices today will help you avoid social risk\u00a0and\/or expensive changes in the future.<\/p>\n In their recent book, the Second\u00a0Machine Age<\/a>, authors\u00a0Erik Brynjolfsson and Andrew McAfee argue that we are now in the “second half of the chessboard<\/a>” when it comes to computer technology. The exponential trend means the increases in data processing power this year will be the equivalent of decades of progress in the past.<\/p>\n Nowhere is this clearer than in the area of analytics, where the biggest problem is increasingly that\u00a0organizations just don’t know\u00a0which of the\u00a0myriad\u00a0business opportunities to implement first.<\/p>\n 2015 will be a wonderful year for analytics, just like it has been for the last\u00a0quarter-century—as long as we remember that great power brings great responsibility, and that we must also strive to adapt our information culture and processes.<\/p>\n","protected":false},"excerpt":{"rendered":" Here’s a summary of what I believe are the seven most interesting trends in big data and analytics for 2015<\/p>\n","protected":false},"author":2,"featured_media":12804,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[14],"tags":[56,100,160,173,204,344,496,835,1085],"class_list":["post-12448","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-thoughts","tag-56","tag-analytics","tag-bi","tag-big-data","tag-business-intelligence","tag-data-science","tag-forecast","tag-predictive","tag-trends"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2015\/01\/big-data-and-analytic-trends-2015-2.jpg?fit=608%2C456&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3X9RF-3eM","_links":{"self":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/12448","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/comments?post=12448"}],"version-history":[{"count":0,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/12448\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/media\/12804"}],"wp:attachment":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/media?parent=12448"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/categories?post=12448"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/tags?post=12448"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}More Magic<\/h3>\n
Datafication<\/h3>\n
Multipolar\u00a0Analytics<\/h3>\n
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Fluid Analysis<\/h3>\n
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Community<\/h3>\n
Analytic Ecosystems<\/h3>\n
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Data Privacy<\/h3>\n
Conclusion<\/h3>\n