Insight

Hot topics in 2016: big data and analytics trends

Big data – a key enabler of digital transformation – is becoming a bigger deal every day. In 2016, big data will affect everyone doing business, from the most detail-oriented chief information officer to the retail clerk who rings up purchases and has no idea of the extraordinary analytic power it takes to drive those daily sales. During 2016, we’ll be profiling 10 engaging, interesting, and potentially influential industry trends ranging from highly detailed technological shifts to broad trends potentially disruptive across all businesses. Everything we’ll write about interests us—and we think it will interest you, too.

This article previews our top trends, and nothing is off-limits because if it’s going to make an impact on how big data affects business, it’s on our radar screen. Here’s what we’re looking at.

  1. Spark replaces MapReduce processing technology. Many technical ramifications result from changing a processing framework for new workloads to Hadoop environments, but even business end users of data analytics should know something about this change because it will have an impact on what you can do with your data, how you do it, and the people you’ll have to hire to make it happen.
     
  2. The impact on open data. Open data gives remarkable insights into sociodemographics and public spending, among many examples. Open data is now moving into the private sector and is likely to have a dramatic impact on a range of industries that will see value from turning some of their data into open data.
     
  3. Retail banking adopts open data. As standardized application program interfaces and data-enabled product comparisons become mainstream, data and analytics are transforming retail banking. We’ll look at how that transformation is progressing, which is partly through intervention by regulators and partly because data-scrutinizing customers are quickly realizing not all banks are the same.
     
  4. The Semantic Web arrives. Internet pioneer and elder statesman Sir Tim Berners-Lee first predicted 15 years ago that the World Wide Web he helped create would eventually become the Semantic Web, wherein machines would better understand information through linked data. We believe 2016 will mark a turning point in the use of semantic, graph technology and that the benefits of the Semantic Web are just beginning to be realized.
     
  5. A revised look at data management. In addition to industrywide shifts, we want to pay attention to certain basic principles of data and analytics; we want to go back to the basics of how to realize the value of data; and we want to reconsider some of the principles of data management. The changes aren’t revolutionary, but they’re important, and not getting them right will mean nothing else will work.
     
  6. Retail stores get a data face-lift. Brick-and-mortar outlets continue adapting the best practices of e-commerce’s agile pricing. If pricing optimization works for online retailers, it should for traditional sales channels too, and we’ll explain how this might work in 2016.
     
  7. New tools to visualize and interrogate data. We’ll take stock of offerings that have greater sophistication and broader functionality. We’ll examine how businesses can apply new tools to gain competitive advantage. And we’ll show how those tools can provide added depth of insight, added ease of use, and added speed for everyone.
     
  8. The growth of in-memory analytics. The relocation of data is becoming more and more mainstream, partly because of declining random access memory (RAM) costs that make it more practical for data to reside in a computer’s RAM rather than on physical disks. We’ll look at how mainstream users are benefiting from this important shift.
     
  9. Practical benefits of in-memory analytics. We’ll explore how businesses can save time and money by using in-memory analytics instead of replacing core systems.
     
  10. Executing successful big data projects. Data scientists tend to focus 89% of their time on data, 10% on technology, and 1% on everything else—including the people who make it work. That distribution emphasizes the wrong set of priorities, and we plan to give our views on the skills and structures of teams that get those priorities right.

 

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