An Update on Big Data Trends in 2017

2016 was a very eventful year for the ever-growing world of big data, with more and more organizations using storage and processing methods that have developed in the field to extract more value from their business through data of all forms and sizes. In 2017, there is an expectation that many systems that support both structured and unstructured data will continue to emerge. The market is currently demanding platforms that help data scientists and custodians secure large volumes of data, while also empowering end users to easily analyze hidden trends behind the data.

Ironically, perhaps the largest indicator that big data had truly taken its place in emerging technologies was the fact that people finally stopped talking about it, per Douglas Laney, VP and Distinguished Analyst, Gartner. Big data now ‘just is,’ and the focus has turned not towards integrating it into systems, but managing, measuring and monetizing ‘information assets.’ According to data blogger Yves Mulkers, this shows that analytics solutions in the big data world are maturing and becoming more available, and are not just being reserved for front runners.

We look at several possible emerging fields in Big Data, listed below:

  • Movement to the Cloud: Small and medium sized enterprises, as well as larger companies are exploring strategies that allow them to take more and more of their products and applications into the cloud and out of conventional data centers. This is not being limited to analytics, but also to transaction processing; companies are looking to save on data storage at centers and instead invest money in talent to run in-house Hadoop clusters and processing services. Cloud service providers are looking to cater to this need by providing not just big data processing platforms and storage, but also the expertise needed to interpret the data.
  • Aggregation of digital unstructured and machine IoT data: Though IoT and its generated data has not been implemented or used by companies as much as was perceived in 2016, companies are thinking about integrating IoT – big data aggregation goals are set to expand to methods where standard digital data originally manually entered, and data issues by machines will be aggregated into visualizations that transform the way data is collected. Tech Republic cites the example of “drone-hosted data that will combine an assortment of sensory and standard IT inputs into a single pane of glass view for an operator of how a drone is functioning.”
  • The use of more dark data: Companies will finally begin to use massive amounts of information contained in paper-based documents, photos, videos and other data assets lying dormant in attempts to aggregate massive amounts of historical data. Such data can help companies gather a sense of their past performances and mistakes and help plan for future cycles, and provide opportunities to see where trademarks and intellectual property laws may have been violated in the past.
  • Stronger administration of data security permissions: The goal of big data is to produce on ‘true’ version of what’s going on that is understandable and usable by everyone, but not necessarily accessible to everyone. By revising their data access policies and permissions, companies are likely to monitor data exfiltration so that each data user has correct access permissions in place.
  • Immediately gratifying analytics: As hard as it may be, managers and executives are interested in big data not just so that they can plan, but create agile processes that depend on acting very quickly on real-time data. The pressure is likely to be on IT departments at companies to produce quick reports, one-liner summaries for C-suite employees and focus on real-time data to help companies implement policies quicker and adapt to failures quicker than ever before.
  • Data agility separates winners and losers: Software development has changed dramatically to a process that is continuously updated, and operations departments provide continuous delivery of changes. Processing and analytic models are likely to generate a similar level of agility ­in their data (understand data in context and make quick business decisions) to gain as much of a competitive advantage as possible; batch analytics, global messaging, database models are likely to drive agile development processes that will support analytics models in broad ranges of data.
  • Blockchain transforms select financial service applications: There are likely to be select applications of the technology underlying bitcoin, that show broad implications of the way data is stored and used. In a nutshell, blockchain provides a globally distributed ledger that changes the way data is stored and transactions are processed. Chains can be viewed by anyone, and transactions are stored in blocks timestamped when storing data that cannot be altered. In a theoretically un-hackable system, we see implications on efficiency, where customers may not have to for example worry about SWIFT codes and data center leaks in money transfers, while the technology itself provides a cost-saving opportunity and competitive advantage for data-centered businesses.
  • Machine learning maximizes microservices impact: There is likely to be an increase in machine learning applications and in the need for microservices when it relates to data, this year. These had previously been limited to ‘fast data’ integrations but we are likely to see a fundamental shift to applications that leverage big data, and the incorporation of machine learning approaches that use large of amounts of historical data to better understand the context of newly arriving streaming data. (CIO Trends).