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Technology / Data

2017 predictions: Big data and its coming of age

It’s that time of year again, the streets are full of festive cheer, amusing jumpers are being worn, and belts are being loosened as mince pies, mulled wine and cheese threaten to force you into a larger size of clothing.

That’s right it’s Christmas and that means the New Year is just around the corner.

As traditional as a post Christmas dinner lunch snooze, predictions for what to look out for next year are here to give you something to look forward to, or live in fear of.

Big data may have had a quiet year but it’s definitely not going away, so here’s what to look out for in 2017.

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Aaron Auld, CEO Exasol

Self-service BI will go mainstream: Self-service tools are gaining ground in the enterprise and startup alike. As data analytics integrates itself further into the core of the business, there will be a shift towards the business diving into data analytics with databases, visualization tools such as Tableau and data-prep tools such as Alteryx. Business users have begun to use the tools that take the time and complexity out of data analytics; this is very important as enterprise deals with different data types and formats

Machine learning and AI becomes embedded within the database to drive predictive analytics: This will enable more advanced algorithms and better actionable insights. It will also see solution providers develop even more bespoke services that respond to the needs of certain issues, for example, predictive analytics for replenishment in retail.



John Schroeder, CEO and founder, MapR
Big Data for Governance or Competitive Advantage

“In 2017, the governance vs. data value tug of war will be front and center.

“Enterprises have a wealth of information about their customers and partners. Leaders are transforming their companies from industry sector leaders to data driven companies.

“Organisations are now facing an escalating tug of war between governance required for compliance, and the use of data to provide business value and implement security to avoid damaging data leaks and breeches. Financial services and heath care are the most obvious industries with customers counting in the millions with heavy governance requirements.

“Leading organisations will manage their data between regulated and non-regulated use cases. Regulated use cases data require governance; data quality and lineage so a regulatory body can report and track data through all transformations to originating source. This is mandatory and necessary but limiting for non-regulatory use cases like customer 360 or offer serving where higher cardinality, real-time and a mix of structured and unstructured yields more effective results.”


Data Agility Separates Winners and Losers

“Software development has become agile where dev ops provides continuous delivery. In 2017, processing and analytic models evolve to provide a similar level of agility as organisations realise data agility, the ability to understand data in context and take business action, is the source of competitive advantage not simply have a large data lake.

“The emergence of agile processing models will enable the same instance of data to support batch analytics, interactive analytics, global messaging, database and file-based models. More agile analytic models are also enabled when a single instance of data can support a broader set of tools. The end result is an agile development and application platform that supports the broadest range of processing and analytic models.”


Tim Seears, VP Emerging Technology at Think Big, a Teradata Company

“Data analysis has become an increasingly vital component of business and I fully expect its growth trajectory to continue through 2017. It will be the year of predictive analysis, which will help businesses to unlock new insights and predictions, with robotic and automated adoption increasingly replacing traditional manual processes.

“This will not only be a huge cost saver for business, but it will also expedite them through the boring’ admin stage so that they can move on to applying deep learning properly and getting maximum value from it. I expect to see more companies beginning to realise value from their big data investments and applying that knowledge in new ways, while data analytics will become increasingly prevalent in industries such as agriculture and healthcare.”

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