2017 Trends in Data Analytics: No Stopping the Momentum

2017 Trends in Data Analytics: No Stopping the Momentum

2017 Trends in Data Analytics: No Stopping the Momentum

The enterprise lives in a world of data, and the desire – the need – to analyze that data for business success is paramount. As businesses march into 2017, what will be at the top of their Data Analytics and Business Intelligence agenda so that they can realize their goals? How does that reflect upon their work in this area in the last year?

We explored these issues with Ajay Anand, VP of Products at Kyvos Insights and co-founder of Datameer; Ihab Ilyas, co-founder of Tamr and Professor of Computer Science at the University of Waterloo; Thomas C. Redman, the Data Doc and President of Data Quality Solutions; Nova Spivack, CEO and co-founder of enterprise intelligence company Bottlenose; Michael Stonebraker, Ph.D., co-founder and CTO of Tamr, and recipient of the 2014 A.M. Turing Award; and, Anand Venugopal, Head of Product, StreamAnalytix, Impetus Technologies.

Some interviewees decided to discuss some issues and events of the recent past that may influence how business’ Data Analytics efforts move forward in 2017.

A number of trends that have been developing for decades got even closer to each other in 2016: Count among these the growth, availability, and affordability of massively parallel computing and massive storage capabilities with a corresponding massive growth of data, says Spivack.  His company has been working to help the process of turning data into useful and actionable insights. That massive pile of data particularly includes new kinds of data – like video and tweets and IoT data – and 80% of this is not structured.

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Spivack discussed efforts such as heavy equipment maker Caterpillar’s deal with Uptake in 2015 to use that startup’s platform, which helps industries leverage the data they collect from sensors and gauges on their assets, to analyze and predict when customers’ machines need repairs, and replacement parts. Such cutting-edge Data Analytics projects will increasingly become the norm, he notes.

Interesting insights are hidden in unstructured data:  “Within a text field, for example, there may be 100 different, important things to know,” Spivack says. Of course, it’s hard to talk about unstructured data in all its permutations without talking about Apache Hadoop, which Anand points out reached the 10-year mark in 2016. The industry continues to see a maturing of its ecosystem and its readiness for enterprise deployment, he says. “We saw more focus on making the data in the data lake available and accessible to business users, not just Data Scientists,” he says.

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Redman points to the fact that companies have started to become more focused on the ties that bind Data Quality to Data Analytics, as well, noting the growing interest in the concept of the Data Provocateur, (see 2017 Trends in Data Strategy for more information).  “I don’t know how a lot of executives have been running their companies with the data they have,” he says. Data Quality isn’t an end in and of itself, but a need for enabling good Analytics. A front-and-center cause of the 2008 financial crises, he reminds us, was bad mortgage-related data that affected analysis of loan risks.

“The point [of Data Quality] is to make an organization work better, to make it so [the business] has data it can trust going forward,” Redman says.

The expectation going into 2017, as Anand sees it, is for the industry to continue working to ensure that business users will not have to go through a steep learning curve to access Big Data for their Analytics requirements. Kyvos, for instance, offers technology that makes it possible for users to use their existing desktop BI tools, he says, and get the high performance, interactive access that they are used to. (See our recent article about Kyvos’scalable, Self-Service Analytics technology.

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