ODBMS.org Q&A with Shawn Rogers, Chief Research Officer, Dell Statistica: 2016 Analytics Predictions
“A ‘citizen data scientist’ is an everyday, non-technical user that lacks the statistical and analytical prowess of a traditional data scientist, but is equally eager to leverage data in order to uncovering insights, and importantly, do so at the speed business”.– Shawn Rogers.
Q1. What are the most significant changes to analytics expected in 2016?
Shawn Rogers: We see four primary trends driving change in the advanced analytics industry in 2016.
The first is the movement of analytics to the edge. The explosive growth of connected devices and IoT infrastructures has created a landscape where more organizations will look to push analytics out to their data and run analytics directly at the source. A second major catalyst driving change is the growth in vertical-specific analytics use cases, as companies across a number of key industry verticals will increasingly look to analytics to deliver business process optimization and validation. In addition, we’re expecting the continued expansion of the analytics user base beyond the traditional data scientist and into the realm of the everyday user, or the “citizen” data scientist. And lastly, we expect that analytics will gain mainstream attention as the engine that drives innovation. Companies simply have to innovate in order to stay competitive in today’s business landscape, and they’ll increasingly turn to advanced analytics to deliver that innovation.
Q2. How is the Internet of Things (IoT) changing the analytics landscape?
Shawn Rogers: IoT is helping change the paradigm through which organizations view analytics. As is widely understood, with the rise of the Internet of Things, we’re experiencing a velocity and volume of data that we’ve never seen before. What’s perhaps less understood is that IoT is helping data achieve a level of gravity that it’s never achieved in the past. We’re getting better, smarter, and more sophisticated about having the right data in the right place for the right purpose. More so than the sheer volume and velocity of data IoT creates, it’s the gravity it creates that is changing the analytics landscape, and it’s doing so by forcing us to push analytics to the edge. Instead of asking how can we bring all of this data into the analytic environment, companies are instead starting to ask how can we push our analytics out to where the data lives. We expect this emphasis on pushing predictive algorithms and scoring functionality directly to the source of data to quickly become the norm in terms of how companies deliver analytics.
Q3. Performing analytics at the source versus within a central data warehouse – what are the pros and cons?
Shawn Rogers: As previously noted, we’re doing a better job ensuring that the right data is in the right place for the right reason. Therefore, in certain – but not all – instances, it no longer makes nearly as much sense as it once did to bring the data back to a centralized source. After all, it’s already in the right place for the right reason. So, performing analytics at the source, or “at the edge,” has become more appealing, especially as organizations work in more hybrid environments. Running analytics at the edge saves the time, compute resources and costs usually associated with relocating data, and it allows users to utilize the analytics output quicker. That’s not to say doing so is easy or is always appropriate. Running analytics at the source of data requires a level of flexibility and openness in the overall data environment that not all organizations have in place.
Q4. How will analytics affect vertical markets?
Shawn Rogers: Analytics plays a huge role in vertical markets. Take healthcare, for example, where doctors are using data to prescribe treatment and medicine based on specific circumstances and not just aggregate data.
Companies in the pharmaceutical space are likewise leveraging analytics to design new and more highly focused drugs for niche diseases.
But that’s really only part of the story. Advanced analytics are traditionally seen as a means to uncovering new and unique products or processes. While that’s certainly one of its key applications, advanced analytics can play an equally big role helping companies streamline, optimize, and validate existing products and processes. This is especially true when applied to vertical market use cases such as regulated manufacturing. As managing production becomes more and more expensive, and as regulatory requirements increasingly call for validated processes, analytics becomes a valuable tool in helping streamline and validate the manufacturing process. Analytics allows manufacturers to streamlines processes from the predictive phase all the way to the validation phase, thus enabling them to be proactive. With the innovation taking place around analytics, and availability of tools that work at the speed of businesses, analytics are now the gateway for companies in every way they do business.
Q5. Who is a ‘Citizen Data Scientist’ and what is his or her role?
Shawn Rogers: A ‘citizen data scientist’ is an everyday, non-technical user that lacks the statistical and analytical prowess of a traditional data scientist, but is equally eager to leverage data in order to uncovering insights, and importantly, do so at the speed business. The citizen data scientist relies less on things like advanced statistics and predictive algorithms and more on visualization, visual discovery, and dashboarding – the types of functionality that makes complex data more easily consumable. And with today’s new tools and platforms, they’re increasing getting that ability to easily consume and understand analytics with more visually appealing depictions of data. Enabling the citizen data scientist is critical to any organization that wants to expand or integrate analytics across various business workstreams, and not rely solely on the traditional data scientists.
Q6. Is really possible to have non-technical analytics users?
Shawn Rogers: Absolutely. Now, that’s not to say there won’t be a learning curve or that this is something that’s going to happen overnight, but continued technology advancements are already making analytics more consumable than ever before, and will continue to do so in the coming years. Need is a great motivator, and the fact is that in order to remain competitive in today’s business landscape, even those companies with the resources to hire data scientists need to cultivate a broader range of users across various functional areas that can drive innovation by deriving insights from data.
And when end users have a critical need, vendors usually move quickly to meet it. To that end, I think you’ll see continued emphasis on the development and delivery of technologies built with the needs of the citizen data scientist in mind.
Whether you’re talking about quick-start analytics templates or reusable workflows or capabilities of that nature, the emphasis will be on making it easier for non-technical users to utilize analytics platforms.
Shawn Rogers, Chief Research Officer, Dell Statistica, Dell Software
Mr. Rogers is an internationally recognized thought leader, speaker, author and instructor on the topics of big data, cloud, data integration, analytics, data warehousing and social analytics. Prior to joining Dell, he was Vice President Research for Business Intelligence and Analytics at Enterprise Management Associates a leading analyst firm. He co-founded the BeyeNETWORK, a global online publication covering business intelligence, data warehousing, and analytics. He was also a partner at DMReview magazine (now Information Management) and has held various executive level positions with technology companies.