4 Takeaways From the 2019 Gartner Data and Analytics Summit

Analytics8’s CTO shares insight into common themes and his takeaways from Gartner’s annual summit.

The annual Gartner Data and Analytics Summit has always been a great opportunity to check in with our customer base and learn more about what’s exciting them, their pain points, and their goals; this year was certainly no exception. As the only services organization to sponsor on the showroom floor, our conversations revolved less around specific software and more around how companies can get a handle on the bigger picture – data strategy, modern data architectures, cloud deployments, how to find value in data, and blurring the lines between operational and analytical systems.

No matter how mature your data and analytics process, people, and systems, ideas and conversations from the Gartner summit can help gauge if your initiatives are in line with industry trends and can serve as inspiration as you move towards your company goals. Here are four major themes we took away from the conference:

  1. Data Science, Machine Learning, and Artificial Intelligence Are Here to Stay

Not surprisingly, the subjects of data science, machine learning, and artificial intelligence were ubiquitous, but in a much different tone than years prior.  Rather than advocating the need for advanced analytics, defining the roles of data scientists and citizen data scientists, and providing fundamental education on the topics as in the past, this year the topics were talked about “matter of factly” in normal conversation as a part of an overall analytics strategy.  Data science is beyond trends, and while the mostly old technology reinvents and re-veneers itself to meet the needs of a newer, broader, and mainstream user base, it is clear that the tech in some form is here to stay.  If companies aren’t employing these technologies in some capacity, they are late.  The time is now for companies to (re)audit their analytics strategy, of which data science should be a component.

  1. Modern Data Architecture and the Role of Data Lakes

Another topic that was talked frequently talked about this year was data lakes.  Data lakes were red hot a few years ago, both in Gartner circles and in the industry.  Data lakes subsequently fell out of fashion for a variety of reasons, mostly due to the simple fact that they became non-performant and unmanageable after becoming an enterprise dumping ground of data with no vision.  The topic of data lakes found itself back in the middle of many conversations this year as a part of a broader data management strategy, not as the silver bullet or “data warehouse replacement” as many overzealous CIOs and CDOs asserted while misinterpreting the data lake movement in the past.  Data lake conversations, still a bit radioactive at times, were usually preceded with disclaimers and stories of lessons learned.  We saw and talked to several technology vendors that have found a way to better leverage and/or manage data lakes, so we concur with the esprit de corps that data lakes will be employed for the foreseeable future and have a better shot at success this time around.

  1. Technology Hype

We didn’t notice any major software releases announced in the exhibit hall as in years past, but the energy and excitement from attendees on the floor was at its typical fever pitch.  This is where attendees could see and talk to (sometimes for the first time) the software vendors who comprise the magic quadrants.  This is also where we had the opportunity to talk to many attendees at our booth and perform our own observations on industry topics.  A very common opening question from our booth guests was, “What does the tech from ‘Vendor X’ at the booth over there really do and why do I care?”

  1. Cloud Deployments Are on the Rise

We heard lots of affirmation during various presentations and from people who visited our booth that more and more companies are moving their data to (or keeping their data in!) the cloud… and seemingly everybody else has plans to do so.  The expectations of reduced management costs and the ability to be nimble seemed to be the primary motivators.  This news isn’t new or especially surprising, but anecdotal observations from our booth suggests that the allure of platform cloud data warehouses like Snowflake (versus building a datacenter mostly from scratch in AWS or Azure) is increasing.  Data sharing was a common topic discussed at our booth, which seems to align with the increased attention to platform cloud data warehouses.

Patrick oversees R&D and is responsible for the technical direction of Analytics8. When he's not working, he's probably playing with his 2 sons. If the kids are with the babysitter, he's sharing a bottle of wine with his wife while binging on Netflix - probably a documentary or historical drama.


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