Over the course of 2016, my publish research diversified quite a bit from previous years. When I first started working at Gartner, I focused on information infrastructure topics: DBMS, Hadoop related topics and big data. This year I dug into a range of topics related to data management, including blockchain, web-scale IT and bimodal. I also led this year’s Magic Quadrant for Operational DBMS.
With several Business Intelligence and Enterprise Information Management Summits (now called the Data & Analytics Summits) in the first quarter of 2016, I got a late start with published research. This piece, published in May, was a collaboration with Mario Faria on the what Chief Data Officers should focus on when embarking on a data lake project. This was the first of two data lake research projects this year. The other addressed data lake design best practices.
Bimodal is becoming increasingly understood as an essential capability for competing in digital business. This research note discussed how Gartner’s seven Enterprise Information Management building blocks should be recast for Mode 2 projects while also supporting traditional Mode 2 efforts. For a relatively short note, this piece has been well received and proven popular with the data and analytics leader audience.
Easily my most read piece in 2016, this research note starts the conversation about how blockchain will impact data management and how to start experimenting. Most blockchain pilots target processes found in financial services, but blockchain may have a massive impact on how data is managed and trusted across organizations. Data managers need to be ready for this potential shift.
I interviewed a number of web-scale digital natives to see how their data management practices differed from traditional approaches. The two biggest differences were in how vendor relationships were managed and exploited, and the depth of expertise in deployed technology. Interestingly, this was my least read note in 2016 but I think it will attract increasing attention going into 2017 as more organizations start down the web-scale path.
Speaking of attention, the final big data adoption survey always gets a lot of it. This year saw the first drop in anticipated investment and a shift towards more traditional challenges as the big data fad is absorbed into standard practices and infrastructure. Luckily, I’m collaborating with Jim Hare next year on the evolution of this survey. 2017 will see it shift to broader data & analytics topics.
After collaborating on three previous Magic Quadrants, it was my first opportunity to lead one. The Magic Quadrant for OpDBMS reflects a maturing market with broadly available capabilities and a focus on operational excellence. We’re already gearing up for the 2017 edition.
2016 saw a 21% increase (YoY) in end user inquiries on data lakes and I take most of them. This note distills the best practices from those interactions and primary research. Next year I’ll continue this line of research by looking at infrastructure choices aligned to user populations and requirements.
Predicts 2019: Data and Analytics Strategy
Data and analytics are the key accelerants of digitalization, transformation and “ContinuousNext” efforts. As a result, data and analytics leaders will be counted upon to affect corporate strategy and value, change management, business ethics, and execution performance.Read Free Gartner Research
Category: data-and-analytics-strategies dbms
Tags: hadoop popularity research