With a full travel schedule and competing demands on my time, I don’t get much time for professional reflection. As 2018 closes out, I always find it helpful to take a step back and revisit the research I produced for my day job over the previous twelve months.
As the operational DBMS market shifts to the cloud, practical concerns around vendor lock-in, staffing challenges and evolving pricing models are forcing data and analytics leaders to re-evaluate operational DBMS investment as part of their larger data management strategies.
The “State Of” note is always a fun and challenging document to produce. It gets created as part of the process around the Magic Quadrant and Critical Capabilities for Operational DBMS research projects we complete annually. The “State Of” essentially distills down where we think the operational DBMS market is and where it’s likely headed in the foreseeable future.
Business and IT leaders are overestimating the effectiveness and usefulness of data lakes in their data and analytics strategies. Data and analytics leaders can avoid data lake failures by comparing their skills, expectations and infrastructure capabilities with the scenarios in this report.
I’m flooded with calls about data lakes. (Pun intended.) Enterprises continue to struggle with the concept, use cases and implementation choices. I wrote this note based on hundreds of conversations with clients about their data lakes and what went right and wrong. It’s been well received but this is one document I wish were freely available as I think it would help more companies.
Increasingly demanding consumers and intensifying digital competition are pushing analytics from transactional to continuous. To achieve the necessary continuous intelligence, data and analytics leaders must understand and master the event stream processing market.
Now an annual document. The data-in-motion space continues to diversify across products, deployment models and user audiences. The problem is that, frankly, every product and vendor pitch sounds the same. Parsing out the space is increasingly challenging for companies looking to invest in areas as diverse as streaming data integration and continuous intelligence.
Hype for blockchain-based data management is creating myths around how the technology will replace traditional data management technologies. Data and analytics leaders must understand these myths, and the realities behind them, to manage business expectations and temper vendor enthusiasm.
“Is blockchain a database?” Kinda, but it’s an awful database. I’m happy to see some of the blockchain enthusiasm decrease at the end of 2018 but I suspect this is just the wave momentarily receding. This document gets in front of the blockchain hype from a data manager’s perspective.
Digital business requires new and flexible data management practices that adapt to uncertain and changing conditions. Data and analytics leaders must create and support an agile data infrastructure to meet the needs of digital business initiatives.
This document took about a year to produce and it’s easily one of my favorites. I interviewed several digital natives about their data management practices, from infrastructure to support, and distilled it into this research note. When companies want to revamp their data management capabilities to be more resilient and agile, this is still the place to start.
As organizations expand how they create and use data, data and analytics leaders must transform how they use, control and manage increasingly distributed data assets. Modern data management strategies must become expansive, flexible and outcome-focused.
2018 was my first year coordinating the research agenda for my group. This document, revisited annually, outlines the research the group intends to produce over the coming year.
And that’s it. I didn’t produce as much writing as I would’ve liked in 2018. I’m planning to fill that gap in 2019, both blogging here and for my day job as an industry analyst. Right now, that looks like more blockchain and data in motion coverage, as well as DataOps (whatever that is).