Here’s a recap of my published research in 2017. I’ve been fortunate to cover a diverse range of topics, including blockchain, cognitive enhancement drugs, streaming and DBMS.
As more Hadoop deployments move to the cloud and a monolithic platform is less relevant, vendors are reshaping offerings and targeting specific use cases. Data and analytics leaders need to understand this competitive shift and how it impacts purchasing and deployment decisions.
The Hadoop space continues to evolve quickly. As the market guide details, Hadoop’s broad value proposition hasn’t been realized widely and is quickly being replaced by more specialized go-to-market stories. In the 2017 Hype Cycle for Data Management, we stated that Hadoop Distributions were obsolete before reaching the plateau of productivity. Because of that obsolescence, we’ll do one more update to the market guide in 2018.
Blockchain’s distributed trust model promises to remake existing business processes. Data and analytics leaders must recast existing data management and analytics capabilities and add new competencies to manage risk and exploit new opportunities.
You can’t avoid the blockchain hype. The problem is the blockchain space has little in the way of demonstrable impact to business outcomes. I’ll be writing more on blockchain and data and analytics in 2018, but I need the market participants to deliver on their aspirations in order to have something to write about.
Emerging competitive pressures and market opportunities are forcing enterprises to accelerate decision making using new technologies. Data and analytics leaders must balance the acquisition of technology with their organization’s maturity in business alignment, process integration and governance.
This was a year-long project with Roy Schulte to advise clients on how to think about different streaming/real-time use cases. Each maturity level has different requirements around governance, business process integration, technology, and several other competencies.
The required time to value for analytics insights continues to shrink, driven by fluid market conditions, fickle consumers and intensifying digital competition. Data and analytics leaders must understand the event stream processing market to win in this challenging business environment.
It was time to update this market guide in 2017, and it will be updated again in 2018. Event streams are a hot topic for data and analytics leaders looking to accelerate data consumption and use. Application leaders are using similar technologies to build event-driven architectures. How data is managed in these new application architectures is one research topic I’m pursuing in 2018.
Delivering on new, innovative uses of data and analytics requires new approaches to managing data and analytics programs. Data and analytics leaders must master how to react to these different approaches, how they vary as conditions change, and the associated impact on program management.
This is a revisited note from 2016. Organizations are still trying to balance innovation with the need to keep the lights on. Data management is a foundational discipline to enable bimodal. As clients realize that, the volume of calls asking about bimodal is increasing.
Competitive and social pressures are pushing knowledge workers, including IT professionals, to experiment with cognitive enhancement drugs. CIOs in the vanguard will be the first impacted and must take an early leadership position. (Maverick research exposes unconventional thinking and advice.)
This research, my first Maverick piece, looks at why people take cognitive enhancement drugs (CEDs), the effectiveness of CEDs and recommendations for how organizations can respond. I’ve also written about CEDs in IT here.
Years of investment in data and analytics have paid off for many organizations, but traditional approaches and methods still dominate. Data and analytics leaders should use this report to identify and advance their programs, relative to their peers.
This survey is the update to the big data adoption survey we produced in previous years. With the notion of big data fading as a topic, this was an opportunity to make a fresh start with a new series of questions. Jim Hare and I will be updating this survey in 2018. The update will give us essential trending information year-over-year.
The OPDBMS market in 2017 brings cloud and fully managed options center stage for execution. Market-defining vision includes features for machine learning, serverless scenarios and streaming integration. Data and analytics leaders must balance current and future needs against this market landscape.
The 2017 update to the OpDBMS MQ reflects the development of new DBMS capabilities and the continued momentum to cloud. This research will also be updated in 2018.
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