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As the Data-to-Decisions (D2D) analyst at Constellation Research, I cover a broad domain, from data platforms, orchestration, and integration up into the business domain, where business intelligence (BI), advanced analytics, embedded services, and real-time decisioning help to put insight into action. Twice each year (Q1 and Q3) I make note of the things that impress me through Constellation ShortLists, which are freely available to the public. Constellation recently published the latest updates of the ShortList portfolio, so I thought I'd connect the dots among the nine lists that I publish with this post on D2D trends.
'Big' Isn't the Point
The big data bubble has burst. Don't get me wrong: The data landscape has changed for good (and for the better), and most organizations are now making use of next-generation data platforms like Hadoop and NoSQL databases. But just having access to unprecedented volumes and varieties of data doesn't matter much if you can't figure out what's inside these new platforms and whether that data might drive new insights and better outcomes.
Data cataloging tools explore and make accessible data and metadata across high-scale data lakes and other sources. The best options use machine learning to automatically discover data and detect usage patterns. Collaboration capabilities enable technical and business users to rate and review data assets, annotate what's known about them, and enable authorized users to reject or modify tags and classifications.
The Data Cataloging ShortList still lists just two best-or-breed vendors (click on the link to discover their names). It's not that it's a stagnant market. In fact, new cataloging products are coming out of the woodwork, including new products from industry giants. The ShortList is not a checklist, however, so before adding recommendations to the next ShortList in Q1 2018, I'll be talking to customers and looking for evidence of many successful deployments and signs of significant demand.
On another big data topic, the concept of data lakes is morphing quickly, with cloud-based Hadoop services and cloud object stores rapidly gaining popularity as lower-complexity and lower-cost options for building data lakes. Reality is also setting in that most organizations will have more than one data lake. These trends have introduced new management and governance challenges. Nonetheless, the Data Lake Management ShortList remains unchanged since Q1. Here, too, new options aren't emerging, but I'm still looking for compelling evidence of adoption and success before adding new names to the list.
Cloud, Yes, Lock-in, No
We're seeing strong gravitation toward cloud computing, and the appeal is understandable. Agility, limitless scalability, and low and steadily declining costs are among the attractions. Getting locked into one cloud vendor? Not an attraction!
The new Hybrid- and Cloud-Friendly database ShortLists (one for relational databases and one for NoSQL stores) cite products that are available both as software (for on-premises deployments) and as services on multiple popular public clouds. Sure, you can run software on any cloud on infrastructure as a service, but doing so takes away much if not most of the agility, cost, and administrative advantages of just tapping into services.
Cloud services offer agility, cost, and ease-of-deployment-and-admin advantages, but it's best if such services are available on multiple clouds. Better still if those services are run by the cloud providers or the software developer (and not a third party). That way you minimize finger pointing when something goes wrong. Most providers of popular database products are making services available across multiple public clouds. For now it's about having options, but some vendors are hoping to support seamless service portability across clouds using container technology.
Self Service Expands, Meets Cloud
Demand for self service, which really means liberation from IT dependency, is gathering steam. Democratization has been the dominant theme in business intelligence for nearly a decade. The new Self-Service Data Preparation ShortList recommends three vendors, while an update to the previously published Self-Service Advanced Analytics ShortList adds a fifth recommended vendor.
Over the last three years I've seen rapidly growing demand for supporting BI and analytics in the cloud. Cloud-based BI services have been around for more than a decade, but interest has spiked as data is increasingly stored, managed and generated in the cloud. I added one maturing vendor offering to the Cloud-Based BI and Analytics Platforms ShortList while dropping another vendor that has shifted its strategy to providing insight services (rather than a general-purpose, cloud-based BI and analytics platform). The changes are explained in the ShortList report.
Two other selections that remain unchanged from Q1 appear in the latest Cloud Performance Management ShortList and Integration Platform-as-a-Service ShortList. These are two more cases where I'm actively considering new competitors. In addition to taking briefings with vendors who think they deserve to be on a ShortList, I talk to plenty of customers and want to see evidence of successful deployments and growing customer demand.
If you have any questions about these ShortLists, feel free to contact me here. I'm happy to hear more about your needs and to discuss recommendations that might best suit your specific challenges, use cases, and environment.
View the complete Constellation ShortList portfolio here.
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