IBM named a Leader in the Latest Gartner Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics

By Dennis Duckworth

Note: This blog represents my opinion about what is said in the Gartner Magic Quadrant and does not mean to imply that it is what Gartner intended. You can read the report here to see what Gartner said.

In the most recently released Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics (Published 12 February 2015; Analysts: Mark A. Beyer, Roxane Edjali), Gartner placed IBM in the Leaders Quadrant.

If you are a follower of that particular Magic Quadrant, you will know that IBM has been listed as a leader every year back to the first one in 2007 (I’ll leave it as an exercise for the reader to Google search for all the graph images from all those previous years).)

There were a number of interesting things I took away from reading the latest Magic Quadrant. The first thing I noticed even before opening it was the change in the title. In previous years, this report had been called the “Magic Quadrant for Data Warehouse Database Management Systems”. For me this slight adjustment in naming reflects two recent market trends: first, that analytics is the real business driver for these solutions and second, through the addition of that little word “and” in the name makes it clearer that not all data management solutions for analytics are data warehouses, or at least traditional data warehouses. In 2014, Gartner for the first time included non-relational data management systems (HDFS, key-value stores, document stores, etc.) and that inclusion continued in the new report. This reflects another truth in the data warehouse world – the relational data warehouse isn’t sufficient for the analytics needed in enterprises today.

This reflects another truth in the data warehouse world – the relational data warehouse isn’t sufficient for the analytics needed in enterprises today.

Gartner continues to highlight the Logical Data Warehouse as a key use case for data warehouses and data management solutions. This use case is consistent with what we see our clients doing and wanting to do – they want to have one overall data management architecture in which to be able to land, cleanse, manage, transform, explore, govern, and analyze all their data. That means getting their enterprise data warehouse to play nice with their tactical data marts, operational data stores, Hadoop data reservoirs and real-time streaming systems—all with centralized, uniform data governance and security. In her blog on this topic, Wendy Lucas addresses a reaction we get  all the time, “Does this sound complex? It doesn’t need to be excessively complicated if you deploy the solutions that have been optimized for your specific types of data, data-processing latencies and analytic needs.”

If you have been reading the previous blogs (see a list below), you have seen that IBM is a believer in the power of the Logical Data Warehouse approach for analytics. We have talked about our zone architecture (using separate best-of-breed platforms for the best performance on different types of data or for different analytic requirements). We announced new functionality like IBM Fluid Query for PureData System for Analytics that allows very tight integration between PureData relational data warehouse stores and Hadoop stores. This allows you to run queries on PureData System for Analytics that have the ability to reach over and run in Hadoop with those results being automatically incorporated into the overall query results. That means you are able to send queries and return results rather than needing to ship raw data around, which is much more efficient.

We continue to add more products and more capabilities to existing products to allow our clients to provide more of what is needed as part of the Logical Data Warehouse, including being able to offer Hybrid Cloud implementations. We added dashDB as a cloud-based alternative for high performance advanced analytics (with built-in integration to our Cloudant Database-as-a-Service). We continue to improve our BigInsights for Hadoop and our IBM Streams offerings, both of which have had recent v4.0 releases. We also continue to improve our Integration and Information Governance solutions to be able to manage all of the data that is part of the Logical or Hybrid architecture.

We continue to add more products and more capabilities to existing products to allow our clients to provide more of what is needed as part of the Logical Data Warehouse, including being able to offer Hybrid Cloud implementations.

We do this all to be able to provide our customers with what they need to stay ahead of their ever changing and growing data and analytics needs. Gartner’s Magic Quadrant provides a good validation for us that we are succeeding in that effort.

Read additional blogs

About Dennis,

Dennis Duckworth, Program Director of Product Marketing, IBM AnalyticsDennis has been in the data game for quite a while, doing everything from Lisp programming in artificial intelligence to managing a sales territory for an RDBMS company. His passion is helping companies and people get real value out of cool technology. He is currently contributing to IBM efforts to create a unified comprehensive analytics framework across its entire Big Data platform family. In his previous role, Dennis was Director of Competitive and Market Intelligence for Netezza. He holds a degree in Electrical Engineering from Stanford University but has spent most of his life on the East Coast. When not working, Dennis enjoys sailing and fishing off his backyard on Buzzards Bay and he remains vigilant in his quest for wine enlightenment.

About The Magic Quadrant

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

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