Why partner with a leader in hybrid data warehousing?

In 2016 and beyond, the data warehouse will continue to be relevant, but the new requirement will be for hybrid data warehouse solutions. As organizations strive to be more data-driven, new types of analytics are coming to the forefront. These new workloads are best served from a range of data warehousing deployments—cloud, private cloud, optimized appliances and on-premises solutions—plus a range of stores—Hadoop, structured relational stores, No SQL stores and more. And then there are specialized needs such as the operational analytics model that relies on continuous data feeds to deliver real-time insights. When you combine these solutions, the result is often hybrid data warehouse model in which different deployments are in use across the cloud and on-premises.  

IBM has again been named a leader in the Gartner Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics. In this year’s report, IBM is named as the most advanced leader on the “completeness of vision” axis. You can read the report here. 

In this 2016 report, Gartner affirms that leaders must “prepare hybrid technology platforms that expand the data warehouse beyond any current practice. This is especially important because the influence of the LDW¹ has created a situation in which multiple repository strategies are now expected, even from a single vendor. Additionally, interest is growing in cloud solutions as alternatives to on-premises solutions, although we expect hybrid cloud and-on-premises situations to become the norm.”² Gartner states that this will set new expectations for the Logical Data Warehouse, which will require new ways of managing access and processing needs across a hybrid environment.

Why do I believe IBM offers completeness of vision? IBM has a choice of solutions and deployments that bring the right workload together with the right deployment platform for the analytic need. This ranges from traditional core analytics on a data warehouse appliance on one end of the continuum to analytics on the cloud for data that is “born on the cloud” on the other end—and many combined or hybrid solutions in between. 

The IBM portfolio lets the client also choose among fit-for-purpose stores for each of their data and analytics workload needs. There are solutions to cover different types of data stores (Hadoop, NoSQL and structured relational).  All of these solutions are available today, and the dashDB deployment for private cloud is currently in early access preview. And with IBM Fluid Query, our solutions can provide deeper insights using more data types from across different repositories. So, I believe we are the single vendor to offer a comprehensive solution in terms of the deployment options and data stores you need, as well the ability through Fluid Query to stitch together deep insights that often deliver a business edge.

This Magic Quadrant lays out four data warehousing use cases. These are: traditional data warehouse, operational data warehouse, Logical Data Warehouse, and context-independent data warehouse that is often used by data scientists and others for “discovery” insights and advanced analytics. IBM is a single vendor who can help with all four of these data warehousing use cases alone or in combination, and I believe this has also contributed to our leading rank for completeness of vision on the current and previous Magic Quadrants. 

In addition, these solutions help avoid the need for “rip and replace” scenarios as your needs change through the elasticity to scale up or out; built-in Spark support which can help extend analytics capabilities from the Spark ecosystem; IBM support through BI solutions such as Watson Analytics, Cognos and SPSS; plus IBM DataWorks for data refinement; and the Bluemix development environment, just to name a few. 

Last year I talked about how cloud use cases were driving us towards hybrid data warehousing. This year, technology and use cases continue to extend. Hybrid data warehousing is not only about the “fit for purpose” store for the right data type, but also the “fit for purpose” deployment option to optimize price performance, time to value, and access to information for the business.

It is an exciting time to be in data warehousing as the market is completely transforming and evolving, driven by the expanded business needs and a proliferation of data types. I hope you will read this Gartner Magic Quadrant report to understand IBM's strengths in these rapidly changing times. 

Learn how to modernize your data warehouse architecture.

¹ LDW is an acronym for Logical Data Warehouse

²  Gartner Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics, February 25, 2016

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