Why partner with a business leader in data warehousing
Enterprise data warehouses remain as relevant as ever in today’s business environment.
Whether it be Hadoop data stores, operational data stores, NoSQL-based systems of engagement or others, various types of databases now complement and extend the data warehouse (DW) to provide data services for analytics. Consequently, many users have expanded their collection of DW data assets and platforms to take advantage of the “best tool for the job,” and in more cases it involves a Logical Data Warehouse (LDW) that mixes and matches various platforms for distinct roles.
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. In the latest Gartner Magic Quadrant (MQ) for Data Warehouse (DW) and Data Management Solutions (DBMS) for Analytics, the independent industry analyst firm stresses these fundamentals. In addition, the report shows that Gartner continues to recognize IBM as a leader in this market.
I believe that IBM’s position in the latest DW MQ reflects our continued focus on delivering solutions that fit a wide variety of LDW requirements. Clear evidence for this is IBM’s positioning in Gartner’s findings ahead of Oracle in terms of completeness of vision.
I also believe the latest MQ is significant in its emphasis on an ongoing shift in the DW arena that IBM has also highlighted. Data warehouses are no longer focused only on analyzing internal data sources. Authored by Mark Beyer and Roxane Edjlali, the report points out, “In 2015, organizations require solutions capable of managing and processing external data in combination with their traditional internal sources.” In addition, it states that “the data warehouse has expanded to address multiple data types, processing engines and repositories.”
As with all IT initiatives, the right approach to your DW needs should address your specific business requirements and the outcomes you’re trying to achieve. The Gartner study highlights four key DW use cases: traditional, operational, logical and context-independent. The principal factors that should shape your decision to adopt one or another are the size of your organization, skills available, analytics maturity, industry and the nature of the competition.
Regardless of your specific DW requirements, it’s as important as ever to partner with a vendor that has the proven breadth and depth of solutions to fit each of these needs. Gartner cites IBM’s “broad offering and integration across products that can support all four major data warehouse use cases” as a strength, along with our continued investment in product innovation driven by customer and market demands.
IBM helps clients embrace all data for all types of analytics. To deliver on this means providing a complete set of capabilities in addition to the aforementioned DW use cases. IBM’s leadership spans a wide range of logical-DW market segments, including data integration, data masking, data quality, master data management, operational DBMS, enterprise content management and predictive and business analytics.
To address these requirements, IBM continues to add capabilities that help power the LDW. For example, IBM Fluid Query, which we launched recently, is a powerful query and data movement tookit which helps users access and leverage value from data across a variety of different data stores. For more information on this latest release, catch this informative webcast.
Please download and read the Gartner study to learn more about other trends in the market and additional strengths of IBM’s solutions.
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.