IBM’s Vision for Analytical Platforms: Technology Convergence in the Logical Data Warehouse

By Rob Routzahn

Sometimes, things become more complicated. Your data architecture is no exception.

We all know that there has been an explosion of data available to organizations for business and operational analysis. We also know that there are scores of vendors, each proclaiming their solution is the best for a given data type or workload. Where does an organization get started? How do they make sense of the noise? Who can they trust to help them?

In Gartner’s 2016 Magic Quadrant for Data Warehouse and Data Management Solution for Analytics, I believe that IBM has emerged as the partner with the industry’s leading vision for how to move forward. You can read this report here.

What is the IBM vision for data warehousing now and into the future? It all starts with the Logical Data Warehouse (LDW).
The LDW encompasses the scope of technologies needed in today’s hybrid data world. These technologies have made it possible to push data closer to the user for enhanced analytical effectiveness while providing powerful tools to optimize operational efficiency. New Open Source platforms like Hadoop enable management of virtually any type of data. More powerful relational databases process terabytes of data in minutes.

Data integration technologies enable governed self-service access to data.

Data archiving software helps manage the data load on the entire system. Governance technologies help ensure that the data is clean, secure, and reliable. Finally, organizations have the opportunity to choose cloud-based deployment options for both their data and their applications, providing an unprecedented means to reign in capital costs.

Beyond the breadth of IBM’s vision is the understanding from our clients that this complex mix of data types and technology platforms needs to be easy to manage.

That is why we are converging our technologies to provide common data schemas, a common user experience, and easy to deploy interoperability standards regardless of platform, or where an organization chooses to begin its journey toward implementing a Logical Data Warehouse. IBM also provides the data virtualization tools and fluid data movement capabilities necessary to make finding, analyzing, and using data as seamless and easy as possible.

I would encourage you to read this new Gartner Magic Quadrant.  As you read through this report, I believe you will see that the comprehensive IBM solution to enable the Logical Data Warehouse stands out from the pack.

About Rob,

Routzahn headshotRobert Routzahn is a Portfolio Marketing Manager for the data warehouse product line. His primary focus is driving the messaging and strategy for the data warehouse product line. Prior to his current role, Robert was a Portfolio Marketing Manager for Information Integration products. He has also served as the Product Manager for InfoSphere QualityStage, and other IBM software products. Rob has worked in the Information and Data Management space for over 10 years. Robert holds an MBA from The University of Chicago Graduate School of Business, and an undergraduate degree from Bowling Green State University.


IBM’s Continued Leadership in the Data Warehousing Market

By James Kobielus

It’s good to know that some things remain constant in the ever-changing data warehousing (DW) market. One of those constants is IBM’s continued leadership in this segment, which, as I stated in my recent IBM Big Data & Analytics (BD&A) Hub blog, is as relevant as ever in the era of big data. Even as data industry fads change with seasons, you will still need strong analytics data governance and a repository for master data, and that’s your DW.

IBM has maintained and deepened our leadership in the DW market over many years, and, as Wendy Lucas discusses in her BD&A Hub blog, an independent analyst firm continues to recognize that fact. “Regardless of your specific DW requirements,” she states, “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.”

I’d like to highlight the word “innovation” in what Wendy says. Just because the DW market is of long vintage and has many mature offerings, such as IBM PureData System for Analytics (PDA), doesn’t mean that we solution providers are slacking off on new features. Go check out an important new feature, IBM Fluid Query, which provides a query and data movement toolkit for leveraging insights from data in PDA and various Hadoop platforms, including IBM InfoSphere BigInsights.

The DW innovations don’t stop there. In Dennis Duckworth’s commentary on the Gartner study, he discusses IBM’s ongoing investments in logical DW solutions that can be deployed as hybrid clouds, or as with dashDB, as full-blown SaaS offerings. As he notes, one of the important innovations in dashDB is built-in integration with our NoSQL database-as-a-service, Cloudant.

All of these IBM investments serve to further the evolution of the logical DW as a more fluid, agile, and versatile enterprise analytics platform.

About James,

JAMES KOBIELUSJames Kobielus is IBM Senior Program Director, Product Marketing, Big Data Analytics solutions. He is an industry veteran, a popular speaker and social media participant, and a thought leader in big data, Hadoop, enterprise data warehousing, advanced analytics, business intelligence, data management, and next best action technologies. Follow James on Twitter: @jameskobielus

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

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