IBM Data Warehousing – a Point of View on Modernizing your Data Warehouse Environment

By Wendy Lucas

Technology is rapidly changing, allowing us to consume more data and gain more insight from the mountain of data we all produce every day.

But how does this impact Data Warehousing? 

Are new technologies making a data warehouse a thing of the past? 

Absolutely not!  Through 2020, over 90% of big data implementations will augment, not replace existing data warehouses[1] yet companies still struggle with questions like:

  •  How do I deliver faster analytics for my users?
  • How do I get better price-performance?
  • How do I quickly add new data or applications without impacting current capabilities or performance?
  • Do I need columnar or in-memory technology for my analytics?
  • Should I build my own or leverage an appliance?
  • How do I build an architecture that has the capability and can scale to collect and analyze all forms of data?

The idea of a single, monolithic data warehouse has yielded to the notion of a logical       data warehouse or LDW.  The LDW was introduced by Gartner in 2010 and can be described as a warehouse environment comprised of different repositories of information taking various forms:  structured, unstructured, transactional, operational data stores, third normal form, dimensional, subject area marts, etc., each meeting a certain need of the business in a fit for purpose approach.  In a logical warehouse environment, it is critical to have technology that allows you to add these capabilities in an agile way that responds quickly to changing business needs.

But where do you start?

Companies are tackling these questions by modernizing their architectures to take advantage of big data.  You don’t need a big bang approach.  Start by prioritizing critical business needs or pain points.  Common entry points include adding new data sources, new data types, new analytic capabilities, boosting performance to deliver faster insight and exploiting new technologies such as in-memory databases and Hadoop technologies.

Data Warehouse modernization builds on the existing foundation, without compromising the integrity and trust of the users but allows for new capabilities to be added at the speed the business requires them.  Big Data is not the end of the Data Warehouse but it is causing an evolution and IBM is well positioned to help our customers leverage their existing investments and modernize their environments.

Are you ready to take action?  How can your business move ahead with a modernization strategy?

Read more about IBM’s point of view on modern Data Warehousing:

About Wendy Lucas

Wendy Lucas is a Program Director for IBM Data Warehouse Marketing. Wendy has over 20 years of experience in data warehousing and business intelligence solutions, including 12 years at IBM. She has helped clients in a variety of roles, including application development, management consulting, project management, technical sales management and marketing. Wendy holds a Bachelor of Science in Computer Science from Capital University and you can follow her on Twitter at @wlucas001

[1] :“Predicts 2014: Why You Should Modernize Your Information Infrastructure”, November 28, 2013.  Gartner


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