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7 Keys for Data Agility

Written by GoodData Author  | 

7 Keys for Data Agility

It’s an all too common scenario. What should be a theoretically simple task of providing unified and usable access to information across departments and systems devolves into a web of complexity, unresponsive to the demands of today’s data driven business culture.

Executives, analysts, and developers are united in their frustration with the inability of legacy vendors using traditional architectures to keep up with the pace of data expansion and support even minor changes in schema or business requirements.

Consider that according to Forrester Research companies use only 12.5% of their available data for business intelligence.1 Combined with the flood of new data from social, server logs, sensors, etc. it is apparent that a different approach is required to be competitive in our data-centric economy.

One principle from the Agile Manifesto best articulates the mindset required for success:

“Welcome changing requirements, even late in development. Agile processes harness change for the customer's competitive advantage.”

Even authors of the manifesto will point out that the term overused. There are no agile tools. However the right tools applied properly increase our agility and allow us to deal with the data dilemma.

Pavel Kolesnikov and I recently discussed the main considerations for architecting a data warehouse and implementing an end to end solution that maximizes agility. Our top 7 suggestions:

  1. Start with the minimum requirements
  2. Anticipate changes and iterate
  3. Automate as much as possible
  4. Separate the logical model, the analytical model, and the data storage
  5. Use the right tool for the job
  6. Know ALL of your data sources
  7. Capture everything, including historical changes

Benchmark Your BI Environment, Forrester Research, Inc., October 2, 2013


Written by GoodData Author  | 

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