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The Data Doctrine™ V2

Objective measures for improving data outcomes

It is fine to say that "we want to be data-centric, data-first, data-driven, data provocateurs but without commonly agreed upon definitions and objective measures, it is all just hand waving, isn't it?

Directly building on the original agile manifesto. Todd and Peter spent some time producing the first version. Published in 2017, hundreds have joined various efforts to improve our collective understanding of what are the concrete steps required to achieve better data outcomes. We have updated the original and are anxious for feedback from the community.  

We are uncovering better ways of developing IT 
systems by doing it and helping others do it. 
Through this work, we have come to value:

•  data programmes - driving IT programs
•  informed information investing - over technology acquisition activities
•  stable, shared organizational data - over IT component evolution
•  data reuse - over the acquisition of new data sources

While there is value in the items on the right, we value the things on the left more

Several of our colleagues have had complimentary thoughts in this area also, please check their efforts out as well (links and more below)

The Data-Centric Manifesto

Dave McComb and his team have been at this for many years. His need for this change is an excellent straight forward argument for clearer thinking on this subject.

The Data-Leader's Manifesto

A group including Nina Evans, John Ladley, Danette McGilvary Kelle O'Neal James Price, and Tom Redman published in 2017 and have garnered hundreds of signatures of support.

  • Data-centric computing - Wikipedia article

  • Database-centric architecture - Wikipedia article

  • Introduction to Data-Centricity by Kevin Doubleday - The Data-Centric Architecture treats data as a valuable and versatile asset instead of an expensive afterthought. Data-centricity significantly simplifies security, integration, portability, and analysis while delivering faster insights across the entire data value chain. This post will introduce the concept of Data-Centricity and lay the framework for future installments on Data-Centricity.

  • Data-centric Architecture — A Different Way of Thinking - Data-centric architecture (or model) is a solution that addresses the issues of conventional capital project methodology and delivers positive results. Data-centric execution architecture has been around for a few years and is becoming more popular in the energy sector, where many owners and operators work with a specialized system integrator.

  • Why and how to adopt a data-centric architecture - Data has become one of the most valuable assets in the enterprise. IT teams must make changes -- both culturally and technically -- to ensure their strategy reflects that.

  • The Difference Between Data-centric and Data-driven by Carol Dunn - The recommendation that companies become more data-centric sounds like a great idea. Most companies have the ability and means to accumulate data – in some ambitious cases that turns out to be LOTS of data. Basically, such companies are driven by data. But that’s not the same as being data-centric.

Additional recommended reading

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