These principles guide organizations attempting to better use data as an asset
What are the principles of Data-Centric Thinking?
Leaders can increase organizational effectiveness by focusing on data as a central, shared resource (or better still) as your sole, non-depleteable, non-degrading, durable strategic asset. Data-centric thinking means practicing four data doctrine precepts.
Data-Centric Thinking Values: (In the spirit of the Agile Manifesto)
We are uncovering better ways of developing
systems by doing it and helping others do it.
Through this work we have come to value:
Data Programmes Preceding Software Projects
Stable Data Structures Preceding Stable Code
Shared Data Preceding Completed Software
Reusable Data Preceding Reusable Code
That is, while there is value in the items on the right,
we value the items on the left more.
Agile Software Development isn't enough:
The Manifesto for Agile Software Development is an excellent step in the right direction, however more needs to be done to address the fundamental challenges facing IT. Systems are comprised of different components including people, processes, hardware, software, and data. Agile software development practices deliver better quality software products more rapidly. Better data products pervade and persist in all aspects of systems and increasingly benefit
The Need for the Data Doctrine:
In order for organizations to effectively incorporate data assets in support of organizational strategy, they need to establish a data management programme that is separate from, external to, and precedes software development projects!
Data management and software development must be separated and sequenced
Data structures must be stabilized before the software accessing them can be correctly constructed.
Shared data structures require programmatic development and evaluation.
Reusable data should be leveraged by reusable software.
Note: We are deliberately using the British spelling of the word programme to differentiate it from a software program - in this case used to denote an effort that is initiated and continues until the organization decides that it no longer needs to perform this kind of work, or the organization ceases to exist.
Inadequate or nonexistent data education at all levels leads to knowledge workers under-appreciating the value of shared data assets. This, in turn, leads organizations to over rely on efforts such as software development.
Lack of this data education leads organizations to omit data programmes and instead try to manage shared organizational data assets at the project level.
Increase IT spending compensates for lack of data programmes. Organizations, consequently, spend resources on activities like integrating and cleaning up data and managing far more data than is necessary to manage strategically.
Look around and you can see the consequences
of ignoring the foundational role that data plays in our organizations: