Data-centric architecture revisits architecture and turns that architecture on its head. Ever since the dawn of client-server computing, applications have been the focus of enterprise IT buyers. The integrity, interoperability, and shareability of data have had to wait in line behind the apps..
Dave McComb, the author of the Data-Centric Manifesto and President of Semantic Arts, which hosts the Data-Centric Architecture Forum every year, has consistently used the term “data-centric” to point to two different but related things:
- Methods of harnessing and nurturing data as an organically enriching asset.
- Methods of harnessing and nurturing a unified data model within each organization.
For those not familiar with data-centric architecture innovation and why it’s important, here are some basic comparisons:
Characteristics of app– versus data-centric enterprise behavior
Non-Linear Systems Thinking
The vision of data-centric architecture has one overarching benefit: it gets at root problems that have become more and more paralyzing as we try to move forward to achieve what companies hope will be true digital transformation.
The non-linear thinkers in the semantics community are right: Designing in efficiencies for the data layer requires transformation at the system layer. Leaders of a few dozen organizations understand this necessity. But we’ll need a lot more non-linear thinkers to bring the vision to fruition.