Data-centric and data-driven are not synonyms. In fact, unchecked ambitions in acquiring and analyzing data sets could easily make your organization less data-centric, as you drown in your data lake. Luckily, the data-centric approach has a life preserver: use the shared model of your data-centric architecture as a way to organize and interpret the data you are acquiring in an agile way. It is possible to get the best of both possible worlds. You can become a data-centric / data-driven organization.
In Creating a Data-Driven Organization, Carl Anderson starts off saying, “Data-drivenness is about building tools, abilities, and, most crucially, a culture that acts on data.” I recommend the book for all managers in data-intensive companies. Th ebook echoes what most people think of when they think “data-driven.” It’s about acquiring and analyzing data to make better decisions. As our appetite grows, we gather more and more data. That eventually manifested as big data.
But acquiring more data isn’t going to make you data-centric, and may even make you less data-centric. If each dataset you acquire has a different data model, and you just plop them down in a data lake without any attempt to harmonize them, you are getting less and less data-centric—even as you become more data-driven.
Data lakes are popular now. The traditional data warehouse environment relied on complex ETL (extract, transform, and load) routines to scrub the data and get it all to conform to a predesigned data warehouse schema. But this process is slow. It is not untypical for it to take weeks or months for a new data source to be incorporated into the data warehouse environment. The biggest problem is, until the data is normalized and cleansed, it’s unavailable for analytics.
Data fabrics to the rescue
This last-mentioned problem is what the data fabric concept is tackling. According to Gartner By 2024, data fabric deployments will quadruple efficiency in data utilization while cutting human-driven data management tasks in half. Within inbuilt analytics reading metadata, data fabric is able to learn what data is being used. Its real value exists in its ability to make recommendations for more, different, and better data, reducing data management by up to 70%.
data fabric deployments will quadruple efficiency in data utilization while cutting human-driven data management tasks in half.
But let's get back to the data-driven and data-centric. According to Kevin Doubleday Data-centricity is a mindset as much as it is a technical architecture — at its core, data-centricity acknowledges data’s valuable and versatile role in the larger enterprise and industry ecosystem and treats information as the core asset to enterprise architectures.
Opposite of the “Application-Centric” stack, a data-centric architecture is one where data exists independently of a singular application and can empower a broad range of information stakeholders.
The Data-Centric Architecture treats data as a valuable and versatile asset instead of an expensive afterthought.
Industries are moving towards data ecosystems — an integrative and collaborative approach to data management and sharing. Here are just a few examples:
Data-driven business applications today touch many internal and external stakeholders
Enterprises are building (many for the very first time) a master data management platform for a 360-degree-level view into their master data assets.
Enterprises are creating data “knowledge graphs” that link and leverage vast amounts of enterprise data under a common format for maximum data visibility, analytics, and reuse.
More advanced enterprises are building “data fabrics,” a hyperconverged architecture that focuses on integrating data across enterprise infrastructures.
Enterprises are realizing the value of “data marketplaces,” where “golden record” information can be subscribed to within a data-as-a-service framework.
Towards data-centric future and profits
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