Introduction to Data-Centricity

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.

Welcome to Fluree’s series on data-centricity. Over the next few months, we’ll peel back the layers of the data-centric architecture stack to explain its relevance to today’s enterprise landscape.

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.

Freeing data from a single monolithic stack allows for greater opportunities in accelerating digital transformation: data can be more versatile, integrative, and available to those that need it. By baking core characteristics like security, interoperability, and portability directly into the data-tier, data-centricity dissolves the need to pay for proprietary middleware or maintain webs of custom APIs. Data-Centricity also allows enterprises to integrate disparate data sources with virtually no overhead and deliver data to its stakeholders with context and speed.

Data-Centric architectures have the power to alleviate pain points along the entire data value chain and build a truly secure and agile data ecosystem. But to understand these benefits, we must first understand the issues of “application-centricity” currently in place at the standard legacy-driven enterprise.

Big Data ≠ Valuable Data
The application boom of the ’90s led to increased front-office efficiencies but left behind a wasteland of data-as-a-byproduct. Most application developers were concerned with one thing: building a solution that worked. How the application data would be formatted or potentially reused was secondary or perhaps out of sight.

Businesses quickly realized that their data has a value chain – an ecosystem of stakeholders that need permissioned access to enterprise information for business applications, data analysis, information compliance, and other categories of data collaboration. So, companies invested in building data lakes – essentially plopping large amounts of data, in its original format, into a repository for data scientists to spend some time cleansing and analyzing. But these tools simply became larger data silos, introducing even higher levels of complexity.

In fact, 40% of a typical IT budget is spent simply on integrating data from disparate sources and silos. And integrating new data sources into warehouses can take weeks or months – which is a far cry from becoming truly “data-driven.”

In the application-centric framework, data originates from an application silo and trickles its way down the value chain with little context. To extract value from this data is a painful or expensive process. Combining this data with other data is an almost impossible task. And delivering this data to its rightful value chain is met with technical and bureaucratic roadblocks.
These are not controversial claims. According to an American Management Association survey, 83% of executives think their companies have silos, and 97% think it’s having a negative effect on business.

Let’s explore how these data silos continue to proliferate, even after the explosion of cloud computing and data lake solutions:
The Application-Centric Process
Today, developers build applications that, by nature, produce data.