Against the backdrop of data growth, more and more organizations, regardless of geography and industry, are deciding to make their IT landscapes data-centric.
The principle and benefits of data centricity
Dave McComb, founder of the company Semantic Arts and author of the concept of data-centric architecture (DCA), in his book "The Data-Centric Revolution: Restoring Sanity to Enterprise Information Systems" (2019) provides the following definition: "A data-centric architecture is one in which data is taken as the primary and permanent asset and applications come and go. In a data-centric architecture, the data model precedes the implementation of any application and will exist and act long after it is gone".
Simply put, the idea behind DCA is to get rid of the database within each application. Instead, every application should work with a single enterprise data cloud where every business object has only one, valid and complete view. Imagine a MDM that stores absolutely all corporate data, and doesn't synchronize it between applications, only makes it available via APIs. The advantages of this approach are obvious: the need for integration is eliminated and many data governance problems are solved.
Currently, a number of IT companies are offering private businesses and government organizations solutions to transition to a data-centric model. However, these offers are not always accepted, as the transformation can seem complex and expensive.
"If you do a root cause analysis, you eventually get to complexity as the primary driver of lack of business agility. Our systems have become very complex and highly interdependent (which is just a further dimension of complexity) such that even the simplest change becomes economically unjustifiable. We have many clients for whom the addition of a few fields to a database can be a multi-hundred-thousand-dollar project. Because of this, many incremental changes are not attempted. Instead business users rely on shadow IT to cook up something, which gives them a short-term win, at the cost of making the environment even more complex.", Dave McComb reported in a seminal interview for Business Rules Community (2018).
There are options for a gradual transition to DCA. For example, the first step may be to link all data in Enterprise Knowledge Graph. In this case, if previously the data on objects of each type were scattered between different storages, in EKG they are consolidated into a single view. The structure of information in EKG unified ontological data model. This allows to combine heterogeneous data into a coherent representation, which can be used by analysts and programmers to solve their applied problems. Among other things, EKG may become the only source of information for new business applications that will no longer have their own DBMS. Gradual growth in the number of such applications will make it possible to move smoothly towards a complete transition to a data-centric architecture.
We did an ontology for the Washington State Department of Transportation. In the enterprise model were concepts such as: geospatial locations, roadways, contact information, organizations, physical substances (gravel, concrete, etc.), and something they called "roadway features" which is anything permanently attached to the earth close enough to a roadway that it could be hit by a car (guardrails, signs, trees, etc.). , – Dave McComb said in an interview.
The structured dataset exists in a self-contained repository, which offers many advantages:
- Reduces dependency on specific business applications. Since data is not stored in application repositories, software upgrades become less labor-intensive, making it easier to replace outdated applications with new ones.
- Data structure and processing rules can be changed at any time.
- If information is contained in several databases, it causes duplication of information. In DCA, unnecessary duplicates are not created, which means that no additional resources are required to store them.
- The process of obtaining analytical representations is simplified.
- The cost of ownership of enterprise IT infrastructure is reduced.
Why is the transition to DCA necessary?
The IT landscape of many companies is shaped in such a way that its center is business applications. Combined with the growing volume of data processed by applications and dynamically changing processing requirements, this becomes a challenge. Maintaining, refining and integrating such applications is a complex and expensive task, as any change in the data structure entails the need to intervene in the software code.
Sparse data storage leads to many undesirable effects:
- Employees have to search for information in multiple information systems and/or repositories.
- Since information about the same objects exists in different databases, additional costs are incurred for storing essentially unnecessary duplicates. At the same time, it is often impossible to understand which of the object instances is the most relevant.
- Information about the same objects in different databases needs to be synchronized between them.
- Software upgrades are becoming more difficult.
If you’re still adhering to the 40-year-old app-centric approach, it will be next to impossible to compete with modern, data-centric businesses as their numbers continue to grow. After all, successful businesses already operate as efficiently as possible, and squeezing meaningful change out of your architecture is extremely difficult. Instead of eking out fractions of a percent in improvement, it’s time to embrace a paradigm change like data-centricity. When you can eliminate integration efforts and instantly free up 50% of your IT resources on any project, you will have the bandwidth you need to deliver enterprise-changing innovation – Cinchy CEO and co-founder Dan DeMers in the article The Shift from an App-Centric to Data-Centric Architecture (2021).
Of course, there are other reasons to move to DCA, such as data security. However, the growing volume of information and its increasing complexity are key. Data can be both a benefit to an organization and a burden. Today's organizations should move toward data centricity if they want to spend their IT budgets productively and monetize their data.
DataVera offers Kazakhstani companies faced with the problems of dramatic growth in the volume and complexity of data structure, to use the opportunities that modern information technologies provide for radical reduction of data integration costs, increasing the speed of bringing digital products to market, business transformation through the use of data.
