Enterprises globally are evolving. Much of it has to do with data. Already, some of the world’s most extensive Internet and IT companies, such as Amazon, Google, and Microsoft, have become data-centric businesses. Such a business model has data at the core.
At the start of 2019, data-centricity was among the top 5 trends to watch in the world of business. We are in the last quarter of the year, and that observation looks justified.
In the last decade or so, with advances in technology, we saw a movement in the corporate world that started with “Big Data” and then moved on to the deployment of data analytics. Simply collecting huge tracts of data was not enough; it was the means to an end. Tools, including applications, were built around organizational data to help companies do business more scientifically.
We are now in the next phase of the process – transforming an organization into a data-centric one. What does that mean?
Data-centricity Can Be Explained, thus:
In this way of doing business, data is considered an asset as tangible as a company’s hardware or its headquarters building. It is at the heart of the Enterprise’s operations; in fact, the entire IT and business architecture is built around the fact that data is a prime and permanent asset.
A simple, non-technical explanation of data-centricity can be moving away from an application-centric to a data-centric way of doing business.
So far, we’ve seen that those businesses that decided to deploy data analytics worked in this manner: data was extracted from business practices and fed into a data warehouse or a data lake. Data analytics resulted from a business process using an application built with that specific functionality. Which meant the application owned the data.
Data-Centric Example: For example, in this business model, individual sections buy products or services to address a specific commercial need. In all this, however, the potential value represented by that data was rarely input into the Enterprise’s operations. In a nutshell, data analysis was being done in silos, something that almost all Enterprises that have deployed data analytics models will agree on. In an application-centric approach, each app has its own data model, storing only the data it needs. As the years go by, each business unit develops a segmented vision of its own data, not shared with the rest of the business.
In such a data-driven organization, data analysis flows from the top down. In a data-centric design, however, data analysis flows in the exact opposite manner but feeds into operations.
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Data is no longer being used to extract answers for daily business questions. Still, it is the data that feeds the units the information needed to run the business more efficiently.
But there’s more for an Enterprise to become truly data-centric. Its human resources, processes, and technologies must be geared towards firmly understanding that data is at the heart of the Enterprise, and must be used collaboratively to advance the business goals.
What Is a Great Advantage To Be Derived From Being Data-Centric?
An Enterprise can accrue many benefits by becoming data-centric:
- First and foremost, a data-centric model gives a massive financial lead to Enterprises adopting this approach, starting with savings on infrastructure and a reduction in other recurring costs
- Makes the entire Enterprise “data-smart”, and not just a few team leaders
- Can be used to disrupt the market a business is in digitally
- No more data silos within the organization
How Can My Enterprise Become Data-Centric?
As a starting point, it helps if your organization is already data-driven, as at least some business units are familiar with the benefits of data analytics. It’s about making organizations start walking a path where all operations and analytical processes are based on ALL available data, without needing to know exactly what questions they will ask.
But the transformation from data-driven to data-centric can’t happen overnight.
Any Enterprise wishing to become a data-centric one, first and foremost, needs to invest in:
- Its people, which also includes the company culture
- Its infrastructure
- Its processes
The investment has to be of mind, matter and money”.
A crucial first step in this transformation is for IT and business divisions to come together to brainstorm several issues, starting with a postmortem of the current in-house technology.
Once the business goals are defined and the future growth mapped out, the technology required to become a data-centric Enterprise needs to be identified. In this process, they will face this question: Do I need to re-invent my entire tech stack?
As vast amounts of data flow in, it becomes challenging for Enterprises to store, retrieve, and process it using traditional application architectures. For a data-centric business, what is needed is an agile, data-centric architecture that responds to constant change.
There’s obviously no single system available that can address the large and ever-increasing scale of data and its computation. But new infrastructure and application concepts, primarily based on artificial intelligence, are emerging to address these problems in data-centric computing.
Here’s what the biggies have done. Microsoft Azure or Amazon Web Services, for example, use distributed architectures that work in tandem. Each also retains the ability to scale as the data load goes up.
But this may not necessarily be what your business has to do while turning data-centric. Data team leaders need to sit with business heads and discuss their critical business priorities before deciding on the tech solution.
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Data leaders in the Enterprise need to automate data sources and analytics distribution to various internal stakeholders. For this, they may deploy an architecture that prefers the shared data models over individual data applications having their own separate models.
