Data Centric Architecture

Data Centric architecture

The value proposition of global systems integrators (GSIs) has changed remarkably in the last 10 years. By 2010, it was the waning days of the so-called “your mess for less” (YMFL) business model. GSIs would essentially purchase and run a company’s IT shop and deliver value through right-shoring (moving labor to low cost places), leveraging supply chain economies of scale and, to a lesser degree, automation.

This model had been delivering value to the industry since the ‘90s but was nearing its asymptotic conclusion. To continue achieving the cost savings and value improvements that customers were demanding, GSIs had to add to their repertoire. They had to define, understand, engage and deliver in the digital transformation business. Today, I am focusing on the value GSIs offer by concentrating on their client’s data, rather than being fixated on the boxes or cloud where data resides.

In the YMFL business, the GSIs could zero in on the cheapest, performance compliant disk or cloud to house sets of applications, logs, analytics and backup data. The data sets were created and used by and for their corresponding purpose. Often, they were tenuously managed by sophisticated middleware and applications for other purposes, like decision support or analytics.

Getting a centralized view of the customer was difficult, if not impossible. First, it was due to the stove piping of the relevant data in an application-centric architecture. In tandem, data islands were created for analytics repositories.

Now enters the “Data Centric Architecture.” Transformation to a data-centric view is a new opportunity for GSIs to remain relevant and add value to customer’s infrastructures. It is a layer deeper than moving to cloud or migrating to the latest, faster, smaller boxes.

A great way to help jump start this transformation is by rolling out Data as a Service offerings. Rather than taking the more traditional Storage as a Service or Backup as a Service approach, Data as a Service anticipates and provides the underlying architecture to support a data-centric strategy.

It is first and foremost a repository for collected and aggregated data that is independent of application sources. From this repository, you can draw correlations, statistics, visualizations and advanced analytical insights that are impossible when dealing with islands of data managed independently.

It is more than the repository of the algorithmically derived data lake. A Data as a Service approach provides cost effective accessibility, performance, security and resilience – aimed at addressing the largest source of both complexity and cost in the landscape.

Data as a Service helps achieve these goals by minimizing, simplifying and reducing the data and its movement within and outside of the enterprise and cloud environments. This is achieved around four primary use cases, which range from enterprise storage to backup and long-term retention:



Each of the cases illustrates the underlying capabilities necessary to cost effectively support the move to a data-centric architecture. Combined with a “never migrate or refresh again” evergreen approach, GSIs can focus on maximizing value in the stack of offerings. This approach is revolutionary.  In past, there was merely a focus on the refresh of aging boxes, or the specifications of a particular cloud service, or the infrastructure supporting a particular application. Today, GSIs can focus on the treasured asset in their customer’s IT — their data

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