Extracting Value from SaaS with Data Centric Architecture

BrandPost By Yousuf Khan
Jun 11, 2018
Technology Industry

SaaS adoption is growing, and that can be a problem without a software-centric data strategy. Here’s how to create a plan that will give you flexibility for the future.

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Credit: Shutterstock

Today, organizations can do more with their data than ever before. Advances in networking over the past decade and the evolution of storage and compute over the last several years have unlocked an entirely new universe of possibilities and outcomes. But new, unique challenges have emerged alongside these opportunities. Organizations require a means of simplification and holistic insight to build a truly strategic infrastructure. For many, that journey begins with having Software-as-a-Service (SaaS) at the heart of their IT strategy.

It’s been an interesting journey throughout history when it comes to innovation. When Henry Ford pioneered the assembly line in 1913, the result was previously unattainable personal freedom for the average consumer – and an extra weekend day– all because human beings made it easier to build a car. A little over 100 years later, we’re building cars that drive themselves. Today’s tech has the potential to propel a truly unprecedented level of achievement both for business and humanity. Software is the vehicle and data is the oil.

But to capitalize, this disruptive potential must be effectively harnessed to hold value. Along with the explosion of data, we’ve seen an explosion of tech created to store, move and process that data – public and private clouds, NVMe-based storage arrays, containers and of course, SaaS. Every organization – of any size – must be strategic in how it deploys and manages these tools together to best serve its specific data needs. At Pure Storage, we call this a “data centric architecture” –  essentially the idea that every technology integrated into your infrastructure should be optimized for turning data into value. We’ve seen a growth in software as an engine for agility and transformation, but its true value is extracted from the data it holds and generates. For SaaS to be truly “data-centric,” it has to work seamlessly with other applications and technologies.

One thing is for sure: SaaS adoption and usage is only going to increase in the enterprise, which can become problematic if SaaS usage grows without a purposeful data strategy. This is a common pitfall we’ve observed with SaaS apps being deployed without a deeper integration and data centricity. Organizations are faced with a few critical questions: How do you extract data from dozens of standalone applications into one central data hub? How do you take that data and make fundamental changes to your business? All of this leads to an ultimate point for how SaaS should be viewed in enterprises in the time ahead – is your software strategy data centric?  

Our marketing organization, for example, uses more than 65 applications to execute campaigns and measure performance. That means 65 separate sources of data, all of which have to be accessed within the corresponding application. The only way to understand whether a new application has added value, or how it’s performing, is to collate that data into one place where it can provide a clear picture of organizational performance and needs. In order to get the most value from your data, think about a strategy for how you’ll get critical data off of these applications into a central repository.

To fully optimize on your SaaS footprint, ensure your applications integrate and communicate. Correlate business connections and outcomes, and identify the applications generating data that can help achieve those outcomes. Finally, find a means of extraction for that data, and establish a central hub or data lake that helps turn insight into innovation.

With the right approach, organizations can extract true value from a SaaS footprint that will continue to grow.

Yousuf Khan is the CIO of Pure Storage. Visit https://www.purestorage.com/evolution.html to learn more about the intersection of data, intelligence, and AI.