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By Cody Sanford, former CIO, T-Mobile
You’ve heard it said over and over: Data is an enterprise’s crown jewel. That’s why it’s frustrating to see so many companies struggle to get real value out of their data. For the overwhelming majority of companies, data is the single most under-leveraged revenue growth asset. Here, I’ll discuss what I’ve seen happening, and some ideas to help enterprises get the most out of their data.
Data represents the last mile of enterprise modernization. Velocity, scale, and agility were the aims of the first legs of digital transformation and enterprises have spent the better part of the last decade decoupling, re-factoring, and automating to realize those benefits. Many companies have also begun to aggregate and transform their data storage ecosystems by moving to cloud-native architectures.
Most of this data today is used to enhance analytics capabilities and improve assisted and unassisted digital customer journeys. But a remarkably small number of companies have truly harnessed the power of all of their data to generate true revenue growth.
Why? Companies’ most valuable data—generated through the billions of interactions they have with their customers—remains locked in silos and isolated in diverse operational data stores. Ironically, these data stores have expanded rapidly as a byproduct of digital transformation. As services and applications became more and more decoupled and finer-grained, they have exploded in number.
Likewise, the operational databases and abstraction layers that supported them have multiplied. As the numbers of data stores increased, so did the diversity of operating platforms. Most large enterprises support dozens of mostly proprietary and highly dislocated NoSQL (and SQL) platforms—it’s like a puzzle with pieces that really don’t fit together.
All of this has dramatically increased the complexity that organizations wrestle with when it comes to their data architectures—making it harder to optimize for scale and layering on costs. This mesh of databases is expensive to license and expensive to maintain.
What’s worse, the data in them is often relegated to storage and direct transactional support for their northbound applications. At best, the data is streamed into data lakes and used for decision-analytics.
The immense potential of the data—as a real-time and contextual revenue driver—remains unrealized. It’s just too hard to capitalize on the opportunity.
But what if we could simplify these data environments and unlock the value of this data trove,  by increasing data availability and speed, and what if we could significantly reduce the license and operating costs?
The way you do this is by standardizing your central operational data stores; unifying the format the data is in with a single cloud-native architecture to abstract, stream, combine, and present the data to all the applications in your organization.
Getting to this point isn’t easy, but there are several tools and capabilities that can make data standardization achievable. A critical piece of this puzzle is open source software (OSS). Increasingly, companies that succeed at producing revenue with their data are relying on OSS to build various components of their data architectures. It’s a key way to tap into the latest cutting-edge innovations and also easily build and test different tools without major investments.
Apache Cassandra®, a proven, best-of-breed NoSQL database, and Apache Pulsar, an advanced messaging and streaming platform, are powerful data stack components, for example, that together can help enterprises manage all their real-time data—both data in motion and data at rest. Other important pieces of a data architecture that are also OSS include Apache Spark, an engine for large-scale data analytics, and Elasticsearch, an open source search and analytics engine.
Another key to freeing up teams for innovation is serverless database technologies, which enable organizations to develop and run data-centric apps without worries about bumping into scale limitations. A variety of vendors offer serverless DBs, including DataStax, PlanetScale, CockroachDB, and others. Workload management becomes a thing of the past, and developers can easily test new ideas without worrying about database capacity constraints. In a similar vein, data APIs (like the open source Stargate) are a critical way to simplify development by abstracting away the complexities of data layers, and freeing developers to do what they do best: build.
The struggle to turn real-time data into revenue is one faced by many enterprises today, but there’s a range of technologies that enable organizations to break this valuable asset out of silos, standardize it, and use it to its fullest potential. 
To learn more about DataStax, visit us here.
About the Author:
Cody Sanford served as T-Mobile’s EVP, CIO, and Chief Product Officer until April 2021, leading the company’s digital transformation strategy fueling the Un-carrier revolution. He spearheaded the development of a product-centric technology organization that today leverages the power of people, process, and technology to bring to life T-Mobile’s innovative experiences for customers and frontline employees. Under Cody’s leadership, the Product & Technology organization drove T-Mobile’s digital transformation, with an industry-leading software development shop, expansion into adjacent products and services categories, and a leadership role in delivering open source innovations that solve large customer pain points.

Cody now serves as a Board Member and Board Advisor to a number of technology, enterprise software, and technology services companies.
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