How to Kill Complexity with Data Standardization

By Chet Kapoor, Chairman and CEO of DataStax

Today, we are in the final, and most important, phase of digital transformation. The first phase started with “mobile”. The second was focused on cloud computing. And today it’s all about data.

Data is at the heart of how enterprises create value for their customers, and you cannot win without it. But yesterday’s enterprise data architecture can’t meet today’s need for scale and agility.

Enterprises and developers are faced with 3 problems:

• The need for speed. Disruptors are everywhere, and users are demanding data to make decisions in real-time.
• Data is spread across several platforms. These real-time silos make it hard for developers to be agile, and they prevent enterprises from getting the big picture about their customers.
• Enterprises are still maintaining legacy systems. These are expensive and they make it hard to optimize for scale.

What if you could simplify these environments to unlock the power of your data across the enterprise? The answer is standardization. It’s about putting data into a consistent format that allows you to use it in real-time and create value for your customers. Our perspective at DataStax is that a successful standardization comes down to two things: technology and people/process.

The “easy” – technology

Standardizing your technology is a lot of work. But as the software gets more complex, it is also getting easier to manage and use with things like SaaS models and serverless. Here are some of the key tech components that will help with data standardization.

Kubernetes

Containerization in the cloud and on-premises enables developers to decouple and automate compute and storage. For data platforms with complicated setups—like those needed to retain data from one app to another or for backup-and-recovery requirements—Kubernetes and Kubernetes-based tool capabilities are crucial.

Data APIs

Data APIs simplify integrations between disparate tools and platforms by protecting data teams from the complexity of the different layers. This speeds time to market and reduces the chance of causing new problems in existing apps. APIs and API platforms, like Stargate, enable more innovation and freedom of choice for developers.

Serverless

Serverless databases enable enterprises to build and operate data-centric apps with infinite scale. They also lower the expertise required to build because developers don’t need to configure solutions or manage workloads. As a result, serverless speeds up deployment from weeks to minutes (seriously!) and lowers TCO substantially.

Open source software (OSS)

Many tech leaders think about OSS as a means to an end versus an end itself. But to scale apps, you have to push beyond the boundaries of legacy data ecosystems and proprietary technologies. You need a data architecture with open-source components that can be replaced with new tech as needed.

OSS—like Apache Cassandra^®, the highly scalable, NoSQL-standard database behind some of the world’s most-recognized brands, and Apache Pulsar, the distributed messaging and streaming platform—enables collaboration, grows adoption with open code, and improves reliability as more users and contributors participate. Most importantly, OSS fuels technical and business innovation.

The hard – people and process

Of course, you need the right tech stack. But the CIOs I’ve spoken with all agree that the hard part is leading and inspiring people through change. Here are some impactful steps you can take.

 Create a data culture

It starts by creating a data culture. You can’t buy this; you have to build it. It’s crucial to use data everywhere. Decision-making—at all levels of the company—should be data-driven. At DataStax, we even use a data-driven approach for onboarding new hires.

Another important step in building a data culture is to stop investing in legacy applications, which are based on legacy processes. Every time you think about a legacy process, think about the opportunity cost. If you had one dollar and you couldn’t break it, would you spend it on investing in a legacy process? Or would you come up with something completely new—something that enables people to do their jobs more efficiently and focus on innovation?

 Adopt an iterative mindset

Everybody thinks of iteration as an IT thing, but business leaders need to have an iterative mindset, too. Apply an agile approach to data architecture and don’t be afraid to experiment with different concepts. It’s okay to fail and iterate quickly.

Establish cross-functional data squads or pods

Many of us have made the mistake of shipping our organizational charts to our customers. But customers don’t care about how we’re organized. They care about value. It’s not about the function you’re in. It’s about the impact you create.

Build “pods” of business folks, data architects, engineers, and app developers. And give them end-to-end accountability for building the data architecture. Focus on collaboration and aligning people on the same goals. This will help you break silos and find the right data that drives business outcomes.

Invest in an enterprise-wide DataOps initiative

Deliver a central data platform that uses machine learning to enable self-serve analytics. This will create scalable, repeatable, and predictable data flows.

Note that the cycle time of the two layers—data pods and data platforms—are and should continue to be different. The interface is an API. Pods will move fast and innovate like crazy. Both are enterprise-wide initiatives but work at very different velocities.

 Bringing together the easy and the hard

At the end of the day, it’s all about our people. Team composition is important with a strong focus on diversity and inclusion. Hire people who are mission-oriented, bring a different perspective, and take an outside-in point-of-view. And invest in reskilling your team – a recent survey found that 53% of people in an enterprise need to be reskilled.

As you approach the last mile of your digital transformation, consider the technology pieces and people/process changes. An effective data standardization strategy will help you uncover valuable insights, drive revenue growth, and achieve true business agility—ultimately creating value for your customers.

To learn more about data standardization, visit us here.

About Chet Kapoor:

Chet is Chairman and CEO of DataStax. He is a proven leader and innovator in the tech industry with more than 20 years in leadership at innovative software and cloud companies, including Google, IBM, BEA Systems, WebMethods, and NeXT. As Chairman and CEO of Apigee, he led company-wide initiatives to build Apigee into a leading technology provider for digital business. Google (Apigee) is the cross-cloud API management platform that operates in a multi- and hybrid-cloud world. Chet successfully took Apigee public before the company was acquired by Google in 2016. Chet earned his B.S. in engineering from Arizona State University.