April 21, 2020

Being a CIO is tough.

Amid the myriad ways this is true, keeping up with the continually shifting constellation of buzzwords is one of the most trivial, yet frustrating challenges that you face.

Yet, you never know which buzzword may catch the eye of a member of your board or executive team, so keep up, you must.

But against this backdrop of annoyingness, a new buzzword has emerged that is actually worthy of your attention: DataOps.

In fact, I’d argue that you need to do more than merely pay attention to this new buzzword. I’d go as far as to say that DataOps has now become a CIO mandate.

The fact is that we now live in a data-driven world. But the enterprise technology stack — and collaboration model — is not set up for this reality.

Data is now an essential business driver. It will be the primary driver of competitive differentiation for the digital era enterprise.

That means that you must build a critical capability that enables you to rapidly identify new uses of data to enhance the customer experience and improve operational efficiency, develop and adapt data pipelines to meet these new business use cases, and deliver those data-derived insights into production.

And that is the essence of DataOps.

The Challenges with Current Data Management Approaches

It’s not just that data has become a business-critical driver, however, that is bringing DataOps to the top of your priority heap. It’s that the current approaches to data management just won’t cut it in a world moving at warp speed.

Let’s face it, the technologies and approaches that most enterprises employ when it comes to data management were built for another time and place in which everything moved much more slowly, methodically, and in greater isolation.

Today, the opposite is true.

The result is a general lack of communication, collaboration, and integration between data managers and data consumers. This gap slows everything down.

More importantly, as data becomes ever-more-critical, business users (and data consumers in general) are taking greater responsibility in its application. That means that you must find a way to connect data requirements with the underlying and necessary technical data assets much more quickly. But today’s approaches break down under this pressure.

The lack of a systemic collaboration capability inhibits your ability to scale.

And, as if all that weren’t enough, the number of players on the field is growing exponentially.

The diversity of the quantity and types of data in play, and in the people trying to get access to it, is making the situation untenable.

Your current approaches don’t stand a chance.

The DataOps Mandate

DataOps, while still nascent, is a management philosophy that can help you overcome these challenges and enable your data management practices to keep up with modern demands.

At its core, it is about bringing data managers and data consumers together into a collaborative practice to build, deliver, and adapt data pipelines that can rapidly deliver business value to the organization.

In its best form, it’s a process that simultaneously frees both everyone to focus on the parts where they add the most value — and step aside from those parts where they don’t, but while staying involved throughout the process.

Like DevOps, its development-focused cousin, DataOps must first be an organizational management philosophy. But realizing its vision and potential will demand a technology architecture built to support it.

As a result, a DataOps tooling stack, such as Unravel, will enable and support collaborative work practices, but will also allow the rapid assembly, management, and adaptation of data pipelines across the modern hybrid IT data architecture.

This need to transform both the work model and the tooling is another reason that DataOps has become a CIO mandate: there are just too many stakeholders for this level of change to be driven from any other place in the organization.

But just in case that isn’t enough justification for you, I’ll give you one more thing to consider.

The increasing expectations that organizations leverage artificial intelligence (AI) to deliver insights in real-time and at the point of engagement will raise the stakes even further. And while AI increases the stakes and expectations, it is simultaneously increasing the complexity and difficulty in delivering just this type of capability.

There will just be no other way to meet the rising demands placed on the enterprise data infrastructure. DataOps will be the way forward.

The Intellyx Take: It All Comes Down to Speed and Agility

While most buzzwords are all about hyping some new technology, every now and then, we get one that speaks to a much broader shift in the way organizations function.

DataOps is one of those.

While adopting tooling built to support its needs is critical, DataOps is not primarily about technology. Nor is it even just about creating better ways to organize teams and improve communication and collaboration around data.

In the end, it’s all about organizational speed and agility.

Adopted effectively, DataOps will give the enterprise business teams the freedom to explore and innovate with data — while enabling technical teams to ensure proper data governance, accuracy, consistency, and auditability.

Most importantly, it allows the enterprise to do all of this at scale and velocity, ensuring that the organization can adapt and change as business needs demand.

Achieving this type of adoption, however, demands that organizations take a holistic view of both the challenges and the solution. And this is where many current conversations around DataOps miss the mark.

Adopting DataOps must be about more than just streamlining data pipelines or implementing some new tooling. Instead, it must be about transforming the enterprise’s data management capability so that it can consistently leverage its data and infuse it at the point of customer and employee engagement.

That is the real promise of DataOps. And it’s something that the enterprise will only achieve if the CIO is leading the charge.