The Final Phase of Digital Transformation Focuses on Data

I have always loved data. From my days in a Network Computing test lab, collecting test data to analyze the performance of everything from routers to web servers, to my days in the F5 Office of the CTO analyzing survey data from around the globe, I've always enjoyed digging in and looking at the industry through the lens of data.

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nets me a lot of data to look at it, in a lot of different ways. One of the primary ways I look at that data is through the phases of digital transformation. As organizations dive deep into each of the three primary phases, they tend to focus on different technologies. Of late, most organizations have been in the first two phases, and merely dabbling in the final phase in which AI and data reign supreme.

You will not be any more surprised than I was to learn that over the past year, thanks to the arrival of generative AI, a significant percentage of organizations have marched squarely into that final phase—and they aren’t leaving any time soon.

I say that confidently because AI-assisted business requires some technology support. AI, of course, but that relies on a robust foundation of data and analytics. And to be frank, research from just about everyone uncovers the same refrain with respect to data: organizations aren’t ready for AI just yet. Not really.

That’s because merely generating data isn’t enough. Merely storing the data isn’t enough. Mired in all that data are questions about quality, about management, about governance.

Questions that lead to the adoption of operational practices focused on data. Those practices are already being referred to as DataOps or MLOps. Like DevOps before it, the name is less important than what it implies: best practices around the handling, storage, security, and management of data that lead to a level of quality sufficient to support both predictive and generative AI.

You might recall that decade of DevOps, where CI/CD pipelines emerged, matured, and became the status quo. We are going to see a similar movement in the market that focuses on data pipelines, with all the tools and technologies and blogs and experts to go with it.

And it’s necessary. Because too many organizations in our latest research

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56%

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tagged data immaturity as the number one challenge standing in the way of AI adoption. And we aren’t the only ones to notice. AWS, in its Data CDO Agenda 2024, found that “more than half (57 percent) had not yet made necessary changes to their company’s data strategies to support generative AI, but a majority (93 percent) of CDOs agreed that data strategy is crucial for getting value out of generative AI.”

And when we dug a little deeper into data strategies, we found that 47% of organizations had yet to define a data strategy specific to supporting AI.

A final word on digital transformation

Look, modernizing your data practices isn’t any easier than modernizing your infrastructure. After all, most of the IT stack is built upon a data foundation. Every technology and component in an enterprise architecture revolves around storing, securing, transferring, and analyzing data. That's all it ever was, whether it's bits on a wire, in a database, or on a file server. That’s what APIs are about: apps sharing data. That’s what multicloud networking is about: secure transferring data. That’s what AI is about: harnessing the power of data.

But while businesses have long understood the value of customer and corporate data, it is AI that illuminates the value of all data

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operational, corporate, and personal. But that value does not come without ways to extract it, and that requires modernization of the data domain to include new practices, tools, and technologies. That’s the technology focus of the third phase of digital transformation.

Organizations that fail to recognize the importance of the practices, tools, and technologies that will support, scale, and secure data will struggle to realize the value that AI

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whether predictive or generative

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will bring to those who do.

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