Technology decisions and technical debt in data protection
In this video, two expert analysts discuss digital transformation, IT spending and technical debt, as well as how these topics fit into a data protection strategy.
There is a big change in the market that I've been observing recently in the space of data protection: More and more organizations are now focused more on economic value than any specific technology.
This change is happening against a backdrop of accelerated digital transformation, the expansion of multi-cloud strategies, and of course the constant quest for enhanced cyber-resilience. These areas are salient in a recent report from TechTarget's Enterprise Strategy Group, "2023 Technology Spending Intentions Survey."
I recently caught up with my colleague Nathan McAfee, a senior economic analyst. A lot of his time is spent with IT professionals, evaluating their experience of technology from an economic perspective. His engagements often cover why a customer would make a technology change, dimensions they measure, understanding the economic impact of that change and aligning their business and technical goals.
It's a fascinating topic that we cover in our video discussion. We also cover the key topic of technical debt, which I believe is changing the way IT leaders are -- or should be -- approaching and consuming technology. This is particularly true in the context of data protection and intelligent data management.
To provide more context, in our economic studies we've seen that up to about 40% of current budgets is being used to service short-term decisions or decisions made in the past -- that's technical debt.
It is also important to note that it poses a challenge for vendors today who are trying to reach these audiences and connect with the IT buyer. Vendors need to change the way they communicate the value of their platform: It's not just about feature functions, it's really about the business.
In our discussion, we double-click on many of these topics in the context of data protection strategies. We discuss the tradeoffs and the complexities, as well as how to get past these hurdles.
How do you break the status quo? How do you get from understanding what your technical debt is to breaking that inefficient model and moving forward? How can you get more out of your IT assets and, in time, more out of your data assets?
