There is a widespread misconception that when new technology comes along, previous technology becomes obsolete.
This can be a costly fallacy.
Organizations needn’t continuously overturn their software stack — they need to evolve it. Keep the core intact and make incremental improvements. Strong foundations in data management never become obsolete: good documentation, consistent application of tools, and well-understood processes are always in style. Focus on these and be merciless in your assessment of new technology.
It doesn’t matter how slick the demo is. If new tech doesn’t add value and fit with what’s in place, don’t invest. The return will never materialize.
Do You Really Need New Technology?
Technology is advancing at a rapid pace, especially in the data and analytics sphere. What began as data warehouses in the 90s transformed into data lakes by the 2000s. Now there are lake houses, data landscapes, data stacks, pipelines, and a litany of new technology that grows by the day.
This complexity can be overwhelming. But underneath the jargon, business needs stay consistent. Business leaders want to know how the money flows, understand their customers, and monitor the competition. What has changed is the amount of information (more) and the speed at which it moves (faster).
The advent of distributed processing and cloud computing over the past few decades means data systems can now manage massive amounts of data at lightning speed. This is vital for some use cases. However, in other instances, real-time data feeds offer little value for a high cost. Most business operations work well with a retrospective view of data.
In those cases where you do need real-time feeds, be deliberate about the new technology. Pay attention to how it fits with what’s in place. Do you employ open-source software? Proprietary? What skills do you have on your team now? How do people interact with the data and how will that change?
There are many ways to solve for speed, volume, and any other practical requirements. The right approach is as distinct as your organization. The further a solution is from what’s in place, the higher the cost of adoption. Make improvements, not replacements.
Trying to do everything all at once won’t work either. Evolution is a step-by-step process. The Tyrannosaurus didn’t wake up as a chicken.
Constant, Controlled Evolution
An organization is never done with technology. There will be periods where the rate of change speeds or slows — but it should never stagnate. Keep moving and improving on what’s in place, just like the tortoise: slow and steady.
As an example, SEI recently helped a top 20 U.S. retailer turn around its data strategy. The focus was on improving the quality of data in place and building flexibility into the data architecture. In practical terms, this means the client was able to build state-of-the-art applications for its customers while making incremental improvements to existing technology.
This wasn’t an overhaul; it was a process (still in place) for improving the quality of data and creating a standard mechanism for accessing the data. The project was a major step forward for the client. Because the approach worked with the existing technology, the return was fast.
SEI always focuses on helping clients find value. This means finding the right pace of technology evolution. It is a fine balance — one where expertise and experience are paramount. SEI’s consultants have been working in this industry for decades, helping our clients transform their data technology intelligently and avoid costly investments in superfluous software.
