Data modernization should not begin with cleaning data for its own sake. It should begin with business outcomes, measurable value, and focused use cases that prepare your organization for AI.
Why Traditional Data Modernization Projects Stall
Raise your hand if your last big data project actually delivered the business value it promised… Yeah, that’s what I thought.
Here we are again, with another resurgence of data readiness projects, this time fueled by the rush to get our organizations AI-ready. Even seemingly advanced technologies like AI follow the “garbage in, garbage out” rule, demanding cleansed and structured data to be effective. So, before we charge headlong into yet another daunting data project, let’s pause and make sure that this time, we do it right.
We need to rethink how we take on large data modernization efforts. History has seen IT departments being charged with getting data in order under any number of ambitious programs (“Big Data”, “Data in the Cloud”, “Master Data Management”) and watched those programs stall out. The reasons are familiar. They are costly. They take a long time. They consume a lot of resources. But the biggest reason, the one we keep glossing over, is that these programs focus too much on the data itself and not enough on adding business value.
The Problem With Cleaning Data First
Prior efforts went something like this. A data governance council was formed. The group gathered and picked a data set that needed cleansing. A team was assigned to clean the data. The data was cleaned and stored. The team moved on to the next data set. Rinse, repeat. Eventually, the stakeholders writing the checks asked the obvious question: What are we getting for all this? The data program manager would point to the number of data elements cleaned and tables populated. But reports? Not updated. Automations? Still on the backlog. The promised business value? Absent. Cleansed data, on its own, was not enough. It’s time to flip that equation on its head if we want our data projects to succeed.
What Is Business-First Data Modernization?
A Business Outcomes/Value-Add Approach to Data Modernization is what we need here. There are five steps to follow:
A Five-Step Approach To Data Modernization Outcomes
1. Set up a Continuous Improvement Program: Yup, we still need a program, even with this new approach. The program provides the funding. The program prioritizes the work, and most importantly, the program provides accountability for the desired outcomes. At a minimum, you need a program manager (initially full-time, eventually part-time) and a council. Keep the council to under 10 cross-functional participants who have a stake in the outcomes (meaning their business processes will be improved). Rotate participants over time. It keeps the energy high and the perspectives fresh.
2. Identify Areas of Improvement: This is the most important step. The program needs to elicit use cases from the business, but please, don’t ask “does your data need cleaning?” or “would you like to improve your data?” The answer will invariably be “yes”…but to what end? Instead, ask about business outcomes: efficiency, cost reduction, sharper insights, better quality. Every department, function, and team has these needs. Anchor your efforts there, not in “clean data” for its own sake.
3. Prioritize the Work: This is part art, part science. Evaluate each use case on team readiness, feasibility, effort, and measurable value. If something looks too daunting, break it into parts. Ideally, each effort can be tackled in a matter of weeks, a few months at most. If it takes longer than that, you’re sliding right back into the old model.
4. Identify the Data Platform: You still need a centralized home for the data you’re cleansing. Cloud or on premises (or both)? What does compliance demand? What expertise do you have, and what would you need to hire? What is the near-term and long-term cost? Are you implementing a medallion architecture? (Hint: the answer is most likely a resounding “yes”.) Pick carefully. You don’t want to undertake a costly migration anytime soon, so be comfortable with the platform you choose.
5. Cycle Through Projects: Deploy real project teams against the prioritized use cases. These are full-fledged projects, not side initiatives. Business analysts are to document the processes. Engineers to design the technical solution. Data stewards (these are business resources, not IT) to help shape how the data gets cleansed and structured. Organizational change management to bring the people along, and a PM to coordinate it all. Each project cleanses the data tied to the business problem it is solving and feeds that data into the centralized platform. In the end, a process is improved, business value is realized, AND data is cleansed and centralized along the way. Celebrate the win. Tell the story. Repeat!
What Is Business-First Data Modernization?
Business-first data modernization means starting with measurable business outcomes, then modernizing only the data needed to support those outcomes. Instead of cleaning data in isolation, this approach ties every initiative to improved processes, better insights, cost reduction, automation, AI readiness, or another clear business result.
Why Some Rework Is Worth The Tradeoff
Yes, I know, this all sounds so simple on paper. I have spent several years as a Data Governance Program Manager and have first-hand experience with the hurdles that long-term data programs bring to the table. Hence, my desire to not repeat the same challenges and to try a different approach.
I will also acknowledge that this business-first approach will create some data architecture rework over time. A later project might very well need data analysts to adjust existing structures to accommodate new data elements.
If the prior project did its due diligence and identified ancillary impacts, the rework will be known and expected. Either way, that rework is an acceptable debt to pay, because we have already produced tangible and timely value from the earlier efforts.
Build An AI-Ready Data Modernization Program
Are you about to embark on your next large data initiative? Do you want to deliver a series of successful outcomes this time? Here at eGroup, our engineers can help you build and execute all five steps of a healthy data modernization program. Whether you need to set up a governance council, choose and deploy a data platform, manage the change, or securely migrate your data, we are here to help.
Read more about our data migration and modernization efforts here and reach out to start the conversation with one of our experts. AI is waiting and hungry for modern data…feed it!
Modernize Data With Purpose
Build a business-first data modernization plan that connects migration, governance, platform strategy, and AI readiness to measurable outcomes.
