Why Data Needs Get Ignored - and What to Do About It

Business Person: “So, how’d we do?”

Data Person: “How’d we do … what?”

This is what happens when data is treated as an afterthought, only becoming urgent when it’s almost too late. This frustrating situation highlights a recurring issue that many of us have faced: data needs are often neglected until they become an emergency. Let’s take some time to understand the drivers and explore potential solutions.

The Issue

Imagine an important business event, such as a new product launch, a major marketing campaign, or a strategic pivot. Ideally, data requirements (and the resulting reporting, analysis, and optimization needs) should be anticipated and planned for ahead of the launch. This foresight ensures the business can reap all of the data-related benefits upon launch.

In theory this makes perfect sense, but practically speaking, it can be a challenge. In my own past experience and through discussions with peers, I have found that data needs are frequently overlooked. Finally, when someone asks, “So, how’d we do?,” there’s a mad scramble to gather data, putting undue pressure on leaders who need to understand and evaluate their launch.

One thing that I find especially concerning is that, when there’s a data-weak launch, people are prone to call the gaps in availability “data quality problems.” I do not think these gaps are data quality problems. Instead, I view them as business process problems related to requirements-gathering and prioritization.

Why This Happens

One major factor is the lack of integration between data teams and the broader business strategy. When data folks operate in isolation, addressing only ad hoc requests related to short-term needs, they miss opportunities to look ahead. They miss the chance to anticipate questions that business people will be asking - or should be asking - that are linked to upcoming strategic changes.

Another issue is the insufficient appreciation for the value of analytics. Business people may not fully grasp the importance of data or the effort it takes to meet launch-related data requirements. This lack of understanding often results from a focus on the immediate goal to “ship product” rather than on achieving meaningful business outcomes. If rewards are given simply to those who ship, it can lead to misaligned priorities.

Even when data teams are well-integrated and business leaders recognize the value of analytics, problems can still arise. Data teams may not be included early enough in the planning process, in turn compressing the time for data-related work. Alternatively, a data team’s staffing constraints might lead them to underfund a launch, either by allocating too few hours or by expecting too much from a junior person. These scenarios can undermine effective data planning, too.

What We Can Do

To address these issues, data teams must build strong connections within the business and gain a deep understanding of its operations. This means being proactive in aligning with business goals and anticipating how the data team can support these goals. Lean way in.

It’s essential to include data teams in major projects from the beginning, alongside other key teams such as Design and Legal. If there’s a launch playbook, make sure “Data” is listed there. Inclusion of the data team should be standard practice, and if it’s missing, vocally advocate for it. This may take a cultural shift.

Educating business leaders about the ongoing value of analytics is also critical. Don’t just show them once. Instead, make it a continuous cycle (read my article on how to do it here ). Also, try advocating for business goals that focus on performance rather than just product launches, and for hiring business people who raise the bar on data literacy.

Having executives who understand the value of analytics and are willing to allocate resources accordingly is key. If your sustained efforts don’t lead to improved integration and support, I’ll be bold and say that perhaps it’s time to move elsewhere. Life’s too short, you know?

What Success Looks Like

In an ideal scenario, the data team would be aware of major launches well in advance and be involved early in the planning stages. Business people would anticipate their data needs beyond the launch day, communicate their requirements clearly, and view any data gaps as prioritization decisions rather than mere “data quality problems.”

Business leaders would recognize the value of analytics and be willing to ensure appropriate allocation of resources. The data team would strike the right balance between addressing all data requirements and avoiding unnecessary resource burn for the sake of having everything “just in case.” Harmony would exist.

Conclusion

This is hard work, and it can be frustrating even in the best circumstances. However, being proactive can significantly improve the situation. If proactivity doesn’t pay off and you are left with a pattern of data needs getting ignored, go ahead, you have my permission to quit.

This issue has persisted for decades. Will it ever go away? Could we solve it with technology? Or, will we get over it and learn to live without? I think the truth is somewhere in between. Let’s revisit this topic a decade from now and see how things evolve.