We’ve reached a point where data utilization has become a standard across multiple industries. The information and insights gained from data collection and analysis are mandatory tools for modern businesses, but the construction processes required to achieve stable, accurate, and scalable data solutions remain difficult. Some businesses have latched on to self-service data systems, and while those methods can work, it’s an uphill battle wrought with common pitfalls and all too easy dead ends. The promise of self-service data implies that everything is in-house and customizable, but even if an organization designs a suitable data structure, the process still needs some critical components that are rarely easy to find internally. Having the goal of building a robust data utilization and analysis system is different from having the knowledge and capacity to DIY the solution.

Accessing Data and Data Analysis are Very Different

A man types on a laptop
The first foundational issue with entirely self-serviced data is the internal reliance needed to keep the system working. Keeping every data process internal relies on internal employees to wrangle the data, fix pipeline issues, and more. In a perfect world, employees would be more than willing and able to undertake the ask—but in reality, employees have enough on their plates without taking on the additional headache of data management and, in many cases, lack the expertise to do so. Data analysts, engineers, and specialists are a core part of healthy data management and usage; a completely self-service data strategy often omits roles that reduce the desired value and impact of the processes.

Mismatched Data Goals

Creating a self-service data model is often not the most challenging part of the data utilization process. Throwing together a framework and general process might get things going, but unless the data democratization plan includes considerations for end users, few people will be able to use the new data appropriately or efficiently. Top-down decisions often overlook the actual users and workflows that determine data viability. Not taking the time to create a holistic data plan ultimately creates data silos and unnecessary rework.

Unless your data democratization plan includes considerations for end users, few people will be able to use the new data appropriately or efficiently.

The Right People in the Right Places

People have a discussion around a computer
Self-service data has a role, but it relies on having the right people shepherding the proper functions so that the entire operation flows smoothly. Data fluency and common understanding are essential skills to establish across any organization. Include data enthusiasts already in your ranks and encourage interested employees to learn new analytics capabilities. Leave the door open for employees to embrace cross-training and collaboration. Touting multiple employees with an increased understanding of how to better consume and analyze data is always a beneficial business trait. Focusing on data awareness and appropriate training will empower teams to create documentation, optimal processes, and plans that leverage the organization’s strengths. The outcomes of self-service data with a robust and informed team running the show can produce fantastic results. However, without the proper time and investment to build and nurture team members, it’s far easier to fail than to stumble into success.

The Danger of Losing the Big Picture

When data analysis is only internal and self-regulated, there’s a real danger of making data-driven decisions that can lead you down the wrong path and end with wasted resources. Self-reliance in the data world sounds foolproof in theory, but in practice, it can quickly spoil your efforts. Solely internal data management and analysis often turns into a “can’t see the forest for the trees” issue. Self-service data without expert guidance can lead you down a thousand different paths, but you’ll have almost no way to tell which path is optimal. Charting your course, keeping your eye on the big picture, and using data to guide that journey will reduce team stress and increase team efficiency as everyone strives to meet the organization’s goals. The bottom line is that data structure, proper utilization, and experience-oriented analysis are worth getting right rather than paying the price for overconfidence.

When data analysis is only self-regulated, there’s a real danger of making decisions that lead to wasted resources.

The Value of an Expert Helping Hand

Colleagues meet in a boardroom
Running any organization or team is a multidimensional challenge. Keeping the wheels turning, achieving business goals, and managing staff is a lot to keep in order. Trying to build a self-service data solution along with everyday operations leaves room for error, which is when outside expertise becomes invaluable. Having an outsider offer an unbiased opinion will highlight things a business has overlooked. Outside expertise can help you address a host of unanswered or unidentified questions. Here are a few that are common when it comes to data and processes:

  • Do vital organizational questions still need answers?
  • Are there opportunities for richer insights through more cross-functional collaboration?
  • Are high-level leaders and managers aligned on the actions necessary to uplift a lagging key performance indicator (KPI)?
  • Is data methodology aimed at affirming current actions or improving the overall experience?

This is where a person or team with experience outside your internal ecosystem can be invaluable. Rather than building your entire data strategy alone, the best path is to leverage proven consulting agencies with the expertise to help you map out and implement the proper data infrastructure that will augment your core team strengths and goals.

Final Thoughts

At the end of the day, data-driven organizations don’t arrive at a best-in-class mile marker by accident; they arrive by adopting a curated set of practices and using the best method to get the desired result. Self-service data is a part of the equation but not the overall solution to the problem. The right mix is self-service data coupled with internal and external data and analytics expertise to make life easier and faster cultivate impact. To achieve your goals while avoiding common trappings, get in touch with us and leverage our experts to fill in the gaps and craft a sustainable data infrastructure tailored to your organization’s specific needs and desired outcomes.