We hear a lot about bad data—information that is full of errors, omissions, and inconsistencies. What doesn’t get talked about enough is underwhelming data—information that fails to produce the insights and actionable plans decision makers are looking for.
Underwhelming data is a bigger problem than many realize. Consider, for instance, that in a recent Forbes survey, only 13 percent of respondents considered themselves “leaders” in terms of data utilization. Everyone else felt they were lagging behind in terms of collecting and leveraging insights.
This problem is so common because collecting data is relatively easy, whereas organizing and analyzing it is difficult. Underwhelming data contains lots of actionable insights, just not in a format that makes them obvious or accessible.
In the coming months, we will be writing extensively about unlocking the value of data. Step one is always breaking down data silos, the root cause of underwhelming data. Silos happen when data is segregated into folders and programs instead of being integrated onto the same platform. In these cases, decision makers don’t have actionable insights at their disposal because data is incomplete by design. So instead of a single source of truth, they’re working with half-truths.
Data silos develop accidentally and imperceptibly. Therefore, it takes a proactive effort to break them down, integrate the contents, and begin collecting data holistically. Once that happens, insights become more actionable because they’re reliably accurate and relevant. Follow this framework to make data a collective resource:
- Identify where and why silos exist – Silos, by nature, tend to get overlooked or even forgotten completely, excluding important data from the broader business picture. Make a systematic effort to locate silos across tech tools, workflows, and departments. Along the way, examine why and how each silo exists so that it can be dismantled without affecting data or projects.
- Integrate data through automation – Manually integrating data on an ongoing basis (or even once) is too labor-intensive and error-prone to be an option. Automated data integration makes the process effortless while ensuring it’s as comprehensive as possible. Once technology is handling collection and organization, new data is automatically integrated into the platform so that silos aren’t rebuilt.
- Redefine processes and policies – Data ends up in silos because departments can be competitive, or sometimes because it’s the easiest option. Breaking down silos requires a redefinition of how companies store, use, and share data. New policies should acknowledge the need for security while encouraging data sharing as widely as possible.
- Focus on accessibility and UX – Integrated data needs to be accessible and explorable; otherwise, it’s not much better than siloed data. Users should be able to find the precise facts/figures they need with minimal time and effort. Alternatively, they should be able to drill into data to find the numbers behind key metrics. In future posts, we will talk more about optimizing integrated data.
- Cultivate a collaborative culture – Even with technology in place and updated policies, it’s up to users to prevent data from ending up in silos. People must believe in the effort to integrate data as widely as possible if it’s going to be successful. Begin building a culture that values collaboration, communication, and cohesiveness. If employees see data silos as obstacles to their own success, they will become proactive about integrating information.
Data silos are self-imposed blinders that obscure a company’s true strengths and weaknesses. Companies don’t automatically improve once the silos are gone, but they do gain insights to engineer those improvements. For that exact reason, breaking down silos is a prerequisite for leveraging data effectively.
Our team at insightsoftware understands what underwhelming data looks like. And we’ve developed simple tools that provide comprehensive solutions. Contact us to begin breaking down silos and unleashing insights.