It is important to remain aware of the fact that making data work for you is as much about people as it is about technology. Dataspace has learned a lot about end-user engagement with predictive software and business intelligence infrastructures over the past two decades. After all, it is not the products but the user of these products, the guy or gal who makes the day-to-day decisions, which ultimately determines whether or not your organization uses data effectively.
Create a culture that celebrates data exploration
Unlocking the value of analytics lies in turning insights into action, and that responsibility ultimately falls on the end-user. Oftentimes, this means that skill development is required to make sure reporting end-users don’t have to wait for IT to weed out problems, define the available data, or answer questions about the platform. More importantly, you must foster a data-driven culture to encourage users to make data an integral part of their jobs and to discourage the typical reluctance that comes with adopting new technology. In most companies transitioning towards a data-driven strategy, surveys show that adoption rates hover only around 22% (Gartner, 2013). This resistance can mean valuable insights never translate into action.
What does a data-driven company culture look like?
Strong, data-driven cultures:
- Value and reward employees making data-supported contributions in meetings
- Recognize employees who make the effort to use technology investments by tracking usage information
- Follow suit; if management isn’t using data, nobody else will feel that they have to, either
- Minimize the role of strict processes to incentivize employee exploration and innovation
When Excel is all you know
The main responsibility of the end-users is to understand and address the business problems at hand, not to design data warehouses or carry out the other responsibilities of the technical team. That is not to say that they aren’t intelligent and capable – it simply means it’s not their place in the organization.
So when these users have a problem, they normally resort to the tools they have the technical aptitude to use: Excel and Access. In the absence of well-structured, integrated and complete data stores and analytic tools, these workarounds may not be the best solution. But when it comes to developing citizen data scientists among your employees, Excel should not be underestimated as an incredibly powerful tool. With Excel, you don’t need to know any programming languages to analyze data; regression, pivot tables, what-if analyses, A/B analysis and many other statistical and analytical tools are right at your employees’ fingertips.
Do you wish you had more employees making data-supported decisions? Drop us a line and let us know where you need help.