Why Don’t We Make More Data-Driven Decisions?
As of this writing, Data.gov hosts 184,055 datasets. We have petabytes upon petabytes of data, yet struggle to find the best answers for common problems like employee engagement, program schedules, and budget forecasts. In a recent panel on data-driven government, public and private sector leaders alike agreed that to use our data effectively it’s imperative that organizations apply good data governance strategy and processes. While this is true (more on that in a future blog post), whether you make data-driven decisions is also driven by culture—ironically, a factor which, unless you have data on it, is unruly to address.
Three themes can help you identify where your team or organization is when it comes to making data-driven decisions. Where do you fit in?
Do you have an evidence-based culture? In Carl Anderson’s Creating a Data-Driven Organization, a hallmark of data-driven organizations he identifies is an evidence-based culture. Evidence-based cultures prioritize having data to support major decisions, open sharing of the rationale for decisions, and democratic distribution of information.
How mature are your analytics processes? In 2008, the SAS Institute parsed out eight levels of analytics maturity within an organization, which provides a good gauge for how habituated your organization is at making data-driven decisions. If you use data mostly to understand what is happening, to understand ad hoc issues, or to tabulate requirements, you’re likely on the low end of the spectrum for capabilities to enable data driven decision-making and build an evidence-based culture.
To promote evidence-based thinking and analytics maturity, OMB provides organizational incentives to departments that integrate evidence into their reports. This extends the benefits of evidence-based thinking beyond the inherent value to incentivize behavior changes that can build new habits.
Are you interacting with your data? Data is not information. That bears repeating: data is not information. Once you process the raw data, clean it, organize it, interpret it, and summarize it, leaving it in a data file is not sufficient for driving a data-driven culture. We are more likely to remember information and apply the data the more we interact with it—a concept known as disfluency. We need to work with data enough for it to challenge our assumptions, introduce new findings, and extract information it contains. Print it out, hang it up, view it in multiple formats and on more than one occasion, create a lab or workspace where people can interact with the data sets.
Data governance and effective data management establish a framework and infrastructure that support collaboration across data, but culture is important, too. To drive data-driven decision making in your organization, team, or even in your individual role, consider where you identify with these cultural indicators. Develop your “culture” infrastructure for data analysis along with the technical infrastructure.