Becoming data-driven is a major undertaking for any business — one that requires the right technology and expertise, as well as getting the “people” part of the equation right too. Many organizations find themselves continually falling short of the goal to become data driven. According to a Harvard Business Review survey, 69 percent of responding companies say they’ve failed in their endeavors to create a data-driven organization.
The takeaway? It is absolutely possible — and advisable — to harness the power of data-driven decision-making. But there are barriers to clear along the way that have caused many well-intentioned businesses to stumble. Here are four examples.
Barrier #1: Analytics Adoption Staying at the Top
The first factor to consider is who has access to data and, more importantly, who is actually adopting data analytics into their routine decision-making processes. It’s important for the C-suite to embrace analytics tools rather than relying on intuition and past performance alone. But there’s a problem if analytics adoption stays at the top, failing to trickle down throughout every subsequent level of the organization.
Impactful data-driven decision making can, and must, come from every level. When the benefits of analytics are confined to boardrooms, organizations miss out on the transformative power of analytics — improved operational efficiency, new revenue opportunities, reduced waste, improved campaign performance, etc.
Becoming data-driven is just as much a cultural shift as it is a technology challenge. It requires commitment from the C-suite and bottom-up initiatives to “weave… analytics into the fabric” of organizations, as experts write for Forbes.
Barrier #2: Struggling with Siloed Data
Data accessibility and time to insight are also hurdles to clear on the path to becoming data-driven. And, traditional data siloes hinder both. When data is gate kept by specialized teams, rather than made accessible to front-line users, the amount of time between when questions are asked and when data insights are delivered is increased.
Data siloing also creates an environment for disparate information to thrive. Case in point: One data scientist discovered while working with General Mills that some data sources spelled out “Cheerios” in full while others referred to it as “chrs” — a perfect example of how even simple contradictions supported by siloes can affect reporting and lead to multiple versions of the truth.
Better BI reporting is available today using advanced, full-stack architecture. This approach de-silos data and connects non-specialized users directly with ad hoc insights, meaning there’s no longer a need to wait for reports. Advanced analytics solutions also facilitate a single version of the truth rather than allowing different versions to exist because of standalone silos. This ensures all users throughout a company are on the same page at all times.
Barrier #3: Underestimating the Need for Data Fluency
Designing a data strategy around the right tools is key. But people aren’t born knowing everything there is to know about using analytics — not to mention communicating with colleagues across the org about insights.
This underscores the need for companies to prioritize data fluency. While “data stewards and architects” may already be fluent in “speaking data,” some departments will need specific data literacy training to feel comfortable interpreting data and talking about it. Tie this training to job-specific use cases for the best results. Create a common lexicon to which employees can refer. Help all users — from executives to marketers to HR specialists and more — learn the language of data and how to work with it confidently.
Barrier #4: Treating Analytics as a Science Experiment
The best data analytics efforts are business driven from day one, meaning they align with specific performance objectives. Deploying analytics tech and even asking employees to use them is not enough of a motivator to bolster meaningful adoption; it’s more powerful to connect analytics to tangible goals and aspects of performance at every turn. This will encourage employees to harness these tools throughout their decision-making processes.
Overcoming these barriers to data-driven decision making will allow organizations to boost their analytics ROI and drive better business outcomes. It’s key to consider the human part of the equation just as heavily, if not more so, as the tech side.
