4 Potential Causes of Low BI and Analytics Adoption Rates –
Anyone who has ever owned a piece of exercise equipment knows there’s a huge difference between having it and, well, actually using it. The same principle applies to business intelligence (BI) and data analytics at an enterprise level. Deploying some form of analytics software is a strong start, but it’s wishful thinking to believe employees will harness this tech just by virtue of it being there.
As the experts at Gartner note, it’s not uncommon for organizations to see less than one-third of employees (32 percent) adopting BI and analytics tools. This number reveals the struggle many enterprises are experiencing with getting “the masses” to take advantage of BI and analytics rather than just power users like data analysts.
Identifying potential causes of low BI and analytics adoption rates is the first step toward addressing them — so, here are four major roadblocks worth considering.
Lack of Accessible Self-Service Tools at Scale
The first challenge has to do with the analytics and BI tools themselves — their accessibility, scalability, user friendliness and speed. Legacy tools tend to offer a choppy experience for employees, often requiring intervention from data and IT teams to access siloed data.
Any extra hurdles standing between the “average” employee and data insights will complicate and discourage the use of these tools. Consider the difference between someone being able to ask questions and receive answers already formatted as interactive best-fit charts — on-premises or on the go via mobile analytics — vs. someone having to request reports through the data team because they lack direct access to data.
Legacy tools provide limited access and restricted usability, which means companies are leaving valuable data insights on the table. Boosting adoption requires a push for self-service analytics tools capable of scaling up to handle thousands of users’ queries on a daily basis — and providing results in convenient, accessible formats without delay.
Lack of Executive Buy-In
Lack of executive buy-in can hamper analytics initiatives from day one. A recent collaboration between ThoughtSpot and Harvard Business Review found that empowering front-line employees starts with support from the top. Over 40 percent of companies believe it’s the responsibility of senior management to make sure employees have the “tools, training and support they need” to achieve success.
Multiple Versions of the Truth Sow Employee Mistrust
Another hindrance to widespread adoption can occur when different users end up with conflicting versions of the truth. Simply put: If employees don’t trust the data available to them, they’re much less likely to turn to it while making decisions.
Many organizations make the mistake of trying to cobble together disparate analytics systems — which in turn fuels siloes and the opportunity for multiple versions of the truth to exist.
In order for users to trust data, it needs to be reliable and valid. Establishing a unified platform that brings together multiple data sources to present one version of the truth is key. Allowing users to trace data insights lineage back to its sources is another way to boost transparency and trust — and spur adoption.
Lack of Data-Driven Culture to Support Adoption
Employees’ reluctance to adopt analytics and BI into their routine decision-making processes is oftentimes a product of company culture — which can help or hurt efforts, depending on its nature.
Here’s an example: Corporate culture may inadvertently foster a divide between IT and everyone else, so everyday users continue to believe BI reporting is outside their realm of responsibility. As a result, they’re less apt to proactively seek answers themselves, relying instead on the traditional model of requesting the IT team handle the querying of data.
Another potential cultural shortcoming is leaders talking the talk — i.e. talking up the importance of becoming data-driven — without walking the walk, which involves making analytics the cornerstone of their decision-making processes. Employees can absolutely tell when a leader is paying lip service to an idea vs. when they’re actually implementing it. Team leaders need to go all in on championing modern data analytics, publicly and privately.
It’s also helpful to engage employees by showing them the possibilities advanced BI and analytics hold — as well as how their jobs can become easier by updating their approach.
Is your enterprise experiencing lower BI and analytics adoption rates than it would like? Invest time, resources and energy into eliminating hurdles like these so more employees will get on board.