When leaders consider the moves their enterprises need to make to become data-driven, the first thought is usually infrastructure. This makes sense, as analytics architecture and governance play a leading role in the success of data initiatives. People can only start mining data insights if they have access to the tools they need — and those tools depend on having the back-end infrastructure squared away. There’s also the matter of culture to consider, like how leaders will communicate the importance of making data-driven decisions.
But one aspect of moving toward data maturity oftentimes gets lost in the shuffle or underestimated by leaders setting the business intelligence (BI) analytics strategy: data fluency.
In fact, the experts at Gartner predicted half of organizations would not have the data and AI literacy skills to realize business value by 2020. The firm likens a lack of data literacy/fluency to different teams speaking completely different languages and struggling to hold a fluent conversation. Unless everyone throughout a firm “speaks data,” it’s very difficult to collaborate, share insights and drive better business outcomes.
Here are four tips for providing effective employee data fluency training across your workforce.
A simple — but oft-overlooked — effort is having executives model the behavior they want teams to emulate. In other words, ensure leaders lead by example in their words and actions. This sets the tone for culture and encourages employees to take part.
A basic survey can go a long way toward helping your organization understand existing fluency gaps, barriers and frustrations. Start by asking employees their comfort level with tasks like analyzing data, explaining their findings, making data-driven decisions and communicating with other teams about data-related issues. It’s immensely helpful to get this kind of feedback straight “from the horse’s mouth” rather than acting on assumptions.
It’s common for various teams throughout an organization to have different fluency levels and areas of expertise before training. This amplifies the need to provide relevant, context-based training on getting the most from data analytics software — rather than a one-size-fits-all education. Employees tend to get more value out of examples and lessons tailored to their actual work, plus the lessons will meet them where they already are in terms of data fluency.
One way to do this is by establishing an “academy” to serve as a hub for learning role-specific skills. That is, employees are able to access materials and lessons relevant to their usage of data. As Harvard Business Review notes, taking a more personalized approach tends to ensure the messaging is helpful and relevant, especially compared to a general-purpose learning system that tries to encompass all users at the same time.
A recent Harvard Business Review study sponsored by ThoughtSpot also noted the importance of “engaging all parts of the organization” in data fluency training, including leaders, middle managers and frontline workers. In other words, make sure the trainers themselves have the training they need to facilitate effective training and set a strong example.
How many times throughout history have people mistaken correlation for causation? Hint: Many, many times. Being able to mine data insights is a necessary foundation, but having the confidence to analyze, question and interpret findings requires more than access to tools; it requires literacy and fluency training.
Ensure employees can trace data insights back to the source, an action that’s important for building users’ trust of reporting validity and helping users make sure they are looking at the relevant big picture for the situation at hand.
Just like enterprises seek to measure the return on investment of their overall data initiatives, it’s also highly important to measure the impact of fluency efforts. These metrics will be the ones closely tied to overall business outcomes — like measuring user adoption rates by percentage before, during and after the first wave of data fluency training to see the program’s impact on getting employees to utilize the BI tools at their disposal. It’s also important to look at how users are utilizing analytics software before and after fluency training.
Effective employee data fluency training can have a substantial impact on the business value enterprises are able to attain through AI and analytics — so prioritize it alongside tools and culture using these tips.
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