Five Steps to Boost Data Literacy Across Your Organisation
“So, how do I export this to Excel?”.
The question that any person involved in delivering a data solution dreads. We’ve all heard it. The story usually goes something like this.
An organisation spends six months and several hundred thousand pounds on a modern data platform. Data ingestion, modelling, and stunning dashboards. It is all there. A year later though, usage is minimal, users are creating spreadmarts in Excel, or worse, they are still making decisions based on gut feel.
There are so many reasons why a data platform initiative can fail, but in this article we’ll explore one of the key reasons and what organisations can do to mitigate this.
You see, often the big investment is in technology, and not in people’s ability to understand, question, and use the data presented to them. Data literacy is the bridge between data investment and business value. It is the organisation equivalent of financial literacy - a language that everyone needs to speak to be effective.
In the rest of this article we’ll cover five steps that organisations can take to build that bridge.
Step 1: Define What Literacy Means for You.
Data literacy, like a data platform, is not a one-size-fits-all outcome. Providing company-wide, generic training is not going to work. You need to start by defining role-specific personas, for example what does a “data literate” account manager need to be able to achieve versus a supply chain analyst or HR business partner?
Use these personas to support your learning needs analysis. This will provide insight into the outcomes people are trying to achieve, what challenges they currently face, which skills they have, and which skills they need.
This makes any new skill relevant and immediately applicable and, very importantly, answers the question of “what’s in it for me?”
Step 2: Create a Common Language
Not having an aligned view on key business terms and metrics is a silent killer when it comes to adoption. Just ask any person in any organisation “how many customers do you have?”.
Organisations need to develop a shared and accessible business glossary. This should be a shared effort across the organisation, but led and owned by business teams. Define important terms like “customer”, “margin”, and “efficiency” and then strictly govern these definitions. Ideally a business glossary would be in place before embarking on any data initiative, but the data initiative can be an opportune vehicle to create a business glossary.
This common language builds consistency and trust and does away with meeting room arguments about whose numbers are right.
Step 3: Make Learning Practical and Pervasive
Attending a single training course doesn't create lasting skills. Learning needs to be continuous and integrated into the organisation’s way of working.
Training needs to extend beyond watching a few generic online videos. Training teams, delivery teams, and business teams can run show-and-tells, sharing how they use data to solve problems. Data coaches (usually in the form of analysts) can be embedded within business teams to provide support. Persona-specific workshops can be run as hands-on sessions using the tools and data that people are expected to use every day.
This approach builds a culture of continuous improvement and shared learning and makes the acquisition of data skills a habit rather than a once-off event.
Step 4: Champion and Celebrate Success
A successful change in culture needs visible champions which leads to positive reinforcement.
Early on in your data journey the organisation needs to identify and empower data champions. These should be enthusiastic colleagues at all levels in the organisation. This approach can be particularly successful when these individuals are well-respected (note: this has nothing to do with seniority in the business).
When a team achieves a successful outcome, for example, improving a process or spotting a new opportunity, celebrate this publicly. The earlier on in the data journey, the better. Don’t leave this to chance, plan for it up front as part of your change management plan.
Public recognition makes data fluency an aspirational capability. “I want some of what they've got!”
Step 5: Provide the Right Tools and Space to Experiment
People cannot become data literate without getting their hands on both the data (in a business-friendly format) and some user-friendly tools.
Self-service reporting, analytics, and visualisation tools need to support the personas you identified in step 1. Try to step away from vendor hype so that you can choose the tools and technologies that will best support the organisation’s use cases and people.
Most importantly though, provide the space to experiment. Something like a “sandbox” - a safe space with non-critical data where people can explore and experiment without the fear of breaking something.
This will help people get started and encourage curiosity and experimentation, both of which are key to a data-driven culture.
Conclusion
Building a data literate organisation is not about turning everyone into a data scientist. It is about helping every person in the organisation move from simply looking at data, to asking critical questions. This then leads to challenging assumptions and making more informed decisions.
How do you know when you are successful? When the conversation moves from “what does this data say?” to “What does this mean for the business?”.