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11 Ideas To Improve Data Literacy

11 Ideas To Improve Data Literacy

Data literacy is the cornerstone of all data democratisation. If your business is looking to adopt a data-driven mindset, then half the battle is getting the right data, and the other half is ensuring people use it the right way. 

It sounds simple, but is an area a lot of businesses are struggling with. Time and effort goes in to creating dashboards and reports that people simply don’t use. This could be because they find the visualisation tool too difficult, have set ways of working or are simply not talking the same language.

Here are 11 ideas to improve data literacy across your organisation:

Internal Data Wiki – All of your data resources need some place to live. A Data Wiki or Knowledge Base is a great place to start in making data easily accessible across your organisation. 

Data Dictionary – Get everyone singing from the same hymn sheet. A data dictionary captures and defines KPIs across all business functions. It outlines the business and technical definitions for each metric along with other useful information. The next time someone asks “what’s the difference between a qualified lead and a marketing qualified lead?” point them in the direction of your data dictionary!

Data Literacy Assessments – How can you gauge your efforts around data literacy  if you don’t first set a benchmark? Consider the current usage of analytics tools, application in current processes, level of critical thinking, a count of people with relevant qualifications. Reassess every year to see your progress.

Regular Data Updates – It can be difficult to keep track with the rapid pace of change in the data industry, but giving your team a general overview of new trends and technologies can be helpful when it comes time for you to consider adopting. If people are at least somewhat aware of what Machine Learning is for example, then  you can better frame the conversation when you want to introduce an ML project. This can also help prevent conversations like “this AI thing seems cool, can we do that?”

Introductory Training – The more people in your organisation who can understand at a top level, what a typical ETL process is and how data goes from source to dashboard, the better. It might seem unnecessary but it can help them make more informed requests to the analytics team and understand why certain activities (like data modelling) take longer than others.

Training On Visualisation And Storytelling – Analysis and report-building shouldn’t solely be the remit of your analytics team. So try and provide some training on best-practices around making charts and graphs easy to interpret. What sort of analysis should you undertake for the problem you are looking to solve? How can you relate data to real customer stories and examples? What are the right questions to ask? How can you use your toolset to accomplish this?

Certification And Courses – Above and beyond internal training, you might consider offering full online courses. Many tool providers have their own certificates and there’s never any harm with your team becoming more familiar with them. While there’s a lot of value that can be gained from online courses, we always advise that the best learning is with real code in the real world and this needs to be a consideration if you want to offer additional training. How can you then help that person to take what they’ve learnt and apply it practically?

Data Champions – Individuals who act as data advocates within their respective teams, and contribute to spreading data awareness and driving adoption. These champions serve as points of contact for data-related queries, facilitate training sessions, and promote a data-driven culture. They are your front-line in helping to encourage data literacy across your organisation.

Knowledge Sharing – Schedule regular meetings where teams can share their data-related insights and learnings. Get end users to share their particular tips and tricks with each other, troubleshoot common problems as a group. Peer-to-peer learning can be really helpful and stops all of these requests coming directly to your data teams.

Data Challenges/Hackathons – Organise competitions, set challenges, try and find fun ways in which your internal teams can use their data tools and skills. It might be a better way to benchmark the current level of proficiency in your team. Where people might be hesitant to change their ways of working, showing them some of the more interesting features in your tools can help to engage them. 

Guest Speakers – Invite data experts or guest speakers to share their experiences and insights with the team. Like, I don’t know, a plucky data agency based in London? *cough 173tech*

In Conclusion 

We hope this has given you a few ideas on how you can encourage greater democratisation and enhanced literacy across your organisation. If you’re struggling to benchmark your maturity today and need an external viewpoint, why not get in touch with 173tech today?


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