As reporting and analytical tools continue to evolve, the push to empower end-users with these software capabilities becomes harder to argue against. With minimal training, individuals without technical or analytical backgrounds can now produce a variety of analyses and reports. Termed as "data democratisation", this approach appears to be a great solution in organisations with limited analytical resource or where existing analytical resource is haemorrhaging and becoming a bottleneck to decision making.
Yet, like many significant advancements, utilising these tools requires a cautionary note. Entrusting such power to the uninitiated can result in flawed analyses, misinterpretation and ultimately mis-directed decision making with potentially devastating consequences for the organisation. For individuals without a deep understanding of the underlying data's quality and intricacies, or lacking adequate numeracy skills, using the software can be akin to unleashing a bull in a china shop. So, how can we safeguard organisations against misguided decisions in such scenarios?
The answer is to implement an effective Data Fluency programme alongside any technical training rollout.
But, before we look into some key elements of a programme, let's start by defining the difference between Data Literacy and Data Fluency.
There isn't a universally agreed-upon definition, but in practical terms, someone who is data literate understands the origins and definitions of their organisation's or department's data and can interpret outputs (such as charts and reports) from technical teams. To draw an analogy, becoming data literate is akin to learning a foreign language: it involves studying textbooks, doing exercises, and engaging in basic conversations.
On the other hand, Data Fluency entails being completely comfortable discussing data and analytics and proficiently using software to generate required reports and analyses. In the same analogy, being Data Fluent is comparable to conversing comfortably with people from a country whose language you've studied.
While someone who is Data Fluent may not possess the same depth of understanding as an analyst, they comprehend how analytics can address challenges. For example, they may understand how a segmentation model could help the business differentiate its product offerings without necessarily knowing the intricacies to undertake the required work.
So, firstly, the organisation needs to establish whether they are looking to increase Data Fluency or Data Literacy to set the scope of any programme.
So how do you put a Data Literacy or Data Fluency programme together?
For any programme to be successful and deliver on engagement and uptake here are 5 key elements you should look to include:-
i) Involve the data/analytical community in both the development and implementation of the program.
This presents an ideal opportunity to foster closer relationships within the organisation; a crucial step for analytical teams seeking recognition as valued consultants rather than "number crunchers." Given their depth of expertise and enthusiasm, they can collaborate with other departments, positioning themselves as experts capable of offering valuable advice and guidance on various approaches. By pairing a professional trainer with an experienced analyst, organisations can ensure that the technical experts feel adequately supported throughout the process, preventing them from feeling overwhelmed.
ii) Keep the sessions practical and fun.
While data can be a dry subject, structuring the sessions as interactive workshops can enhance participant engagement and involvement. Incorporating real-world data, relevant examples and interactive exercises will create a learning environment where participants can relate directly to the material. This approach not only enriches the learning experience but also encourages and stimulates healthy discussion and debate among participants.
iii) Consider using on-demand resources for some elements of the programme.
For segments of the training that delve into more academic topics, such as data definitions, data structures, and intricate technical content, providing recorded sessions allows participants to revisit detailed information at their convenience. Since retaining the detail from a single workshop session can be challenging, having on-demand resources ensures that participants can review material in their own time. These recordings don't need to be professionally polished as long as the messages are clear.
iv) Develop the programme in stages, don’t try to tackle everything at once.
By gradually developing training initiatives, organisations can continuously review, adjust, and adapt the program based on feedback from facilitators and participants. This step-by-step approach allows for ongoing refinement and prevents overusing critical resources (e.g analysts, L&D personnel) over a short timeframe.
v) Secure buy-in from senior executives to ensure the programme's success.
Without endorsement from senior leaders, the initiative will be prone to encountering resistance from the teams they oversee. Competing priorities, including other initiatives and business-as-usual operations, may overshadow participation in the programme by team members. If overall support isn't possible, consider launching the programme within a department that fully supports the programme’s aims first.
Ultimately, a successful programme will equip both stakeholders and analysts with the collaborative skills necessary to optimise the organisation’s investment in its people and technology. This capability not only fuels but also empowers data-driven business decisions, laying the foundation for organisational success.
Steve Hulmes
Are you considering implementing an internal Data Fluency program? Sophic can help by providing assistance in the development and, if required, delivery of the programme. Steve Hulmes has extensive experience in developing and delivering analytical workshops for both technical and non-technical audiences. Just email steve@sophic.co.uk to schedule an initial discussion.
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