By Jordan Morrow, Global Head of Data Literacy, Qlik
Adapted from his presentation at the Southern Africa Qlik Summit 2018
It is essential that modern businesses implement an organisation wide data literacy initiative. This must comprise a framework that everyone can access. The first step to developing this framework is establishing a roadmap.
The Data Literacy Roadmap
- Must be iterative – it’s up to you to try and fail (and find ways to succeed); new software is emerging all the time.
- Communication – a strong communications plan can mean the difference between success and failure, as it brings awareness to what data literacy is. Through purpose-driven communication, the business can begin to weave data literacy throughout the existing culture.
- Culture – introducing data literacy is not designed to shift the organisation’s entire culture. Rather consider incorporating the DNA of data into the current culture.
- Training – it will be a learning curve, so training is essential.
It’s a Six Step Process
- Plan and establish a vision to drive the adoption of data literacy, ensuring the right people are in the right places and data science is applied.
- Set up the communications plan. Strive to answer this question; “why are we doing this?” Then describe what you hope to achieve and how.
- Assess your workforce; everyone has different skills across the data literacy spectrum. Determine where the skills gaps lie and fill them, while relying on strengths to drive adoption.
- Train the culture to assess what the true data literacy status is – and then to act upon it.
- Move on to prescriptive training according to the skills gaps identified.
- Measure, reassess and strategise for continuous improvement.
Characteristics of Data Literacy Culture
- Data fluency – the organisation must have a common language that can be shared. Develop a “data dictionary” so that standard terms can be accessed and shared.
- Analytical skills – this doesn’t necessarily require stats type application, but you must ask the right questions of the data. Knowing how to frame the right questions is essential to putting things into place.
- Statistical methods – algorithms, predictive analytics, artificial intelligence – you must be able to analyse and predict what is likely to happen, based on your data (including hypothesis testing).
- Visualisations – this is a key element to data literacy itself. Most people don’t know how to use visualisation to simplify complex data. If you put all the data into a visual with the right context, it can give you the answers you’re looking for.
- Learning – this has to happen. We have to take the time to learn; whether it’s about building a visual, framing questions, or coding.
- Mentoring – data science is something that must be applied from the entry level employee right through to the CEO. In many instances, employees are mentoring their executives from a digital perspective.
The Role of Leadership
We’re now seeing the role of Chief Data Officer occurring all over the world (or CIO or CAO). The key to success is ensuring that the need for data literacy is accepted by those in the highest roles of leadership.
- Their role is to put the data vision itself into place
- They are responsible for governance
- The data investment is driven by the leadership
- Leaders set the tone for the data literacy culture (or lack thereof)
If you get buy in at the top, the entire data literacy establishment process becomes much easier.