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Data literacy: taking a practical approach

Some simple steps and storytelling tools to help build data literacy in your organisation.

Data literacy: taking a practical approach

In my previous post, Life After COVID – three key focus areas for Data & AI leaders to consider moving forward, I briefly touched on the topic of increasing skills and capabilities in data literacy. I also looked at some eye-opening research from Gartner on critical success factors and reasons for failed data adoption projects, which that a lack of skills and capabilities in data literacy was ranked as one of the greatest barriers to an organisation achieving success with its data initiatives. Alarmingly, by Gartner quoted that in 2020, only 50% of organisations will have enough AI and data literacy skills to achieve business value.

What if we simplified the approach to data literacy and looked at this emerging trend through a different lens? Let’s try to use a practical approach to data literacy and break down the barriers.

We’re all familiar with the old expression used to describe something that’s second nature – It’s just like riding a bike. Let’s extend that analogy and use a similar metaphor – It’s “just like driving a car – to look at data literacy and help us better understand how we can analyse and work with data to make data-driven decisions.

When we’re driving, what does the instrument cluster in a car – often referred to as the “speedo” – tell us? Technology in cars has changed significantly over the last decade, moving from analogue instrument clusters to virtual cockpits that provide a range of information to the driver, from very basic things like travelling speed, to encouraging the driver to take a coffee break when detecting irregular steering wheel movements as the car actively monitors driving behaviour. The basic function of the instrument cluster is to keep the driver informed with the most current information when they drive. Gauges or indicators provide the information for speed, distance, fuel and engine and oil heat. Indicator lights provide warnings and updates, such as the ‘check engine’ light and the ‘low fuel’ light.

Let’s examine the fuel indicator, which tells us whether our petrol tank is full or empty but also measures fuel consumption. Fuel efficiency measurements (litres/100km) indicate how many litres of fuel the car needs to travel a 100km distance. This is often referred to as ‘fuel economy’. Typically, the lower the number of litres consumed, the better the fuel economy.

Statistics show that increasing your highway cruising speed from 90km/h to 120km/h can raise fuel consumption by as much as 20%. Based on this, an actionable insight to takeaway is that driving at 90km/h rather than 104km/h helps to improve your fuel consumption by approximately 10 – 15%.

So, from a data literacy perspective, we are now understand that an instrument cluster is an important element in aiding the driver, and it does so much more than simply indicating the speed or whether the fuel tank is empty. It is designed to present the driver with a myriad of data points that can give better insight into driving patterns, and how to take appropriate action to be data-driven and clever in optimising certain processes, such as gaining better fuel efficiency.

So, what can we take away from this practical example, and what Data & AI leaders do to help educate and empower organisations to increase their data literacy? Often, practical solutions are best and can help grow an organisation’s data capabilities.

For example, some simple steps might be:

  • Plan and ask the right questions – What is the fuel indicator really telling us?
  • Prioritise and understand which data is relevant and how to test the validity of the dataWill looking at the water indicator help me increase fuel consumption?
  • Understand the context and interpret the data well, so the results are useful and meaningful – For example, understanding that monitoring fuel consumption will save fuel and money and that, if your fuel consumption has increased, you are probably driving too fast.
  • Have a go at testing the hypotheses –Try slowing down to 90km/h on the freeway to validate the statistic and take advantage of saving 10-15% at the fuel station.
  • Remember that a picture is worth a thousand words and back your results with easy-to-understand visualisations – As an example here, the virtual cockpit has a bar graph to help compare week-on-week fuel consumption.
  • Take your decision-makers on a journey by building a story to help everyone see the big picture – For example: Slow down, as your spouse will know you are speeding if you’re spending more on fuel.

Share your metaphors

Do these points resonate with you? Do you have your own data literacy metaphors and practical solutions that can help increase awareness and data capabilities? I’d be keen to hear your thoughts and reflections – please get in touch or drop me a line.

This blog is part of the #ReimagineWork series. For more experts' insights, clients' experiences and to download our datasheets, click the banner.

For more experts' insights, clients' experience and to download our datasheets, click the banner #datareimagine

Posted by: Leo Kozhushnik, Data & AI Lead Strategist | 18 May 2020

Tags: Data Analytics, Data Insights, Dynamics 365, Customer insights, #datareimagine, COVID-19, #ReimagineWork

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