5 reasons why data literacy is so hard to achieve

Just as owning a Ferrari does not make you a Formula One driver, so having built a centralised data asset does not make your organisation data literate. Plenty of obstacles get in the way of building out a shared culture and set of behaviours. David Reed identifies five typical challenges.
5-reasons-why-data-literacy-is-so-hard-to-achieve

But just as owning a Ferrari does not make you a Formula One driver, so having built a centralised data department does not mean your decision-making is now evidence-based, that processes are being optimised using data science, or that forecasting is now driven by insights into market and customer headroom.

This was clear enough when we asked these same organisations about the level of data literacy beyond the data department – 71% described it as moderate, with skills just starting to develop among certain stakeholders. Of greatest concern is the fact that only three in ten of those firms whose data maturity is advanced saw a similarly advanced level of data literacy.

So why has that heavy investment into data and the push to build out this capability not been translated into operational excellence? Here are five reasons.

1. Nobody likes to be told they are bad at something

While lifelong learning and absorbing new skills get the nod from everybody in business, this usually only applies to abilities outside of an individual’s core domain. Telling a senior executive that the way they make decisions is not effective is unlikely to get a positive reception. For that reason, I have even been told that the very term “literacy” is to be avoided when trying to up-skill people in data and analytics. While not sharing this view, it is clear that the approach needs to focus on how to get a new skill (ie, harvesting value creating insights from data) rather than on why existing practices are no longer fit for purpose.

2. Nobody owns the problem

Got under-performing marketing? Speak to the CMO. Need funding for a technology project? Bring in the CTO and CFO. But need to change the culture of your executive board so it routinely demands evidence of options and their outcomes and who do you call? Few CDOs have the weight to throw around, so they have to rely on gentle persuasion or the fear of missing out to do it for them.

3. Training is easier at the bottom than at the top

Junior roles are often held by younger people who have an appetite to learn and a desire to advance their careers by acquiring new skills. By the time a middle manager has ten-plus years of experience under their belt, chances are they will feel confident that their abilities are fit for purpose. So most data academies focus on basic training and leave everybody else to figure things out for themselves. Without full-spectrum engagement, however, those new skills will not have the opportunity to be put into practice.

4. Finding time to learn is hard

Anybody who has studied for a MBA will know what a miracle it is if you complete the course. Just managing the reading involved is tough if you have a demanding job that routinely extends beyond the conventional 9 to 5. Add to this that most data departments are under-resourced at every level and allowing practitioners, direct reports and senior data leaders to spend time away from their desks in order to up-skill becomes a significant challenge. If they need to do this as a group activity (often the most effective), it means down-time across the department that can be very hard to schedule.

5. There is no bottom line for soft skills

DataIQ is very clear that real data literacy is not just the ability to read and communicate with data – it demands a shared language and set of behaviours. Measuring the impact this can have is no easy task when there is not a vendor-approved certificate to wave in front of the HR director or L&D lead at the end. Demanding hard metrics often stalls data literacy projects (and may actually be a deliberate tactic – see point 1). Instead, a combination of indicators needs to be used that both identify the problem at the outset and reveal an improved culture by the end.

Members of DataIQ have all faced these challenges and unpicked them one-by-one with our help. Not surprisingly, the three out of ten organisations in our research who claimed an advanced level of data maturity were all brands that we work with. If you want to join them in that elevated status, look at our membership options here or contact matthew.white@dataiq.global

Author’s edit: This comment from Wade Munsie via LinkedIn makes for an excellent addition:

6. Data Literacy isn’t really about data at all

“I reckon I can educate a senior exec about data without actually saying the word ’data’. Understanding the situation (may not be a problem), setting the question and then taking that to an empowered team should just about do it.”

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