It’s vital that organisations of all sizes can make decisions quickly. A McKinsey and Co study highlighted that companies that “make good decisions fast, execute them quickly … see higher growth rates and/or overall returns from their decisions.”
Yet to reach that point requires businesses to overcome several potential issues, which, if left unaddressed, could cost millions of dollars in lost opportunities and added costs. How does this occur, and what happens?
The impact of low data literacy
Fundamentally it comes down to a lack of truly understanding how data can be read and used. The ability to do so properly is known as data literacy, a vital ability in a world defined by huge volumes of information. Indeed, a study by a Wharton School professor and IHS Markit and commissioned by Qlik identified an improvement of between three and five per cent in enterprise value for firms with pervasive data access, skills and a culture empowering data-driven decision making.
So, what happens when organisations are not data literate? They struggle to enable dynamic decision-making, their potential handicapped by some of the following issues:
Incorrect data: As the saying goes, you can only get out what you put in. If the data is wrong on some level, then any resulting decisions are more likely to incorrect as well. It might be a lack of standardisation in data definitions or the calculation used for a measure, or it might be down to decision-makers not having access to the right data; whatever the reason, the result is inaccurate or untrusted data.
Incorrectly built analytics: If you don’t know how to read something properly, you’re unlikely to understand its true meaning. Closely tied to incorrect data is the issue of incorrectly built analytics, whereby individuals and organisations that are not data literate are more likely to use less than ideal analytics and visualisations as part of their decision-making process. Where this could have a significant impact is when the wrong analysis leads to organisations driving behaviours and performance to a measure that is not positively impacting overall goals. In fact, in most cases, it ends up hurting the organisation. For instance, a retailer that wants to track sales performance across multiple stores could inadvertently create a culture in which individual branches cannibalise customer bases, leading to a drop in sales.
Incorrect interpretations: Some issues are less about the data and how it’s presented, and more about knowing how different groups think. In most organisations, one team might be responsible for building analytics and visualisations for another group that has to use them to make decisions. If the data literacy, unconscious bias or decision-making processes of the latter are not considered when building the analytics and visualisations, or they don’t receive the right context, information can be misinterpreted and the wrong conclusions and insights made.
Blindly trusting the data, or just going with your gut: The best decisions come from incorporating a mix of knowledge, intuition and trusted data. Focusing only on one element at the expense of the others is not a shortcut to the best decisions, even when just looking at data. By having a balance, any flaws in the different elements are minimised. For instance, if intuition says something doesn’t feel right, that can be used as a sign to relook at what you know and what the data is saying. In the same way, if intuition and knowledge say one thing, but the data says something else, it’s an indication that something is wrong and should be revisited.
How organisations can improve
These summaries are the symptoms of low data literacy. For organisations that don’t truly understand how to use data effectively, they lead to ineffective decision-making processes, flawed decisions themselves and, ultimately, impacting the performance of the enterprise.
So how can this be avoided?
Businesses need to start with improving data literacy for everyone who touches data and analytics and makes decisions. This needs to cover understanding data fundamentals and how to read data, then how to work with and analyse the data properly, and how to argue with the data rather than just blindly following what it says.
They also need to adopt a data and analytics strategy, being clear on what they want to achieve with their data and deploy analytics to support that. Simply saying data will inform every decision is not a strategy – as with any approach, it needs to be aligned with the overall business objectives.
It’s also vital that they leverage a systemic and systematic process for making data-informed decisions. It is only in doing so that they can ensure that the reasons for making decisions, and the way in which they are made, are consistent and can scale effectively across the wider organisation.
This all ties in to creating and evolving a culture that embraces both data literacy and decision-making. In doing so, making using data effectively becomes ingrained into the organisation’s operations and decision-makers are held accountable when not incorporating data.
Curing the symptoms to unlock better decision-making
The amount of data organisations deal with is not going to dissipate. While the pace of information growth has outstripped enterprises’ ability to cope with it to date, investing in data literacy is vital. It is the only way to cure the symptoms of not having a data-informed decision-making culture, avoid the pitfalls they threaten, and help businesses capitalise on the opportunities the proper use of data promises.
Kevin Hanegan, Chief Learning Officer, Qlik