With so many digital transformation priorities, many business leaders would be forgiven for asking why they should focus on data literacy specifically, as it is just one of the core skills requirements of digital transformation.
The straight answer is that higher data literacy levels directly impact corporate performance, which directly affects the bottom line.
The Data Literacy Index − research performed in 2019 with a 7 300 sample size − is a rigorous model that ranks companies against corporate data literacy scores and attempts to correlate literacy levels with corporate performance. The study revealed the following:
- Workforce data literacy has a proven correlation with corporate performance. Organisations ranked in the top third of the index are associated with 3% to 5% greater enterprise value/market capitalisation.
- Based on the average organisation size of this study ($10.7 billion enterprise value), enterprises that have higher corporate data literacy scores can have $320 million to $534 million in higher enterprise value.
- Improved corporate data literacy positively impacts other measures of corporate performance, including gross margin, return-on-assets, return-on-equity and return-on-sales.
Some of the key elements that contribute to the advantages data literate organisations have can be summarised in tangible and non-tangible benefits that can be itemised under specific categories.
Let’s start with efficiency improvements providing data literate businesses with a reduction in the number of hours spent completing analytical jobs. Often senior staff can be highly literate in their field of specialisation − eg, finance, but are not data literate – this can have a serious negative impact on costs in any organisation. The value of time saved through the efficiency of data-fluent senior staff is significant.
This is largely because users are more enabled to do these non-core, but essential tasks. Time saved completing such duties can be utilised doing the core tasks the specialist in question was hired to perform, or even to scrutinise the insights revealed, which leads to better decision-making across organisations. Progressive leaders re-engineer data and analytics to turn decision-making into a competitive advantage.
Staffing and hiring cost savings
More and more staff members are now expected to work with data on a daily basis, but they have not been sufficiently exposed to data and analytics in a pre-digital economy. This poses a question worth considering on how to solve the enablement challenge by hiring versus investing in existing resources.
The Data Literacy Index found that 63% of large businesses planned to increase the number of data literate staff in their employ.
Not all technical talent has the necessary acumen to unearth business insights from the data.
So, there are two primary ways to plug this gap – either hire skills or develop them in-house or a combination of both. I recommend a hybrid approach. Hire key technical skills – focus on recruiting talent that can act as data literacy champions within your business.
There are some caveats, including the fact that hiring is expensive and difficult due to the acute skills shortage. This is compounded by the fact that not all technical talent has the necessary acumen to unearth business insights from the data.
The next part of the strategy must include the development of business talent within the organisation. Key to this is to identify individuals that have the skills interest and potential to become data champions in their part of the company. This approach will ultimately help to inject a data-driven culture into the business.
The cost saving to be gained from training existing resources should be significant when compared with the expense of trying to hire scarce resources.
Often, companies may have well-articulated requirements, a sound business intelligence strategy, and a good toolset solution, but still lack technical skills or the necessary data literacy skills required to get a return on their data investment.
This is manifested by a low number of active users and low utilisation (hours) − the objective should be for more users to start using the system, more frequently. Business intelligence system owners will be able to review this by checking log files to track utilisation pattern change.
You will need to see existing frequent users having a decrease in session time as they become more proficient in using data. Also consider conducting a survey of managers to review their teams’ use of data. A before and after view will indicate the changes of use and improved data-driven decision-making – or not.
Let’s qualify the value of the decisions made by staff. If processing loan applications or performing risk assessments, the value of a poor, or good, decision can be significant. Extrapolate this impact over a year and it can make a material difference in organisational performance stats.
Research reveals it can have a 3% to 5% impact on bottom line. Senior business leaders make massive game-changing decisions all the time. There is overwhelming evidence in the Data Literacy Index research that data-driven decision-making leads to improved decision-making, so an incremental increase has a large and positive impact on many aspects of business performance and customer satisfaction levels.
In my second article in this series, I will expand on data-driven digital transformation.
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