Large swathes of data are pretty useless if you can’t draw relevant insights from it – which is why data literacy is so crucial
For an economy accelerating at breakneck speed towards complete digitisation, there is still much room for internal growth within the moving parts driving this nascent digital revolution, i.e. the workforce. Research has shown there is much scope for organisations to improve data literacy and data skills internally in order to remain competitive in the world market. This is especially relevant at a time when most industries are engaging head-on in the data race to extract the best possible insights from data to aid their business decisions.
While it is true that almost every firm in the world today collects data in some form to improve business performance, not all businesses are particularly adept at using this data and drawing relevant insights from it to actually make a difference. Additionally, many firms even believe that just by accruing data and the relevant technologies, their workforce will be able to employ them immediately. This, however, is hardly the case. This is why data literacy is so key – in leveraging said data sources and putting them to beneficial use. In fact, technologist Bernard Marr even suggests that “data literacy is as important to this century as reading/writing literacy was in the past century.”
In order to combat this data literacy issue, a high percentage of firms today employ external services from data experts to aid the process. Yet, this is rather expensive and creates several bottlenecks when the data analytics needs to funnel down to the workforce not equipped with appropriate data skills. Thus, it may prove of much prudence to broaden the scope of data expertise throughout the organisation. According to Marr, firms must “create the culture, build trust and provide the tools to give everyone in the organization the ability to use data by themselves to inform decision-making.”
One may indeed wonder, in this regard, that if the solution was, in fact, this simple, why it was not applied sooner. Marr, in this respect, points out several other considerations that may be causing a barrier to data literacy:
- Company Culture: “Does your company culture support data literacy? If you have a command-and-control environment, you might as well save your investment in data literacy because it will fail before you even start. Cultures that embrace data literacy start from the top. The culture in your organization needs to allow people to use data, come to conclusions about the data and then make decisions with the data without needing to wait for approval from top leadership before they can act. Leaders need to delegate authority to employees so they can actually use the data and make informed decisions. Without this data-driven decision-making culture in place, it doesn’t matter how data literate your employees are, they won’t be able to actually make decisions and act from that literacy if you don’t allow it.” (Bernard Marr)
- Data exploration technologies: While data is one of the most valuable assets for any organisation, it is crucial (i) to determine whether or not firms are collecting the right kind of data that can aid their strategic objectives; (ii) to ensure the data is trustworthy and that all employees using it trust its efficacy; and (iii) to ensure the presence of appropriate technologies to store and process the data effectively. The use of data exploration tools that allow users to appropriately map, visualise and dissect the data is imperative in this regard.
- Data Skills: The primary path to data literacy, is, of course, accruing the right skills. Apart from using expensive external consultants, firms must invest in their own personnel, in developing the right skills. Often, hybrid teams combining external data science teams and internal resources can help build better data skills and literacy within the organisation.
The creation of new positions — data translators, for example (as in energy giants Shell) – can also prove to be beneficial in this regard. Data translators are roles that sit between business function and data science teams to help bridge skill gaps and facilitate conversations.