Areas of concern
When asked to identify issues that could arise from poor data literacy across their organisation, more than half of DataIQ members listed the following as key areas of concern:
- Data being misinterpreted or inaccurately represented (74.6%)
- Data office needing to provide more hands-on support to users (69.8%)
- Tasks taking longer to complete (66.7%)
- Shadow processes and shadow data get adopted (66.7%)
- Users feeling overwhelmed by data (65.1%)
- Users avoiding accessing data and insights (61.9%)
- Users delaying making decisions (60.3%)
- Dashboards or self-service tools not being used (60.3%)
- User productivity is negatively impacted (57.1%)
- Data office struggling to integrate with the wider business (52.4%)
Data literacy is the bedrock of a successful modern business, but becoming data literate is not easy to achieve, particularly for a large or legacy organisation. Data leaders and CDOs are often against the clock when it comes to proving return on investment (ROI) as well as having to quantify the unquantifiable with metrics usually associated to cost, so a long-term data literacy plan can be difficult to gain backing for.
CDOs must engage with all areas of the wider organisation – particularly sectors that they do not currently have a longstanding working relationship with – to understand how and why these issues may arise. With nearly three quarters of respondents being concerned that data will be misinterpreted or inaccurately represented, CDOs need to ensure they are engaged with ongoing conversations around data literacy and can spot the root causes of misrepresented or misinterpreted data before it becomes a systemic issue.
What can be done?
To avoid the potential issues identified by data literacy, DataIQ asked its members what they are currently doing to improve data literacy across the organisation. More than half of the respondents noted that they were doing the following:
- Encouraged dialogue between the data office and the wider organisation (88.9%)
- Implemented data literacy training for some staff (58.7%)
- Engaged the wider organisation with data events (Hackathons, conferences, etc) (50.8%)
These are strong starting points to enhance data literacy and begin cultivating a data culture, but they would not be sufficient to drastically improve data maturity and wider data literacy skills, particularly for large organisations, by themselves. More needs to be done to alter the way in which data is digested by the organisation and perhaps even the way data is structured within the day-to-day operations.
Other initiatives being employed by respondents included:
- Implemented soft skills training for the data office (49.2%)
- Encouraged data office staff to move into roles in the wider organisation (27%)
- Hired a “translator” to sit between the data office and the organisation (23.8%)
- Implemented data literacy training for all staff (14.3%)
- Specified a base-level of data literacy as part of the hiring process (12.7%)
The difference between investment in some staff (58.7%) to all staff (14.3%) is quite staggering, but understandable – receiving backing and funding to train a whole staff is definitely an undertaking, so it needs to be broken down into smaller chunks. It is here that the ROI needs to be highlighted so that the perpetual funding to train the rest of the staff – and keep training staff for the future – can be acquired.
It has been mentioned many times at roundtable discussions that data staff often work and speak in a different language to the rest of an organisation. Some businesses (23.8%) have taken on data specialists that are able to translate the findings and projects into terms that non-data professionals can readily understand and embrace. Not only does this make the work of data teams more accessible, but it can also be a strong point of contact between the data team and other sectors of the business. Storytelling is pivotal to data success and achieving data-led decisions, so having someone on board that can readily translate between data and the non-data world is a strong addition.
Just over 12% of respondents stated that they specified a base-level of data literacy during their hiring process. On the face of it, this looks like a smart solution to ensure more data literacy in the organisation, but there is a balancing act that must play out. On-the-job training is frequently cited as cost-effective and those recruiting new talent do not want to risk losing any applications during a time of skills shortages. The CDO and data team must work with HR to highlight the skills, attributes, attitudes and other traits required to be a successful member of the data team, which could also mean hiring someone that does not currently have the desired data literacy level. If companies specify a base-level of data literacy, they risk turning off potential candidates who may otherwise have what it takes to be a data leader.