Once a company has identified data literacy as the mission-critical skill that it is, the next logical question to ask is: just how data literate is our team currently?
Companies know the value of data literacy. A report on data literacy upskilling states that 85% of C-Suite executives believe that data literacy will be as important as basic proficiency in computer usage in the near future.
Once a company recognizes the importance of data literacy in its success, it needs to assess just how well its team is currently able to handle and understand data. Does the team have enough experience selecting appropriate analytical methods or machines, interpreting results correctly, or communicating those results effectively internally and externally?
Below, we explain the relevant data literacy skills employees need, how to assess and identify skill gaps, and how to address them with avenues of training.
Each organization — and the different team members within the same organization — will need different data literacy skills. C-Suite execs, for instance, require data literacy skills and levels inherently different from someone from a different role with different responsibilities, like financial analysts or sales specialists. In general, however, the data literacy skills needed by employees can be broken down into general silos.
Critical examination of data sources involves a good understanding of the type, quality, and accuracy of the source. This could involve fact-checking or investigating any inconsistencies in data sets. It also includes being able to assess how reliable a source is and whether it can be trusted when making business decisions. Employees must be able to identify patterns or trends within the data that can provide valuable insights into business operations.
Proficiency in database and spreadsheet software is essential for data literacy because it enables the quick organization, visualization, and analysis of data. Microsoft Excel and Google Sheets are two of the most popular choices among businesses today as they offer a broad range of tools for analysis, such as:
Spreadsheet software proficiency is highly applicable to employees in key roles such as performance and financial analysis and market prediction.
Meanwhile, database software is an irreplaceable part of a full tech stack for any web application or software deployment. The backend developers responsible for database software development and maintenance likely already retain high levels of data literacy. However, other users who deal with database-related tasks often do not.
This includes understanding descriptive stats such as mean, median, and mode, as well as inferential statistics such as confidence intervals. These measures provide upper and lower bounds on the likely values of an unknown population parameter.
For example, when determining what percentage of people in a target market own a pet dog, confidence intervals can help figure out the extra margin (or leeway) within which this true value should lie. This makes it easier to determine marketing campaigns aimed at pet owners.
Ultimately, this data literacy skill amounts to the ability to "speak data" — how to use it to comfortably and effectively communicate important concepts and information for informed decision-making.
This can allow employees to identify patterns and trends more easily. For example, if a company works in an industry that routinely collects vast amounts of streaming data, such as social media mentions or clickstreams, then being able to work comfortably and quickly through millions of rows/records would be beneficial.
Furthermore, leveraging Big Data analytics tools like Hadoop or MapReduce for distributed computing tasks allows the use of parallel processing for enhanced analysis of volumes of information. Parallel processing reduces potential bottlenecks by scaling linearly as input sizes increase, thus improving response times immensely.
KPIs are at the heart of performance management for many modern organizations, and identifying and tracking them is easy. What really matters is the data literacy needed to harness them for actionable insights to arm key decision-makers.
This means workers throughout the organization must be able to align relevant KPI data points to concrete business results. Increasing data literacy in this regard changes an employee's capability and mindset from just monitoring a KPI's ups and downs to connecting it to other meaningful figures and deriving value from the bigger picture they present together.
Deloitte recommends using assessments to not only steer progress in data literacy efforts but also prove its value.
A baseline assessment of employees' literacy skills can help identify which workforce segments to prioritize. With these results, data literacy programs specific to each segment can be customized and tailored based on their level. For example, employees could be divided into brackets based on data literacy maturity after analyzing the data from testing. Based on their allocated cluster, team members can be given individual guidelines for bettering themselves in data literacy.
Assessments will reveal which team members are increasing their skill levels and in what areas of data literacy. However, don't expect their newfound data literacy levels to reflect in their work immediately. The assessment could reveal other gaps that indicate more data literacy training is needed. Assessing their skills quarterly will keep track of what clusters have achieved so far along their journey and where they may need more instruction.
Assessments will also point toward skill gaps, which a successful data literacy strategy should address by focusing on education, training, and professional development.
Before deploying these, however, consider all key stakeholders' needs (for specific employees with different-level requirements). This is essential when determining the best training modules or initiatives and when to use them. Tailoring an implementation plan according to the department can help maximize efficiency while avoiding unnecessary overhead costs associated with broader upskilling efforts.
These considerations will determine the next steps toward implementation — formalizing education protocols and resources for each team level or introducing more hands-on guidance via workshops conducted by outside experts.
Fundamental data literacy education should begin with providing a basic theoretical understanding of data-related concepts such as types of data sources, collection methods, and analysis techniques used in various fields or industries. Training can vary from department to department depending on its specific goals — whether the goal is learning how to use analytics software more effectively or obtaining more advanced statistical knowledge.
With a comprehensive program built around education and training and developmental tools at their disposal, organizations can better equip themselves with the necessary resources to embrace their literacy efforts in full force.
What's important here is that the team — if possible, the entire organization — strives for excellence in data literacy. Luckily, many top executives just need a solid push in the right direction.
Assessing data literacy is the first step toward impactful initiatives for any modern organization that wants to achieve meaningful digital transformation.
Reach out to the Correlation One team to find out how they help companies ensure success with essential data literacy training.