In the first chapter of Learning Analytics (2nd edition), we write, “Now is a great time to be managing talent and working in human resources. The convergence of three factors — data availability, technology changing the way talent analytics work gets done, and novel insights into employee behavior — makes the workforce a rich area for further analysis and within bounds to be optimized alongside other business inputs.”
This perspective on the importance of learning analytics has been reinforced by diverse research on this topic. In LinkedIn’s 2020 “Global Talent Trends Report,” for example, 73% of respondents said that “people analytics will be a major priority for their company over the next 5 years.”
At the same time, learning and development (L&D) and human resources (HR) organizations may not be ready for this opportunity. The Data Literacy Project found that collectively, businesses lack the skills to read data, analyze it, use it in decision-making, argue with it and communicate it throughout the organization. These skills form the foundation of data literacy and are critical to harnessing the power of people and learning analytics. In its most recent study, the Data Literacy Project found that only “24% of business decision makers surveyed are fully confident in their ability to read, work with, analyze, and argue with data,” and only “32% of the C-suite is viewed as data literate.”
Building data literacy is an opportunity for senior L&D leaders and has wide-ranging benefits. Firstly, data is the language of business, so if L&D practitioners can “speak data,” they improve their ability to align with business needs and are more likely to influence strategic training and development planning. Secondly, when L&D professionals understand the data that both their function and the business generate, they enhance their ability to monitor progress, uncover causes of low performance and create action plans to improve. Finally, a data-fluent L&D function is more likely to take greater accountability for results and to successfully improve its own performance.
1. Establish the Right KPIs
The first factor is to ensure that you have right key performance indicators (KPIs) to manage your business. Go beyond volume measures (e.g., activity and cost) to value measures of performance (e.g., application rates, performance improvement and business impact). Building data literacy requires that you have meaningful measures, goals that define success and plans to achieve them. Chasing after the wrong measures will not build confidence in the data or its value to the organization.
2. Create Role Accountability
Next, clarify the performance expectations of each role. Nothing creates the impetus to learn to “speak data” more than being accountable for a specific outcome. Hold instructors accountable for quality of instruction on post-event surveys. Hold designers accountable for content and role relevance. Hold managers accountable for supporting learning and providing opportunities to apply learning. When you create accountability, employees are more likely to engage with the data.
3. Develop and Distribute Role-relevant Reports
Build data literacy through repetition. Reports that contain context, role-relevant success indicators and meaningful demographics will enable users to become familiar with the data and what it means. If you provide a one-size-fits-all report to data consumers, they are likely to become overwhelmed and confused by the mass of data, much of which is likely irrelevant to them. Give them what is relevant, and do so in context and in a timely manner.
4. Build Skills in “Speaking Data”
If your data consumers break out in a rash when they receive reports, they likely need training and coaching to help them become comfortable with the data. This comfort is the heart of data literacy, but it only becomes sustainable when it is couched with the other five factors. In the case of learning data, focus initially on a few key concepts:
- Measures that are specific to learning (e.g., application rates and scrap learning, expected performance improvement, net promoter score, and manager support).
- Standard calculations that describe the data (e.g., central tendency and deviation).
- Best practices in interpreting graphs and charts. Use a simple method, such as the New York Times’ three-step process to identify “what you see,” “what you wonder” and “what’s the story.”
After employees are comfortable with the basics, you can move on to more sophisticated concepts (e.g., regression, t-tests and analysis of variance) and approaches for data visualization and reporting.
5. Use and Discuss Data at Every Opportunity
Once you have set the foundation, you should model the desired behavior. Remember the definition: Data literacy is the ability to read data, analyze it, use it in decision-making, argue with it and communicate it. Discuss the month’s results in a weekly meeting. Review findings and insights with your organization, and ask team members to pose questions about what they see or do not understand. Encourage employees to question the results: Do they make sense? If not, why not? If so, why? Make the data review a group activity.
6. Recognition and Rewards
When a skill or competency is important, recognize it. Communicate best practices or lessons learned from not paying attention to data. When employees see that you are serious about data literacy, they will step up.
Analytics is a powerful tool to enable the learning function to make major leaps in how it serves and advance organizational performance. Data literacy is a critical success factor in harnessing its power.