Organizations are increasingly relying on data to make informed decisions and maintain a competitive edge. This trend isn’t limited to a particular region, industry, or business size. Whether it’s a local grocery store ensuring their shelves are properly stocked or a Fortune 100 organization creating a global transportation plan, data plays an integral role in our daily lives.
We’ve continued to see similar data trends in our annual Measuring the Business Impact of Learning (MBIL) surveys. And Raconteur’s recent infographic, “The Importance of Data,” further illustrates this trend. In this post, we pick three trends highlighted by this infographic and discuss how they relate to our research and the importance of L&D data.
Reason 1: Data analytics allows you to prove return on investment.
Companies are investing in data analytics and are optimistic about seeing returns on that investment.
Raconteur’s infographic shows that 99% of large companies invest in data analytics and AI, and expect to see successful outcomes. (Note that this stat applies to organization capabilities as a whole, not specifically within L&D.) This means that not only are companies increasingly spending in these areas, but they are also confident this investment will pay off.
We’ve seen similar trends in our MBIL survey data—with more than 80% of respondents agreeing or strongly agreeing that they want to measure the business impact of their learning programs and that they believe it’s possible to demonstrate the effect of learning. In other words, L&D departments want to invest in learning analytics, and they believe that learning analytics works.
Because senior executives and L&D teams both see the value in data analytics, now is a great time to discuss adding learning analytics to your budget.
That’s why we've added questions to this year’s survey to understand L&D teams’ analytics capabilities, available budgets for learning analytics, and the demand for these analytics.
Reason 2: Learning analytics technology is ready.
People and culture barriers, not technology, are preventing organizations from becoming more data driven.
The infographic suggests that many organizations aren’t more data driven due to barriers around people, business processes, and culture. In fact, 92.2.% of companies reported these issues as their most significant barriers to becoming a data-driven organization—compared to only 7.8% who say the challenge is related to technology.
Our MBIL survey results help shed some light on the specific people, process, and culture challenges that L&D departments face when getting started with learning analytics. Respondents said their biggest challenges measuring the business impact of learning are competing priorities, no access to data, or not knowing where to start.
It’s not surprising that these issues are the most significant barriers to analytics. Moving to a data-driven approach to learning and development requires a different way of thinking and working as well as a reevaluation of what data is vital for L&D.
It’s a shift from focusing on the amount of learning produced to its effectiveness. It requires a change in how the L&D department’s performance and value are measured as the purpose moves from putting people through training to making people—and thereby the organization—better. In short, does your training help improve your organization's key performance indicators (KPIs)?
These are significant paradigm changes for both the L&D team and the business as a whole, and they require deliberate effort to achieve. Implementing technology such as a learning analytics platform gives you the tools to support a data-driven L&D department. Still, the technology can’t transform the department to behave in a data-driven way. That’s a change the organization has to make for itself.
That’s not to say that L&D teams have all the technology sorted and only need to address the people, culture, and process barriers. Regarding using data to personalize learning, more than 60% of our MBIL respondents said their main obstacle was either not having the right tools and systems or not knowing what tools and systems they needed. Many organizations face both people and technology issues when it comes to learning analytics.
The good news is that an organization can overcome people, process, and cultural barriers. For example, read how Caterpillar’s Global Dealer Learning shifted its culture from incidental training delivery to talent and performance improvement.
Reason 3: Knowledge is power.
While managers and senior executives have access to data and analytics, most frontline workers don’t.
Perhaps the most interesting chart on Raconteur’s infographic looks at the disparity in access to data and analytics between senior executives, managers, and frontline employees.
As a global average, only 52% of frontline employees have access to data and analytics compared to 81% of managers and 76% of senior executives. This disparity is even starker in the United States—where 81% of managers and senior executives have access to data and analytics. Still, only 48% of frontline employees have the same access.
Understandably, senior executives and managers have prioritized access to data and analytics projects. That’s because the insights they glean from these analytics can help them steer and run the business in a data-driven, informed way—avoiding costly mistakes and poor investments due to a lack of information.
But as data is valuable to inform the work of senior executives and managers, it is equally beneficial to inform frontline employees who could use data and analytics to work more intelligently and effectively.
For example, Amazon uses data analytics to direct fulfillment center pickers to the locations of items to pick for delivery. This approach enables workers to find products faster, rather than relying on memory to find a product or hunt for it on the shelves.
Data for front-line workers can be overlooked in L&D, too. High-level executive reports on compliance and completion rates may be produced in BI tools or spreadsheets, but reports for those on the front lines may be missing. These include:
- Reports on learner interactions with content that would be helpful to inform instructional designers on improving their designs.
- Reports on individual learner competency and performance to inform development plan discussions with managers.
- Learning transcript, skills diagnostic, or learning opportunity recommendation reports targeted at learners themselves.
Learning analytics is not all about reports for senior executives and managers. Putting data in the hands of those on the learning front line is a vital step toward moving to data-driven learning and development.
About the author
As one of the authors of xAPI, Andrew Downes has years of expertise in data-driven learning design. With a background in instructional design and development, he’s well versed in creating learning experiences and platforms in corporate and academic environments.
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