People analytics and learning: Driving workforce development by delivering the right solution to the right people at the right timeFebruary 20, 2019
The context for learning analytics
The desire for a more accurate and reliable measure of learning’s effect on business outcomes is not new, but access to the data points to create it is. And now it’s more than just desire—we live in an evolving reality that makes measurement an imperative. The rapidly changing nature of work is creating a nearly continuous need for upskilling the workforce. With the half-life of a learned skill at 5 years,1 the hunger for learning and development programs is ravenous.
Furthermore, as technology and learning platforms make information more accessible, people are now learning within the flow of work.2 While classrooms and instructor-led training may not go away entirely, the reality is that the majority of learning is happening informally—from an employee’s simple search for a how-to video to address a task in the moment to reading an article of interest posted by a former colleague.
But all learning solutions do not provide equal value, and as the level of investment required grows, so does the need to prune and focus on the most effective learning programs.
From creation to curation
The mission-critical need to upskill3 and the variety of content sources has sparked a move from content creation to experience curation4 further enabled by the learning experience platform.5 This growing need for upskilling,6 the expansion of learning beyond the classroom, and the proliferation of data sets are the building blocks of a new era for the learning function: an era in which analytics powers the learning function to operate as a driver of the business, providing valuable insight and guidance on how to develop the workforce to optimize business outcomes.
Measuring the effectiveness of solutions in an increasingly social learning model in which content is curated, not created, demands broadening metrics and indicators to include not only learning data, but business data and user behavior data, too. As learning moves in the direction of expanded user choice, like that found in on-demand TV, organizations need to update their approach and design analytics tools to capture the learning in these new platforms. This is the cue for real-time learning analytics and insights.
With learning analytics, the organization gains timely information and feedback on the efficacy and impact of various learning investments on individual development, organizational learning trends, and business outcomes. The mature learning function can use these analytics insights to drive decision-making about which programs, tools, and resources to continue, to expand, to terminate, and to initiate.
A sustainable analytics strategy starts with an effective data strategy
Evaluating the ROI of a learning solution by its impact on business outcomes requires robust statistical analysis. Even before the analysis can be complete, it demands the combination of independent data sets—data that live in different functions (and possibly even storage platforms) inside and outside the business. Effective aggregation depends on a clear data governance structure, standardized data sets, and a data normalization process. Once combined, disparate business, learning, and user behavior data can enable new insights about the impact that a particular learning resource is having on the business.
For example, imagine if you could see the change in investments made by your financial advisers after participating in a financial strategy lab; or if you could measure the increase in “followership” of your senior directors after attending a leadership seminar. To take it a step further, imagine knowing which links are most shared, which articles garner the most careful read, and who people actually go to when looking for new information. We already know the importance of lifelong learning on leadership,7 but what if you could identify your star performers based on learning habits, or preemptively upskill in the areas where learners are self-directing their focus? These insights are available. The data exist. They just need to be brought together and analyzed.
Aggregating and analyzing siloed data is not easy; it requires an intentional and well-developed data strategy. An effective data strategy should include five components:
- Alignment on the goals between stakeholders
- A hypothesis of which data is necessary for meeting those goals
- A plan for how to integrate the data
- Clear expectations for all stakeholders
- A built-in review process to evaluate progress and adjust as necessary
Learning functions that develop this analytics acumen are equipped with foresight—a capability that helps them see what information is most in demand by employees, which learning experience is most effective at changing target behaviors, which learning solution has the strongest correlation with performance, and in turn, which learning intervention is most valuable to their company.
While stitching together data can be difficult, it is a necessary prerequisite for gleaning insights into the impact of learning on the business. The beauty of an effective data strategy is that it lays the foundation for an L&D function to gain insights from the top down and from the bottom up. When the data is actively managed and integrated, Learning functions can ask: What impact did a learning solution or program of learning solutions have on the individual and business performance?
Simultaneously, an effective data strategy with real-time integration enables the business to realize trends among top performers and backwards engineer the common variable, potentially tracing it to a specific learning resource. For instance, imagine searching for a common learning experience among all of your most effective salespeople—perhaps, they all follow the same SME on your learning experience platform, read the same book, or completed the same online course! Stitching together data sets across different parts of the business and understanding the impact of specific variables requires a strong data strategy, statistical expertise, and a commitment to robust analysis.
A case in point
One public sector agency has already been able to optimize learning investments based on this type of learning analytics. Taking a broad-based data strategy, this client analyzed the relationship between L&D investments and KPI performance, quantifying the value of the investments made in their employees’ learning experiences. They examined operational efficiency metrics, regulatory metrics, and business KPIs to evaluate the effectiveness of the learning investment and its impact on organization performance. These investment analyses were provided in a digital, interactive dashboard providing a single point of access for agency leadership to continue to track the ROI of their L&D investments. It is the link to business impact that allows the Learning function to make data-driven business decisions to precisely deliver the most effective learning solution to the right people in the right way at the right time.
Making your spending count for more
The process of becoming a Learning function that leverages analytics to drive the business forward begins with understanding and anticipating the business’s needs and aligning the learning strategy to optimize learning investments. What is important for one organization to address may not be relevant to another, but the opportunity is the same. L&D functions that aggregate the right data sets and conduct the relevant analysis to yield valuable insights are drivers of the business, rather than simply providers of programs.
1 Deloitte Consulting LLP (2017), Bill Pelster, Dani Johnson, Jen Stempel, Bernard van der Vyver, Careers and Learning: Real time, all the time https://www2.deloitte.com/global/en/pages/about-deloitte/articles/careers-and-learning-real-time.html
2 Bersin™ (2018), Josh Bersin, A New Paradigm for Corporate Training: Learning in the Flow of Workhttps://joshbersin.com/2018/06/a-new-paradigm-for-corporate-training-learning-in-the-flow-of-work/
3 Austin Carr (2018), Moneyball for Business: How AI is changing talent managementhttps://www.fastcompany.com/90205539/moneyball-for-business-how-ai-is-changing-talent-management
4 Deloitte Consulting LLP (2017), Jason Magill, Carly Ackerman, Mariana Aguilar Learning FOMO: How curation can help prevent you from missing out on the development you expecthttps://hrtimesblog.com/2017/11/20/learning-fomo-how-curation-can-help-prevent-you-from-missing-out-on-the-development-you-expect/
5 Deloitte Consulting LLP (2018), Michael Griffiths, Josh Haims, Terry Patterson, Lindsey Straka West, Meriya Dyble, Debbie Blakeman Learning as Platform: Redefining how learning delivers value to the businesshttps://www2.deloitte.com/content/dam/Deloitte/us/Documents/human-capital/us-cons-learning-platform.pdf
6 Deloitte Consulting LLP (2018), Dimple Agarwal, Josh Bersin, Guarav Lahiri, Jeff Schwartz, Erica Volini From careers to experiences: New Pathwayshttps://www2.deloitte.com/insights/us/en/focus/human-capital-trends/2018/building-21st-century-careers.html
7 Harvard Business Review (2015), Kenneth Mikkelsen and Harold Jarche, The Best Leaders are Constant Learnershttps://hbr.org/2015/10/the-best-leaders-are-constant-learners
Originally published at Capital H blog