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7 crucial steps to drive effective learning in your organization

Follow these steps to ensure alignment and investment, and derive maximum value, when introducing learning analytics.

Mention “learning” and “measurement” in the same sentence and it may evoke flashbacks to sharpening your No. 2 pencils in preparation for standardized tests. The good news is that measurement in learning is no longer a distinction between correct and incorrect answers, but rather a process designed to measure and improve engagement and effectiveness.

Today, learning measurement involves collecting data from multiple sources—online course activity, surveys, human resources (HR) data and performance assessments—and applying analytics to measure and improve the impact of learning programming. This emerging discipline, known as learning analytics, has great potential to strengthen an organization’s overarching talent strategies and link learning directly to business impact.

Learning analytics has great potential to strengthen an organization’s overarching talent strategies and link learning directly to business impact.

When introducing learning analytics, we recommend learning and development (L&D) leaders follow seven crucial steps to ensure alignment and investment across the organization and derive maximum value from the data and insights:

  • Establish the strategic plan for analytics. L&D departments must have a long-term vision and strategic plan for analytics, including focus areas. For example, if a company identifies their learning technology as an area for improvement, one objective should be analyzing how users are engaging with the technology and where it is or isn’t successful. L&D leaders must develop the plan in conjunction with the organization’s larger business mission, from growing service lines to retaining top talent.
  • Develop a measurement strategy and identify data needs. Learning teams need to define the questions they seek to answer, which metrics to track and their processes. They must also establish data-quality standards as changes in digital, social and informal learning provide new avenues for data collection.
  • Move from program- or course-centric views to learner journeys. As more organizations design long-term learning programs that span delivery methods, themes and content, it can become challenging to tie outcomes to a specific program or training module. A look at the learner’s journey helps examine outcomes and activity through the lens of multiple learning modalities and experiences.
  • Learn from the education space. Education-focused organizations are using data and analytics to customize daily lessons for students. New York-based organization New Classrooms uses a technology platform that assesses students’ current performance and automatically customizes curricula, skill libraries and lesson banks tailored to their needs.
  • Find the right learning-analytics talent. The nascence of the learning-analytics field means few professionals combine a strong understanding of data science with meaningful experience applying analytics to L&D. Organizations already investing in people analytics can apply this expertise to L&D to bridge gaps. Given that higher-education institutions are now offering graduate degrees in learning analytics, companies can expect an emerging pool of qualified candidates.
  • Link learning data to performance data. Learning professionals must establish an integrated data architecture that permits the ongoing flow and linkage of data among the learning function, talent and business outcomes. Functions across the organization, including HR, benefit from learning analytics. These teams should be involved in the data sourcing.
  • Start small. L&D organizations with limited resources can offer analytics-driven learning by introducing the approach incrementally. To start small, organizations might first consider multiple options for learners to “test out” of certain content if they have existing knowledge or abilities; providing regular, personalized surveys to measure improvement and identify patterns by learner segments; and offer recommendations based on existing data—for example, recommending an elective course that others in a similar role found valuable.

The appetite for data-driven measurement will continue to grow. As L&D leaders use data and metrics to quantify results and improve programs and processes, they can better support their organizations’ goals and those of their learners.

This post is drawn from the learning-analytics discussion in Chapter 12 of Elevating Learning and Development: Insights and Practical Guidance From the Field; the chapter is authored by Gina Fine, Gene Kuo, Maeve Lucey and Lois Schwab.

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