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Big Data Predictive Analytics
Insights

Data and Analytics Is a Vital Capability for TD Professionals

Friday, December 4, 2020
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Data and analytics is a key driver for organizational performance and one of the 23 capabilities in the Talent Development Capability Model under the Impacting Organizational Capability domain.

The ability to collect, analyze, and use large data sets in real time to affect learning, performance, and business is a differentiator. Discerning meaningful insights from data and analytics about talent—including performance, retention, engagement, and learning—enables the talent development function to be leveraged as a strategic partner in achieving organizational goals.

The pace of change and the rise of analytics in the workplace has forced TD professionals to take a broader look at what analytics they should be measuring, the meaning behind the analytics they capture, how to align the findings to strategic business goals, and how to communicate to executives the impact of the data findings.

A TD professional with capability in this area will need knowledge of:

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  • The principles and applications of analytics (for example, big data, predictive modeling, data mining, machine learning, and business intelligence)
  • Data visualization, including principles, methods, types, and applications (for example, texture and color mapping, data representation, graphs, and word clouds)
  • Statistical theory and methods, including the computation, interpretation, and reporting of statistics

An effective TD professional will need to be skilled in:

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  • Identifying stakeholders’ needs, goals, requirements, questions, and objectives to develop a framework or plan for data analysis
  • Gathering and organizing data from internal or external sources in logical and practical ways to support retrieval and manipulation
  • Analyzing and interpreting the results of data analyses to identify patterns, trends, and relationships among variables
  • Selecting or using data visualization techniques (for example, flow charts, graphs, plots, word clouds, and heat maps)

“The first step in commencing your learning analytics journey is that you need to begin with the end in mind. Start with defining your learning analytics challenges,” wrote Stella Lee in an August 27, 2020, ATD blog post. “What learning-related questions or pain points do you want to gain insights from? Before you gather data, clarify what learning challenges (and ultimately performance challenges) you are trying to solve where data can provide insights.”

According to the 2019 Association for Talent Development research report Effective Evaluation: Measuring Learning Programs for Success, one of the challenges associated with evaluation is accessing and analyzing data.
Among the report's findings:

  • Less than half of organizations (43 percent) use big data—defined as extremely large data sets that are too big or complex for traditional data software to process—as a data source for evaluations. Two of the most common challenges are analyzing and communicating findings from data analytics.
  • Top barriers to conducting learning evaluations are the difficulty of isolating the effects of learning programs, the lack of access to data needed to conduct high-level evaluations, limited time to properly evaluate impact, and the costs of conducting those evaluations.

“Designing a data strategy is a long-term investment that will have far-reaching, organization-wide impact. You must think about things in both the current and the future state and view your learning ecosystem and goals in multiple dimensions,” Margaret Roth wrote in a January 23, 2019, ATD blog post. “Be sure to consider what you have now, what you’ve already made plans to invest in, what gaps you know exist, what gaps you haven’t uncovered, and what resources you’ll have access to as you move forward.”

About the Author

Paula Ketter is ATD's content strategist. Previously, she served as editor of ATD's periodicals.

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