Two powerful business forces — pressures related to the VUCA (volatile, uncertain, complex and ambiguous) marketplace and massive amounts of available talent management analytics data — are colliding, driving leaders to explore in earnest the data’s untapped potential to address key talent questions. Despite good intentions, however, this potential is often squandered by a poorly planned and implemented analytical process.
Why? The process steps — identifying, gathering, organizing and analyzing the data and extracting insights and actionable implications — are not always performed within a structured framework. As a result, the frequently hyped promises of big data generate more skepticism and disillusionment than insight, and talent data remains an underused source of in-depth, business-critical knowledge.
To counteract these forces and unleash analytics potential, CLOs should use a sight-insight-foresight-constructed framework to move from isolated data points to value-adding insight.
Sight: The What, Why and How Behind Analytics
The analytical process begins when leaders:
Define business context and imperatives. The sight phase of an analytics effort begins with a clear understanding of the talent-critical business questions that need to be answered and the sought-after outcomes that will propel the enterprise forward. These outcomes, not spurious data, should be the starting point; only later should a logical analytical plan be created. Without these outcomes in sight, there is a dangerous temptation to focus on transactional questions that are unlikely to yield answers of any consequence to the business or ongoing initiatives.
Connect the data from talent programs to outcomes. Data can only be called analytics if it offers new process considerations or changes a decision. The sight phase creates a logical framework composed of steps that connect imperative business questions to data related to current talent programs first and to business outcomes next. In this phase, evaluative decisions regarding the quantity and veracity of the data must be made.
For many companies, existing data will fail the talent management analytics test because the data on hand may not answer the appropriate sight questions: What business questions require talent answers? Can our existing data be credibly associated with the outcomes of the talent program? What additional metrics do we need to capture to draw definitive conclusions?
Note the summary results in the sight-focused analysis in Figure 1. Using the scores from a 2013 large-scale executive-level assessment called “Readiness of Executives: The Business Context” by Development Dimensions International Inc., where the authors work, numerous competencies were evaluated and personality characteristics measured to assess readiness. These were then mapped to appropriate business drivers — leadership imperatives to propel the business forward. This analytical approach produced differentiated lists of drivers for which executives demonstrated the least and the most readiness. The results can be used as a future-foundation for targeted development programs to use current executives’ strengths, while remedying growth needs.
Insight: Identifying Change and Risk
When does a data-driven finding become insight? There is no one answer because the standards continue to rise. Credible insight pairs data with context, integrates additional, relevant data and moves beyond merely reporting the news to spell out implications for the business. Bolstering data with organizational and business context is critical to convert an individual data point capturing what is occurring into an insightful understanding of how, why, what’s next and when, as well as prescriptive guidance about what now.
To glean insight, CLOs should focus on past changes, gaps that remain, progress against expectations and the “so what?” implications. The following questions may help: What is the current impact of a learning initiative on the existing talent pool quality, including current skill gaps, readiness and the leadership pipeline? How do learning initiatives affect the business? Do they improve customer satisfaction, direct report engagement or increase efficiencies?
Analytics is often associated with impact measurement for existing learning programs. This encompasses resultant changes in behavior and the enterprise-level outcomes achieved. To add incremental insight to measurement, also look at variables related to organizational enablement factors coinciding with existing programs. When applied systematically, enablement factors can produce actionable guidance about the learning support factors most damaging to the program’s impact.
The 2013 DDI analysis “Better Leaders — Better Business Results,” based on 50 evaluation studies on leadership development programs composed of more than 4,000 leaders, revealed four enablement factors: leader motivation, opportunities to apply skills, holding leaders accountable and program content relevance that are found to have strong links to higher levels of leader behavior change.
By looking at both impact and enablement factors surrounding a learning program, this analysis can offer insight to obtain a higher return on related investment. Armed with this information, CLOs can take steps to improve the enablement factors as well as the program’s effectiveness.
Foresight — Future Expectations
Most executives are more interested in looking forward than reviewing past events. In the foresight phase of an analytical plan, information gathered earlier can be used to extrapolate future talent readiness and skill gaps. Foresight models also can use scenario planning and “what if” analyses to prioritize proposals, weigh the cost-benefit ratios of selected potential adjustments and ultimately optimize talent programs. Future-focused analyses also permit learning leaders to make the rapid adjustments necessary to realign with changing business objectives.
Answering these questions might help: Will talent required to meet future global expansion goals be available in five years? What design and implementation modifications will most improve the rate of behavior change generated by existing learning programs? When will identified leadership readiness gaps necessitate drastic course corrections in development?
Future-focused analytics can be used to gauge and mitigate risk within a leadership talent pool, and forecasted skill gaps can be proactively addressed through targeted development. The value of foresight is particularly significant in high-growth markets such as India and China. Results from DDI’s 2013 report “India vs. China: How Leaders Measure Up in Behaviors, Personality and Business Context,” an analysis of structured behavioral assessments, show that leaders in both countries struggle with preparedness to meet future business challenges (Figure 2). The results also show Indian leaders are stronger when engaging employees, while Chinese leaders are more proficient in cultivating customer focus and culture.
When leadership readiness is paired with future growth targets, the savvy CLO can project out the consequences of the current state of readiness and determine if the business strategy will outstrip available talent. Key questions include: Where will current talent most need development to contribute sufficiently to organizational goals? Which organizational enablement factors need to be redirected so that readiness improves to meet business goals? Armed with this analytically derived knowledge about the risks of leader deficiencies, learning leaders can design programs to close these gaps.
Implications for the CLO
To execute the sight, insight and foresight framework adroitly, and achieve the insights critical to the C-suite and drive the business, the CLO must be equal parts:
Master integrator: The CLO will facilitate the integration of learning subsystems — onboarding, training, feedback and measurement — and ensure the subsystems function well together in the talent management and business ecosystem. Master integrators must be skilled at matching customer needs with existing learning levers and data. As a result, CLOs of the future will be charged with a substantial amount of diagnostic and troubleshooting work. Their ability to research existing products and talent management components to improve organizational readiness, capability and capacity will be critical.
Alchemist:Much like the alchemists of the Middle Ages who sought to transform base metals into gold and discover an elixir for life, CLOs will need to transform base data sets into talent insights that executives can use to drive business strategy and extend the life of learning systems. The CLO-alchemist will apply a logical pathway linking data with business questions and avoid reliance on convenient, on-hand data. Similar to alchemists, CLOs will require a well-developed sight-insight-foresight framework, with precise terminology, experimental processes and specific techniques. The CLO-alchemist’s mastery of this framework will transform base data into powerful future insight.
Futurist: To add value, insight must mitigate future risk. Therefore, the CLO must play the role of futurist and be the person who studies the future, applies insight and makes predictions based on current trends. It is not the futurists’ goal to predict what will happen. They focus on how best to use insight and foresight to describe what could happen and, in some cases, what should happen in the future. Two analytic practices — working backward from strategy-level business questions and formulating forward-looking prescriptive analyses that project the talent readiness levels necessary to match business needs — are necessary for the CLO to provide insight powered by analytics.
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