A successful workforce analytics project requires a mix of specialized credentials such as business case management skills, project management knowledge, and workforce data knowledge. However, finding the right talents can be a challenge. Managers and executives often repeatedly ask the same question: what type of human capital analytics topics should a workforce analyst learn?
1. How to Create a Winning Business Case
A great analysis alone doesn’t equate to a winning workforce analytics business case. An analysis that doesn’t answer compelling questions for the organization will not be carried out. A business case without its linkage to business impact in term of ROI, cost saving or potential profit will not be able to show the size of the opportunity and receive buy-in from management. Insights that can’t be summarized in a qualitative and easy-to-understand story will confuse project stakeholders with technical details.
The ability to translate complex analysis into actionable insights that can address the organization’s human capital goals and show the potential impacts is what sets analytics leader from the rest. Here are the 5 elements that make up a successful business case for human capital analytics:
Compelling questions that tackle strategic human capital topics
Objective measures and facts to back up the analysis
Powerful visual charts with insights that tell a story and drive action
Qualitative storytelling to deliver the message to key stakeholders
Linkage to business impact in ROI, potential savings or profit
“Two of our 3 top sales people came from other industries…”
– An example of an effective qualitative story
2. Find Sources of Value of Human Capital Analytics
For workforce analytics to become a strategic function, it requires the practitioners to showcase its potential impacts and earn buy-in support from smaller projects. Similar to the fear of a blank canvas, recognizing where to discover sources of value of human capital analytics can help any analytics team jumpstart their journey with confidence. But do such opportunities exist and how does one know where to find them?
By combining workforce data with Finance and Operations, analysts can start shedding light into many low hanging fruit opportunities that were previously hidden due to the lack of metrics. It’s also an easy start when one focuses on different talent management functions. Below are some project examples:
Performance: Quantify and benchmark workforce productivity metrics
Turnover: Calculate the cost of turnover to the organization
Recruiting: Measure quality of hire and analyze buy vs. build vs. renting talent
3. Analytics Process & Methods
You get out what you put in. In every analytics project, the findings and pretty charts are just the tips of the iceberg. While hidden in plain sight, foundational elements such as data quality, metrics, and measurement standards dictate the accuracy of the results and the efficiency of the analysis. Understanding the data foundation and standards will help practitioners to avoid common pitfalls and possibly shorten project timelines.
Here are the data foundation topics that matter and why:
Metrics Standards, Definitions, and Formulas: Syncing human capital metrics that are recorded and measured across the organization is essential to having a reliable and robust workforce database. It will also help avoid future miscommunication and data misinterpretation.
Timing and Cut-off Standards: Properly implementing this framework will help eliminate transaction backdating, control all workforce system data transfers, and standardize routine system updates and maintenance
Job Classification: Should a workforce analyst be placed in the same category as a financial analyst? Not only does this decision dictate the accuracy of future analysis, but it also dictates how far analysts can drill down in terms of analysis scope. A good classification framework should be able to achieve both goals without compromising data quality.
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