CWS 3.0: October 16, 2013

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Big Data Must Be Meaningful

By Kay Colson

Big data is everywhere. Innovative technologists are introducing new solutions and tools daily, enticing us to “cut through the big data clutter” and “access timely and accurate insights.” It all sounds exciting and important … the business of planning, recruiting and hiring the right workforce being driven by meaningful data!

Recently Pinstripe, a recruitment process outsourcing provider, suggested “The true sign that analytics have arrived in the HR software space is the presence of visual reporting tools, customizable data relationships and cross-platform data integrations in nearly every product. Software vendors clearly understand how important analytics are to HR, and they are building analytics functions into their products. It remains to be seen how the execution holds up, but understanding the need for tools is an important first step.” No doubt analytics have arrived, but what does that mean to the average recruiting manager? For starters, it means that you need to define what business intelligence you need and the data that drives it.

There are four types of business intelligence that can aid recruiting. All are powerful and should be considered as we put big data to work to improve recruiting. To be successful, your external or internal recruiting team must be actively pursuing all four.

Performance. Most commonly used for service-level agreements and performance plans, this category includes time-to-fill, candidate and slate quality, hire retention, service delivery quality, and many more.

Retrospective. We don’t use these as well as we could, but they are very important as key performance indicators, which help us diagnose delivery failure before it happens. Think about best sources as well as ratios of hires to candidates presented, interviews, offers, etc. Effective workforce planning brings knowledge and forward planning to the recruiting process and is highly dependent on good historical data. We need more work on tracking where best candidates come from, i.e., schools, competitors, geographical regions, etc.; how many candidates it ideally takes to make a fill; and why qualified people decide to join. We would also benefit by looking back on successful hires and the characteristics that contributed to their success.

Trends. With global requirements increasing, program managers need to understand current trends in each market where they hire; the market changes quickly, so tracking must be ongoing. Compensation trends alone require constant diligence to ensure you are always considering market rates. Following and understanding trends in open, held and cancelled requisitions, as well as fills, is the best way to know what is ahead in workload.

Predictive. Now here’s the meat — and something we have not yet mastered. We should be exploring what indicators show that a candidate is more, or less, likely to succeed in your organization or move upward a step on the food chain, and what characteristics are likely to contribute to exemplary performance. Does this sound like data we can only gather in the interview and screening process? With big data, that’s changing.

A final word to the wise: Be sure you are comparing apples to apples … location, skill sets and much more can all affect your business intelligence. Data integrity is critical. Thoughtful interpretation turns data into business intelligence. 

Kay Colson, senior associate at Brightfield Strategies, specializes in workforce planning and talent acquisition consulting services. She can be reached at kcolson@brightfieldstrategies.com.

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Data 101 17/10/2013 2:56 pm

Kay, very nice article on Big Data. When considering a big data strategy, I think it's worth mentioning HPCC Systems from LexisNexis. Designed by data scientists, HPCC Systems is an open source data-intensive supercomputing platform to process and solve Big Data analytical problems and can help companies derive actionable insights from their data. HPCC Systems provides proven solutions to handle what are now called Big Data problems, and have been doing so for more than a decade. The main advantages over other alternatives are the real-time delivery of data queries and the extremely powerful ECL language programming model. More info at http://hpccsystems.com


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