Good Employee Selection Takes More Than Hiring A Warm Body

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Good Employee Selection Takes More Than Hiring A Warm Body

Nearly 15 years ago, I created CriteriaOne® – The Whole Person Approach to Employee Selection. It received a trademark shortly thereafter. The concept behind CriteriaOne® was that it took more than one assessment to evaluate the job fit and potential for a candidate or employee.  Despite the growing complexity of jobs thanks to technology, many consultants still promote the use of a single assessment to hire employees for dynamic and complex jobs. That’s like buying over-the-counter reading glasses to treat blindness.

Uncovering the right mixture of knowledge, skills, and abilities from a candidate takes more than just using a DISC profile, MBTI, or personality test.  Many jobs require higher level cognitive skills and general mental abilities. Team and company culture fit demand an inside look at a candidate’s attitude and values?

Thinking about employee selection like a mosaic may be the answer.

A mosaic is an art form.  That’s a potent metaphor when it comes to the evolution of employee selection.

I use the mosaic to represent the art of employee selection because the mosaic is typically a picture or pattern produced by arranging small pieces. In art, the pieces may consist of colored glass, stone, or other materials.

Hiring is no longer just fitting a warm body into an open position. Hiring the right employee is both science and art… and like a mosaic, no two patterns are exactly the same even when same materials are used to create it.

Applying a mosaic approach to employee selection allows hiring managers to create a statistical image of each candidate, where the sum is greater than the parts. While employee skills and abilities may be similar, how they integrate and mix all the components of their personality may create a different mosaic. Whether these subtle differences are significant or just incidental requires converting the bits and pieces of information into valuable trends and predictive data.. By combining data from multiple sources, employers create the preferred mix of knowledge, skills, abilities, personality traits, personal values, and behavioral style for each position. They then can compare candidates and internal employees to the existing data of successful and unsuccessful employees.

Corporations have historically had massive amounts of data at their fingertips, although it was largely buried or ignored in HR. But today fierce competition is forcing human resource professionals to be held to as high a standard as finance, sales, and operations when it comes to maximizing productivity, leveraging resources, and minimizing waste. Matching industry average turnover rates and tolerating under-performing employees are no longer acceptable practices. Industry-average performance these days by human resource professionals leads to company mediocrity.

A successful hire can no longer be measured simply by perfect attendance and tenure. A warm-body and the ability to fog a mirror just don’t cut it anymore. The cost to hire is an easy metric to quantify but whether it’s low or high translate much better to budget discussions employee performance.

Vital metrics now must on performance outcomes. And quality of hire metrics such as individual productivity, innovative capacity, and even manager satisfaction are making huge differences in company growth and profitability.

Companies up until recently have treated the results from different assessment tools as independent, static, and linear data. Success was based on a cumulative effect – 1 + 1 + 1 = 3, like laying the bricks one by one to build a wall. Candidates received a score for experience, the interview, and assessment scores and managers laid one brick upon another. The candidate with the highest wall won.

But when data is blended, integrated, and then mined for dynamic patterns and trends, sometimes 1 + 1 + 1 does not equal 3. By using all the data points, present and past, business can discover significant relationships, formulas for success (and failure), exceptions, and anomalies. As is the case with data mining, the source with the most data doesn’t always win. Sometimes the best scores don’t predict the best candidate. But by mining all the data, a pattern of extraordinary talent or potential can be uncovered. At other times, a combination of abilities and personality traits similar to the interaction of oil and water might predict an under-performer or poor cultural match well before the applicant goes on the payroll.

Beyond the top performer mosaic, data mining of personal and assessment data helps identify candidates for succession planning as well as employees at risk for leaving the company for a competitor.

The key to hiring and retention success then lies with not merely using assessment tests, but combining the right tools in a mosaic of data and information where managers look at the picture the whole person creates.

The mosaic offers a profile that provides a far better peek into “what happens next” than just looking at the individual elements.

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