Data gathering and analysis are touted as the solution to many talent acquisition issues. Still, very few have defined what they hope to get from their analytics, thought out the implications of, or established guidelines for using the results of their analytic process.
Gathering data and analyzing it may present as many problems as it solves.
Even though companies, including Google, a pioneer in using analytics to change how it hires employees, learned that analytics disproved cherished beliefs. Their analysis showed that GPA was not a good indicator of success nor was the school an employee attended which led to consternation and eventually a change in hiring requirements. Good analytics requires an understanding of the potential consequences of what may be learned and the shortfalls and biases inherent in any analysis. Be cautious. Be thoughtful.
Here are eight basic concepts that must be understood and built into any analytic strategy.
#1. No Magic Bullet
First of all, analytics is not a magic bullet. Data can help you understand an issue and perhaps help you argue more effectively for a course of action, but data does not replace the need for empathy or human reasoning. Even when a statistic may indicate something, it may not be reason enough to take action.
Knowing the context of the situation is important to success. Just knowing that of the last eight people interviewed, no one was tendered an offer doesn’t tell you very much. You also need to know who interviewed them and what their reasons were for not making an offer, what the economic circumstances are of the company or function, who the recruiter was and how competent they are, and so on. It takes contextual knowledge, factual knowledge, caution, and good judgment to use the results of analysis effectively.
#2. Know What You Want to Know
Secondly, you need to be crystal clear about what you want to analyze or measure and ensure that it is possible to accurately analyze or measure it. As Einstein said, “Not everything that counts can be counted, and not everything that can be counted counts." You can measure quantity, source, time, and cost reliably. But it is hard to measure quality, satisfaction, or engagement reliably or accurately, as these are subjective elements. Survey results can be interpreted in many ways, survey questions can and often are biased, and employees may answer with what they think you want to hear or maybe answering in the hope that their answer will change something in their favor.
#3. Use the Appropriate Method
The method of gathering data may also be a problem. One use of analytics is to illuminate a problem or find possible reasons for something, yet even that can be daunting and inaccurate. For example, turnover of employees may be caused by a wide variety of factors including poor management, lack of confidence in the organization’s performance, personal grievances, poor pay or benefits, antagonistic fellow employees, personal feuds, family issues, or a lack of empathy with the culture. A survey or a focus group may uncover some of these and even give particular weight to one or two of them, but it is very difficult to determine if you have found the root cause. Answers to survey questions are likely to be subjective and as we all realize, many reasons people give for leaving do not reflect the truth, but are designed to avoid “burning any bridges.” In this case, it may be better to trust instincts rather than to rely too heavily on the results of a survey.
#4. Passive Data may be better than Solicited Data
It is far easier to gather passive data about something than to solicit valid data from people. It is relatively straightforward to gather factual and historical data from the results of actions and decisions. Good analysis can help recruiters understand where their hires come from, which sources provide the most employees, or which social media messages are the most effective for generating leads. But even with passive data, there are significant challenges. For example, it is possible to interpret what traits the most productive or longest-tenured employees have in common, but it is hard to prove whether or not these correlations are the cause of productivity or tenure. The correlation is causation situation is common and has to be vigorously avoided.
#5. A Supportive Culture is Important
To make good use of data, there has to be an accepting leadership and corporate culture that values data and is willing to use it to make decisions. If your organization or functional leadership does not use analytics, you may find your work going to naught. Leaders who understand the value of data and already use it for better understanding manufacturing or marketing will be far more receptive to you.
#6. Focus is King
It is tempting to try and measure everything, especially in the early days of establishing an analytic function. It is important to focus on 2-3 key questions you would like answers to. This will allow you time to gather better data and analyze it more completely. Sometimes data scientists can get so excited over the insights they are digging out of data, that they lose sight of the original goal or purpose of their search. It requires discipline to stay focused. You must define the inquiry carefully and as narrowly as possible to get useful, actionable data. For example, determining which sources of candidates lead to the highest number of offers for a particulate function or hiring manager can be useful. Trying to extrapolate that to the entire organization may result in some poor decisions.
#7. Data Is not Pure
We tend to put analytical data on a pedestal and think that it is pure and uncontaminated by politics or opinion. Unfortunately, data and data analysis are just as subject to bias and opinion as anything else. Politics plays a part in determining what data you gather, what you measure, how you measure it when you measure it, how much focus an area gets, and what conclusions and decisions are drawn from the data. Stakeholders, customers, and employees all have opinions and need to be listened to.
#8. Keep it Simple
Take the time to list what you really would like to know to improve your overall recruiting capabilities. What data would help you make a better case for more resources or answer the pressing questions management has asked you? Work with your analytics expert to determine what can be honestly and reliably analyzed. It is very tempting to go after data collected through surveys, focus groups, and interviews but as mentioned above, data gathered in this manner can be manipulated or misinterpreted because of bias, intentional or not.
Data gathering and analysis are difficult and fraught with problems - poor data, too much data, not enough data - as well as issues of interpretation and understanding. Any good use of analytics requires a well-thought strategy, defined goals, thoughtful discussions, and an understanding of limitations.
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