Every company has large datasets but relatively few are truly able to convert their big data into smart talent data. Here are three steps that will help you to extract actionable value from your employee data.
1. Review what data you have. By 2020, Cisco predicts that IT departments will be coping with a three-fold increase in the amount of information that’s available today. New capture, search, discovery and analysis tools will inevitably be developed to help organisations store, process, ‘wrangle’, integrate, share and gain insights from their data. In the meantime, you should start to identify what data you currently have in your organisation, how it is used and by whom. Specialist knowledge and tools are required to manage and analyse data. If your organisation doesn’t employ data scientists and have the necessary hardware and software – or if you don’t partner with a specialist who can help you make sense of your data – then rectifying these omissions should be a strategic priority.
2. Ask the right questions. What do you want your data to tell you? The key to creating smart talent data is to ask the right questions for the situation that you’re facing. It sounds simple enough but this is actually an area where expert help can be highly valuable, as the ‘right’ questions will greatly depend on your strategic goals. In recruitment, for example, one question you might ask is: ‘where do good candidates come from?’ If you can interrogate your data and answer this, you’ll know where to focus your candidate attraction strategy. This could lead you to invest further in your careers website or to spend more on other channels such as LinkedIn. However, the answer to this question really depends on how you define a ‘good’ candidate. Does that mean someone who achieves high ratings, someone who is engaged, someone who is a good fit for the role, someone who is able to become productive quickly or someone who has stayed with you for five years or more? The answers may be different depending on the criteria you use. The point here is that each question has to be precise, so you need to clarify exactly what you mean. Then you can look for the most appropriate data that will provide the answer.
3. Draw the right conclusions. If you don’t ask a sufficiently detailed question, you’ll get inadequate or ambiguous answers – and that can lead you to draw the wrong conclusions. In some cases, this can be highly damaging. You’ll assume you’re making a sound, fact-based decision but that won’t be the case. For example, a dataset might reveal that the more ice cream that people eat, the less likely they are to wear socks. Clearly, both of these statements relate to being in a warm climate. However, the wrong conclusion is that eating ice cream influences your fashion choices! It doesn’t mean that the data is wrong; it’s just that the conclusion is flawed. Smart data will only benefit you if the results are interpreted correctly. Knowledge and experience are required to look beyond the patterns that emerge and to ‘sense-check’ whether your deductions are an accurate result or a red herring.
Once you’ve formulated the right question, you might realise that you don’t have the necessary data to answer it. You might then embark on a quest to identify the data points that will give you the information you need. Or you could form a hypothesis and test it out with a research or validation study. In turn, the results of these could prompt you to undertake further studies until you gain the conclusive answers you seek.
Another advantage of having readily available data is that you can combine the pre-hire assessment results of successful candidates with their post-hire performance data in the role, to create predictive talent analytics. These can help you to identify and recruit high performing staff and create an action plan for the future that’s based on proven evidence, not hunches or guesswork.
Regardless of what your organisation actually does, smart talent data will provide insights that can benefit your business. The secret is to recognise the potential that data provides and to ensure that you ask the right questions and draw correct conclusions.
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