Data is an essential strategic resource in organisations. It can help you to understand your market and spot trends, respond better to customers and predict their likely behaviour, develop new products and make considered business decisions. Richard Justenhoven explains how organisations can get the best from big data.
Today, every company has large datasets but few are truly able to convert their big data into smart data. Here are three steps that will help you to extract actionable value:
1. Review your data. A staggering volume of structured and unstructured data exists in global data centres and more is coming. 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 converting big data into smart data is to ask the right questions for the situation that you’re facing. The ‘right’ questions will be different in every organisation, as they greatly depend on your strategic goals. Traditionally, businesses have relied on market research, customer analysis and expert judgement to support aspects such as product development and sales forecasts. Now, data is another weapon in the armoury that can provide insights that will drive actions.
It sounds simple enough to ‘ask the right questions’. But this is actually an area where expert help can be highly valuable. 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.
At cut-e, this is how we operate. We work on data projects with clients, in areas such as predictive talent analytics where we help them to identify and recruit high performing staff whilst reinforcing their employer brand. We enable HR and talent teams to utilise and make sense of their data, for example by combining the pre-hire assessment data of successful candidates with their post-hire performance data in the role. We guide them with insights from our own research, to ensure they create an action plan for the future that’s based on proven evidence, not hunches or guesswork.
We also regularly review the assessment market to examine what’s changing and evolving. This enables us to present clients with credible studies and insights that can be shared with stakeholders, to support key decisions and strengthen HR’s value within the business. Our overviews of the market also enable us to spot opportunities for new products. For example chatAssess – our psychometric communication game which assesses a candidate’s strengths, personality and cognitive abilities – stemmed from two key trends: the move to mobile devices and the growing importance of assessing people in the context of the job for which they’re applying.
Regardless of what your organisation actually does, smart data can provide insights that will 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.More about: Predictive Talent Analytics
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