Recently, a local manufacturing company had a problem with a high level of defects affecting one of its products, which was fragile. Even after the company improved the quality of the product so that defects were eliminated in the factory, many of the products still got broken before they reached the point of sale. One day, a member of the early-morning cleaning staff realized that a train passing at 7am was shaking the factory and the products, making them more vulnerable to breakage. The problem was solved on the basis of input from human capital.
The value of data analytics is that it helps companies to filter through the noise created by volumes of data and find these gems of wisdom that help them to work smarter. The insights gained from data analytics matter to decision-makers at all levels in an organisation. Surveys and assessments, feedback forums and other avenues can generate volumes of data. Analytics provides the filter for finding patterns and deriving clarity.
For example, I am working with another company that conducted an employee survey and a client satisfaction survey. I encouraged the company’s managers to look at both surveys together, because many of the issues may be related and it makes sense to look for commonalities. I helped them to look for the value-adding activities that contribute to satisfaction and retention, on the part of employees and customers.
Employees may be unsatisfied because they don’t have the training or skills to serve customers appropriately, and the customer may not be satisfied with the level of service that they are getting.
For a CEO in one of the big companies, his biggest headache is financial management among his sales force. His managers get calls from the sales team when they run out of bus fare. We talked to him about raising the bar and improving financial acumen among the sales team, so that they can work more productively and in a more sophisticated way.
The company is now using a learning management system to provide relevant training and the outcome has been a sales force more capable of managing themselves. They can also talk more intelligently with their clients. This is working to improve retention among the sales force, as well as delivering greater sales and satisfaction to customers.
Workforce planning and retention are common challenges for companies in Kenya’s CIPS sector. Retail companies, manufacturers and agribusinesses tend to employ large numbers of people at any given time. These types of companies may have two workforces, at a basic level. They employ a workforce on a permanent basis and another workforce on a more flexible basis, depending upon distribution channels, weather, seasonal production and many other factors.
For these companies, recruitment, retention and performance assessment benefit from an approach to workforce planning that is data-driven.
Many business growth strategies tend to focus on capital asset investments. Human capital investments are just as important, such as plans to hire a chemical engineer, a financial controller, a team of retail sales associates or lorry drivers. Some investments in human capital will be more high-value than others, and the outputs expected will vary. For an employee who clocks in and clocks out, the number of hours that they work or the output that they produce is measurable. For knowledge workers, the output may be less tangible. An issue that can be missed in both cases is performance measurement.
How do companies assess the value and input of human capital?
CIPS companies can address this question by shifting their focus to planning and measurement. Many company managers assume that the deciding factor for retention is salary. Measuring performance is more challenging and assessing value and input is even harder, but we have found that employees whose performance is measurable and whose value is recognised tend to stick around longer. Some of the common obstacles to measurement and assessment are self-selection bias and systemic bias.
CIPS companies distinguish themselves on the basis of processes, but these processes themselves can derail efforts to gain insight from data. ‘Garbage in, garbage out’ is common IT vernacular for data-driven processes that are fuelled by rubbish data and produce rubbish results. Data-driven organisations know that the processes driving data collection are just as important as the analytics-enabled filters that deliver insight. This is just as true for assessing human resources as it is for measuring client satisfaction, and the benefit is often improved retention.
Handerson Mwandembo is a Consultant with PwC Kenya’s Technology Advisory practice.