There is a lot of data around these days. Global data volumes are now measured by the zettabyte – that’s one with 21 zeros after it. The average smart phone holds more data than a 20-year-old laptop.

Yet lots of data does not necessarily mean it is useful or applied well. It is easy to drown in data and struggle to know what bits matter, what will make a difference and what is just noise.

Businesses have made giant strides in how they use customer data. Companies such as Tesco and Boots have created game-changing insights from their loyalty card data and even monetised sharing this with suppliers. Despite the progress in customer data application, there has not been similar progress in how businesses measure their operations.

Operational measures are often high level, giving a top-line view by examining stock turn, salary to sales percentages, margins and colleague churn. It’s not very different to key performance indicators that were used 50 years ago. Given that sluggish productivity is a feature of the UK economy, and others globally, it seems reasonable to expect more progress to have been made.

Business productivity measures are often output measures, such as scan rate for a colleague on a till and case rates for colleagues handling stock. While these can be a useful indicator of a colleague’s performance that is out of line with their peers, they do not give any indication of why there are differences and what the optimum rate is to balance speed and quality. It is no good being super speedy with cases of ripe fruit or delicate china and damaging the stock as you go.

So, if you want to use operational measures to make data-driven decisions in your business, what can you do?

Process measurement started in the middle of the 20th century. Often known as time and motion study, it was first applied to production lines. It is usually associated with the super-efficient car factories in Japan, yet was first widely used in manufacturing in the US. Several different techniques were developed to create robust data sample sets that can be mined for actionable insights. These quantify wasted resources, identify productivity improvement opportunities and provide insights on the root causes of problems.

For example, although B&M was on its way to becoming one of the UK’s fastest-growing variety retailers, it used an external team to improve productivity as some of its processes had not kept up. Studies from the external team showed that a disproportionate amount of time was spent refilling shelves, and customer trolleys were being used to move items from the stock room. While this had served the business well in the past, it was starting to become costly in terms of colleague hours as stores became busier.

Along with other tweaks to manual systems they had begun to outgrow, it was recommended that B&M introduce new, more efficient stocking equipment, plus options for pricing and ticketing technology. The process measurement data showed just how many hours all this could save.

For customer-facing businesses, quiet periods at tills and counters can be hard to avoid, yet they can have a significant impact on your overall efficiency. Data analytics that quantify downtime are always insightful for businesses.

Screwfix, for example, has separate till areas for trade and retail customers, looked after by colleagues trained to serve one or the other. Measurement showed that, at times, the trade sections were busy while retail was empty and vice versa. But because of the division of skills, colleagues weren’t able to switch over and help out. Management was surprised by the amount of downtime it caused, and although there is no quick fix – recommendations were to either multiskill colleagues or introduce more digital kiosks – the analytics Screwfix was given will help inform decisions around its long-term strategy.

Travelodge had an issue with quiet periods in its hotel receptions. It wanted to investigate whether there was a way to reduce downtime when no customers required help. It was discovered that teams in different hotels had developed their own methods for filling that time, from finding small jobs they could complete in situ to spending short periods away from the front desk. Data analytics gave head office the information it needed to trial different approaches and therefore inform its customer strategy.

You may not have heard of Parkinson’s Law, but chances are you’ll know the proverb that defines it: “work expands to fill available time”. It underpins research into client efficiency. If a team is performing at a less than ideal speed, it is a sign that it is not busy enough and could be costing the business money. That is why pace measurement is something that I recommend as part of all studies. It does not just help to build an accurate picture of our efficiency, it also indicates contributing factors and opportunities.

A pace dip is often seen when people aren’t getting enough direction from their managers, there are not enough supervisors present to motivate teams or there are too many team members on a shift. And it can have big consequences. Whenever colleagues are taking longer than necessary to complete a task, you have to overinvest your salary budget to get the job done.

Pace measurement at B&M and household goods store Wilko showed that although they benchmarked well to similar businesses for overall efficiency, pace-rating studies highlighted areas of work that fell well below expectation. Tackling these underlying issues could therefore save them money.

For both retailers, analysis showed that colleagues were using stock replenishment as a way of filling time, and consequently identified significant opportunities to pick up the pace. It was a particular issue in the evenings for Wilko, when supervisors were thinner on the ground and there was less of an obvious need to move onto another task.

Time and motion measurement across multiple sites also provides rich insight into consistency, or lack of it, across the business estate. Ironing out process variance and identifying best practice are typical outcomes of these detailed operational measures.

Operational efficiency data collection and measurement can provide meaningful insights into the operation and quantify opportunities to improve productivity and efficiency. The first challenge is to design a study and apply a methodology that gives you the right data set to work with. The next is to know how to interpret the results in a way that relates to practical aspects of how the business works.

Presenting decision-makers with dull data sets is never going to stimulate change – the trick is to present analytics in a way that is visually engaging and clearly shows the insights. And if you have got industry or competitor benchmarks to kindle the competitive business spirit, that is good too.

It takes a combination of time and motion measurement know-how and operational understanding to connect the data to value-adding insight. In many businesses a productivity manager is sitting on a goldmine of meaningful measurement and productivity opportunity insights. Find yours – they will be pleased to help you make better data-driven decisions.

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• Business & Management magazine, Issue 281, February 2020 (pp.24-25)

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Further reading on data-driven decision making in business is available through the resources below.