Data allows us to see where an approach isn’t working, to understand what is working and to identify areas where improvements can be made. It supports innovation and advancement. And it can - and should - support and underpin good decision making.

This is truer than ever in the age of Big Data. The ability to use AI, machine learning and other analytical tools to analyse and sift through increasingly huge quantities of data is quickly becoming fundamental to our working and personal lives.

Nowhere is this more relevant than in work management. Comprehensive and cohesive data allows us to identify areas that need improvement, as well as the methods we can use to implement those improvements. There’s a whole host of sophisticated analytical tools that we can use to identify equipment reliability, ensure performance improvements, and essentially just make things better.

Companies with multiple large assets to manage and maintain have a depth of data that should make it easy for us to implement all these improvements. But it seems that the value of good data, not just any data, has not been properly realised by all that many organisations, especially where there is an unavoidable human element required for data input and capture.

It doesn’t matter how many fancy tools, complex algorithms or vast data lakes are mined, analysed or generated. If the underlying data is rubbish, the output will be rubbish.

I could run a benchmarking study of preventative maintenance (PM) programmes across multiple assets across an industry. I could see that Asset A, has 30,000 hrs of PM work, whereas Asset B has only 23,000 hrs of PM work. Naturally, I’m going to say then that Asset B is much more efficient than Asset A, given that it’s had to spend fewer hours on preventative maintenance.

But hang on. What if the quality of both Assets data was rubbish? What if the hours allocated to the PM programme was based on best guess by those who created the data, and the workforce didn’t update any hours that jobs actually took because they thought that if it looked like they did things faster they might lose their jobs? How much would you trust what the data is telling you then?

Poor data management can lead to wrong interpretation of information, which in turn can end up costing your assets more - either in the short term with too many resources, or long term with not enough time or resources for the maintenance required. More often than not, it seems that an organisation’s response to this isn’t to improve their data management, but to downplay the importance of the data they’re collecting. If you don’t treat your data as important, then mis-managing your data isn’t really a problem to you.

Organisations, especially those that are asset-intensive, need to recognise the value of the data they hold. Not only that, but they also need to find a way to impart that value to their workforce, so that the quality of the data collection and management is as high as possible.

Optimising your data management is important – that’s been established. An additional 5 – 10 minutes a day of good quality data input by a technician can reap huge rewards in terms of output and improving workflow.

Let’s go back to a quick example. A certain PM activity is shown to require two men for four hours, for a total of eight hours. The two men get the job done very efficiently, and it turns out it only took them three hours, which saves the organisation two man hours.

If this data is logged, analysed and acted upon, then the next time the PM activity comes around you know it only requires six hours, and the other two hours can be spent elsewhere. You’ve saved time, and used that important data to streamline your processes.

If the data isn’t logged, then every time the PM activity needs to be done, you’re effectively losing out on man hours, as well as de-incentivising your workforce to complete jobs efficiently. Or even worse, because you’re guess-timating how long it takes, perhaps next time you only give them two hours to complete the activity, leading to frustration, corner-cutting, and incomplete jobs.

It takes just a few minutes for this data to be captured and changed for next time. All because of good quality data capture where management and technicians that were incentivised to capture the change.

While you’re working hard to collect and analyse your data, is your data working hard for you? Could elements of your data collection be automated, cutting down on human error? Are there ways to incentivise your workforce to be enthusiastic about capturing high quality and complete data sets before, during and after work execution?

Taking the time to get on top of your data management is crucial to streamlining your work environment, as well as highlighting possible problem areas before they become too serious.

In today’s climate of data-driven decision making, for an organisation not to work their hardest to ensure good-quality data is baffling – and for a workforce not to recognise they have a part to play and will benefit from good quality data is equally baffling. More and more often, any work management role will require experience in ensuring data quality.

Just as top-quality data can streamline your business and maximise your output, so poor-quality data can lose you money, delay and damage your workflow, and cause problems on a daily basis. Increasingly, companies are putting the emphasis on nailing their data management, asking companies like us to come in and lend our expertise to making improvements and changes. We’ve seen first-hand the difference that a valued, good quality data management system can make, transforming a struggling organisation into a hugely successful one.

So put your data to work for you. View it as valuable rather than voluntary, and use it to transform your processes. It’s time that good data was embraced by all, to drive the work management improvements that are really needed.

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