There’s been lots of talk about big data and what it means, both as a concept and in practice. For the cleaning and FM industry, there are some tremendous advantages for big data, and this will look at some of them.

Big data: a definition

Big data is defined as “high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.”  Data comes in all kinds of formats, at different speeds, from different users, and in different timeframes. Big data refers to the very fact that there are so many ways data can be collected nowadays, that we must be able to use it effectively. According to a new report by Market Research Reports, “more data has been created since 2014 than in its entire previous history. It is estimated that by the year 2020, more than 1.5 megabytes (MB) of new information will be generated every second for every person across the world.”

Big data in the cleaning industry

But what does this mean for the cleaning industry? First, let’s break down data management into three simple steps: collection, analysis, and storage.

Data collection can be taken at virtually any point in the company’s supply chain and customer journey. From website visits to staff checking in times, and client retention to spares management. The data collected must mean something to the company. Is it worth investing in calculating your sustainability when it’s not part of the company strategy?

Once this data is collected in its various forms, it needs to be analysed, and this is where big data has its own set of issues- the sheer volume of data in real time means data needs to be prioritised to action can be taken immediately to manage any issues as they arise, or to proactively improve business processes. Machine learning and AI can help here by learning what data to use and when to use it to the best effect.

In the cleaning industry – everything can be a dataset. Imagine a staff member logging in for an evening shift. They log in every night at 7pm. Some nights they work an hour, some nights 50 minutes. You can assess this data against other staff at similar sites (or even the same site) and see if there is an element of training that is needed or if their round needs clarifying. But is this a good way to spend time and money? It’s the same with all kinds of data.

For example, marketing: you’ve got a social media presence and you have an active social media engagement process. Do you analyse all the data coming from this? If not, why not? If so, how often? Does it lead to actual sales or increased brand recognition?

For the last part, storing vast amounts of data needs bigger storage platforms, whether in the cloud or not. Data storage will always be an issue, but as the variety of data increases, so the platform of storage must be taken into consideration.

In conclusion, big data offers a way to see the needle in the haystack by getting straight to the nitty-gritty. But you need to know what data is useful, what data is needed real-time, and what data can be collected in the future that will make a difference? Furthermore, how can we train our staff to collect, utilise and manage their own data successfully?

To request more information on how big data can make a difference to your organisation, email me at We can do reports, workshops and training for your use.