Not a lot of people have heard of the term ‘dark data’ before; Gartner defines it as the information assets organisations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetising). Despite its name, it is nothing nefarious.

Harnessing this dark data – which is already there – can be used to gain a competitive advantage in the industry, which could be financial, could drive more organisational value, or something intangible, such as leveraging the data to drive employee wellbeing. An organisation has numerous sources of collecting dark data – sales data, logistics data, manufacturing data, human resource data, etc.

An exciting new service offering at MAC, aptly titled Predictive Human Capital Optimisation, leverages dark data to deliver competitive advancement for organisations. MAC’s approach intelligently leverages data and technology to develop predictive and prescriptive solutions that enables Human Capital departments to be more proactive in their approach, as opposed to the typical reactive engagement approach. We partner with organisations by tapping into their dark data, intelligently identifying the gaps between the organisation’s strategic objectives, and the workforces’ wants and needs. Leveraging this data intelligently enables the business to improve employee productivity, reduce staff churn and reduces training costs.

For a recent example, we worked with a large manufacturer which had a thousand-strong sales force frequenting specific retailers and outlets daily. Their existing tracking process was for the sales members to report back on where they had been and what they had sold, however, nobody quantified or double checked this information. We started to leverage this dark data; we tracked the vehicle loggers, found weather patterns in rural areas and overlayed this data over the sales reports. We quickly started to notice that there were times where 20% of the workforce reported to be at a specific store when they didn’t leave their house that day.

We used easily accessible data to drive a competitive advantage, including getting the sales force out to the rural areas before rain was forecast to avoid objectionable driving conditions. The workforce was optimised, sales grew and staff who were conning the company out of money were identified. A lack of efficiencies resulting in less desirable business outcomes and financial implications were identified simply by using data which already existed, but wasn’t used.

As the old adage goes, “you don’t know what you don’t know”, and by inference you cannot leverage what you do not know. The reason we have developed this service offering is to bridge the gap between what I call Desirable Business States – the specific target the organisation wants to achieve – and tying it back to employee wants and needs.

Technical expertise is obviously required to harness dark data. This would fall under the remit of the data team. When utilising a centralised analytics function (as discussed in the first article of this series), one of their initiatives should be to drive innovation in the company and that innovation involves exploring alternative data assets which the organisation can tap into. Start small with a dark data pilot project. Document all lessons learned. Test and use key performance indicators to assess results. Be prepared to iterate on the approach with testing and reassessment. As you learn more, gradually scale up dark data analytics efforts. Dark data is there, it just needs to be exploited.