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Thriving in the Age of Digital Adoption: Embracing the Workforce Ecosystem (part 2)

In the first part of this series, we looked at how the fears of technological innovation are resulting in an unproductive resistance toward modernisation, even as it gains extraordinary pace in 2021. We also delved into the importance of a growth mindset in allowing...

Thriving in the Age of Digital Adoption: Overcoming the Fear of AI (part 1)

“What if artificial intelligence takes over my job? What if I become redundant?” Every one of us has experienced technology encroaching on our lives, more and more so with each year that passes. It appears that technological innovation is a certainty that is only...

Starting & Thriving in E-Commerce in South Africa: The Payment

In our previous article, Starting & Thriving in E-commerce in South Africa: The Customer, we looked at a few variables that affect the customer’s experience with a business; these include how you can build valuable information about and around your...

Starting & Thriving in E-commerce in South Africa: The Customer

In our previous article, Starting & Thriving in E-commerce in South Africa: The Foundations, we explored the various options and requirements for taking your business online as well as the importance of meeting your audience in the places they naturally...

Starting & Thriving in E-commerce in South Africa: The Foundations

The COVID-19 pandemic has transformed our lives in a number of drastic ways. While some big corporations struggled to remain relevant in the shape-shifting business climate around them, many start-ups have found their time to shine and are performing remarkably well....

Leveraging data-driven agility in an agile world

At MAC we have recently been having discussions about the concept of Data Driven Agility; how can organisations start to leverage data more intelligently in an agile-delivery process, select data which is fit-for-purpose and, importantly, help solve business problems?...

What is a data journalist and why do you need one?

One of the biggest pain points in many organisations – although they might not even be aware of it – is the disconnect that exists between the data analytics department and the other business units. Despite the concept of a data journalist not being well recognised in...

How to get people onboard your data journey to drive usage and adoption

In the preceding articles we have covered topics on building the correct foundations so as to implement data analytics in an organisation with a centralised data function. Using the Sit. Crawl. Walk. Run. Paradigm, we explored how best to embark on a data journey and...

How to get people onboard your data journey to drive usage and adoption

In the preceding articles we have covered topics on building the correct foundations so as to implement data analytics in an organisation with a centralised data function. Using the Sit. Crawl. Walk. Run. Paradigm, we explored how best to embark on a data journey and...

The Trust Dilemma – how to build trust in Data

In Computer Science, garbage in, garbage out (GIGO), is the concept that flawed inputs will yield flawed outputs, or ‘garbage’. This principle applies to all analysis and logic, in that arguments are unsound if their premises are flawed. In data analytics, it is a...



Mac Consulting

Leveraging Data to develop a competitive advantage

Leveraging Data to develop a competitive advantage

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.

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