HomeBase MetalsEffective risk management requires good information

Effective risk management requires good information

Like most responsible mining companies, Vale invested heavily in data collection and management, yet the CEO still faces charges of homicide by the Brazilian government, arising from the Brumadinho Dam disaster – a risk that could have been prevented.

With ever more data available to decision makers in the industry – sensors, trackers, the Internet of Things, cloud data compilation and so on –  there is unprecedented amounts of time, effort and money being invested in delivering technical solutions on ‘how’ to monitor the data – yet we’re still getting it wrong.

This article first appeared in Mining Review Africa Issue 11, 2020
Read the full digimag here or subscribe to receive a print copy here

This is because much less investment is currently going into ‘what’ or ‘why’ companies are collecting and managing data. The result is a selection of highly technical and expensive solutions that do not necessarily enable risks to be successfully identified and mitigated, and for opportunities to be exploited.

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Therefore, they do not fulfil their potential of contributing to quality decision making or profit. Instead, the resultant over complication runs the risk of drowning organisations in a data deluge.

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This, the first of a two-part article in a three part series that unpacks how to more effectively manage some of the foremost risks in the mining sector, argues that using data to manage risk and make good decisions is not about just wiring up the enterprise. 

It’s about understanding why you’re doing it and what it’s all for.  It draws upon hard-learned lessons from the financial sector and the battlefield – where failure to maintain the information edge – has directly impacted on profit and lives. 

It starts with the premise that decision makers must clearly articulate their critical information requirements and only collect the data they need.

This should then be followed by the appointment of professionals that understand the operating context to ensure that the data is effectively processed, most likely using artificial intelligence (AI) and machine learning, to deliver relevant and assessed information that enables effective decision-making.

Separating the wheat from the chaff

The adage “All that glitters is not gold” is particularly pertinent with information. Decision makers and data providers can often fixate on the source and presentation of data rather than its “true worth,” it’s contribution to profitability.

This is not new. Hugh Trevor-Roper, one of the greatest intelligencers of World War II, said that his strategic decision makers displayed a misplaced preference for “…some nonsense smuggled out of Sofia in the fly-buttons of a vagabond Romanian pimp to what they could learn from a prudent reading of the foreign press”.

The modern manifestation is a fixation with plasma screens, the latest technical cutting-edge capability, sensors, drones and trackers.

This is not to say that technical cutting-edge capability does not have a part to play; its role is critical. However, decision makers must start with ‘The What?’ in order to minimise risk and maximise output, not ‘The How?’ as is currently being done.

The lure of technology can come later. The financial sector learnt this lesson after the 2008 global financial crisis, where a lack of understanding of the aggregated effect of subprime lending, housing bubble, deregulation, and so on, led to a catastrophic mis-appreciation of risk.

Likewise, the military in Afghanistan discovered halfway through a campaign that was leaching blood and treasure that their intelligence organisation was, according to the seminal report Fixing Intel, “token and ineffective”. 

In both cases, the problem wasn’t a lack of data – quite the opposite. Afghanistan was the first digitalised battlespace; even by the late 1980s the finance sector was largely a digital industry. In both cases, there were two flaws; a lack of analysis, which we will discuss in the next article, and a lack of direction.

Direction is about a decision maker clearly articulating the requirement. What is really needed for effective decision making? These requirements must relate to an opportunity that the organisation is able to exploit or to a threat that it is able to mitigate.

Too often they do not, and therefore are meaningless to the business, incur unnecessary costs, create data deluge, with its attendant confusion, poor decisions, poor risk appreciation and management and a hit on output.

It is therefore imperative that setting the requirement is not driven by technology or the sources of available information, however seductive they may be.

Often, having the technical service providers in the room at this stage only serves to offer them an opportunity to over sell their capabilities, which incurs unnecessary costs and creates data deluge.

This is why the financial sector and the military both now employ specialists who lead the process from the direction and requirement setting, right  through collection, analysis and presentation – very closely supported by technology, but not driven by it.

Once clear requirements have been set, the business can look at ‘The How’ – how it is going to collect the data. Technology plays a part, but – as the financial sector and the military have found – it isn’t all about sensors and satellites.

People are always important.  Social trends, the movement of populations, even animals, may be just as important to a business’ social licence to operate as HSE and asset tracking. Collection is about working out how to get sufficiently reliable and timely data that meets the decision-making requirement as quickly and simply as possible – maximising its utility.

As the US military in Afghanistan found, despite its vast technical intelligence apparatus, it was “unable to answer fundamental questions about the environment in which US and allied forces operate and the people they seek to persuade” (Fixing Intel). The same might be said in the case of many recent difficulties in mining across the globe.

So, careful consideration must be given to the balance of technical sources and old-fashioned community engagement, social and social-media interactions, profiles and presences to make sure the business is collecting the data that it actually needs in the most efficient way possible – with emphasis on the collection of only the data that it really needs.

In the next article, we will look at the challenges of bringing diverse data sources together, analysing and presenting the output to the decision maker and how that should, in turn, drive a review of requirements – the blueprint for a more cost-effective way of making decisions and managing risk.


Simon Barry

Barry holds a MSc in Risk Management from the University of Leicester and is a specialist member of the UK Institute of Risk Management and an ISO 9001 lead auditor.

With extensive experience in aviation, logistics and management development he is a firm proponent of the team-based integrated approach to problem solving, addressing the hard questions early.

Hedley Tomlyn

Tomlyn is a former military intelligence officer and diplomat with wide-ranging experience working with multi-national and boutique consultancies.

He is the Director of Programmes at Minerva Advisory Group.