Canadian Mining Journal

Feature

Mining for measurable value from data

EY's Theo Yameogo explains how process mining can help miners get measurable value from data



Monetary or not, many sectors have been able to attach value to the data they collect and use. Search engines can put a value on data because they sell it to organizations. Telecoms can put a value on data because they can share a user’s location. Retailers can monetize data because they can generate sales from targeted advertisements. But mining and metals organizations have yet to really put a value on data.

Generating measurable value from data remains an untapped opportunity for the sector. Digital and data optimization was, again, one of the EY Top 10 business risks and opportunities for 2020. It’s not that mining and metals companies aren’t becoming digital. They’re simply stalled on how to take the next steps. We hear frequently from clients that obtaining the right data and making it actionable are critical components to unlocking value from digital investments. Although we’ve seen great strideswith success stories such as digital twins and integrated operations centresnow is the time to shift focus. Companies need to better manage the data from these business models and processes to extract maximum value.

The rst step is having a deep understanding of what data is available, and then differentiating between what data is valuable versus merely transactional. Process mining could be the answer to that.

At a high level, process mining is a set of analysis tools used to understand the actual behaviour and performance of business processes by looking at the transactional data (events) recorded in common IT systems (event logs). This allows companies to analyze the current state of business process performance, identify areas of improvement and assess the results of future process improvements. Therefore, process mining can help predict faulty assets leading to preventative maintenance, identify more efcient routes for haul trucks and optimize train speed from sensors to maximize time from pit to port, among other things.

Although this concept is new for the sector, it is achievable if organizations are committed to developing a data-driven culture. This four-phase approach can help realize data value:

1. Develop a process model baseline. Identify end-to-end, enterprise-wide processes that have been modelled and held in a process model repository or modelling tool. Organizations have traditionally struggled to keep process models updated, however, process mining allows for automated discovery and visualization of process models through analysis and interrogation of transactional data.

2. Process data value chain (current state). Map the process model baseline to all underlying datasets and associate an approximate business value to each to create the process data value chain. This shows the links between processes and the datasets required within those processes.

3. Value chain innovation map. Identify potential innovations across the value chain, enabled by data and realized as process optimizations. By developing use cases using AI, advanced analytics and intelligent automation, organizations can start to visualize, and rapidly understand, key problems or opportunities.

4. Future state process model. Embed value chain innovation into enterprise process models to create “future state process models.” Each data-driven innovation can be mapped to an existing step in the process. This highlights the potential for automation, or for using advanced analytics to transform decision making through more accurate and earlier predictions.

Process mining can dene and quantify the internal value of data, but also identify opportunities for data to drive innovation over the long-term providing executive management with a clear view of what data is valuable and where investments should be made.


THEO YAMEOGO is the EY Canada Mining & Metals co-leader. He is based in Toronto. For more information on digital and data optimization, visit www.ey.com/miningrisks.


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