Article 1: Challenges and Trends
The mining industry today is, along with the rest of the world, grappling with a number of challenges, including recovering from the effects of a worldwide pandemic and responding to ongoing climate change and potential de-globalization.
These are big challenges, made even bigger by trends specific to our industry, such as:
- dramatically increasing global demand for diminishing and often lower-grade mineral resources
- the worldwide shortage of experienced geoscientists
- high employee turnover resulting in the loss of corporate knowledge and continuity, and
- growing lack of collaboration between people and departments as a consequence of COVID-19 travel restrictions and too many Zoom meetings, resulting in a return to silos and reduced innovation.
And then there is the cherry on top: the burgeoning challenge of big data and big data management.
Big data
Big data used to be measured in terabytes; now it’s measured in petabytes.
Mining professionals today are inundated with so much data — geoscientific data, asset condition data, operational data — that they simply don’t know what to do with it all and often do not have the tools they need to analyse it.
This glut of un-analysed data means mining companies are not getting the full value of the data they are accumulating, nor are they basing their decisions on the best information possible. For example, without a full set of analytical tools, it is difficult for geoscientists to identify the correct trends in the data they have at hand, so decisions are made without taking into consideration all the geological complexities that are now visible through advanced data collection. This has a ripple effect all the way down the value chain, from exploration to resource extraction and even end of life.
Big data management and big data analysis
Big data management (BDM) that includes big data analysis (BDA) is a way for mining companies to deal with multiple challenges at once — from being able to respond to worldwide or mine-specific challenges with evidence-based decisions, to reconnecting departments for improved collaboration and shared understanding, and ensuring knowledge is retained through multiple staff changes.
BDM deals with data collection, pre-processing, storage, and sharing, which are the essential foundation for BDA, which is a tool for interpretating data and developing new insights.
BDM
For big data to be useful, it must be collected efficiently and then processed effectively so that it is clean and reliable. It must also be organised so that it is only available to authorised staff, in its most recent version, and stored so that it is easy to access and share.
Today, with the advent of the cloud, data storage is not limited by a mine’s physical space and mines can decide exactly what kind of storage they need for their data they have collected:
- hot storage for data that mine staff need to access frequently and quickly
- warm storage for data that mine staff need to access less frequently, such as data needed for reporting or analytics, or
- cold storage for data mine staff only occasionally need to access.
BDA
Once big data is cleaned, sorted, formatted, and stored in a way that is convenient to users, it is ready to perform tasks aided by automation or algorithms. The Gartner business advisory group predicts that, “by 2025, 95% of decisions that currently use data will be at least partly automated.”
Even in a complicated industry like mining, which depends on uncontrollable factors like geology and weather as well as science and technology and is bound by strict safety and environmental regulations, data analytics can be immensely valuable.
BDA can, for example, develop accurate models predicting both rock type and the economic feasibility of drilling in specific locations during the resource estimation phase, or later, during the operational phase, analyse a huge amount of data (far more than a human could) to accurately predict when a piece of equipment or machinery could fail, or determine how best to minimise truck queue and hang time.
Next in this series
This series of posts is intended to help mining companies plan a pathway to employing BDM and BDA to improve their mining intelligence by fully utilising the vast quantity of data available to them.
The following articles will discuss data challenges and solutions for developing specific types of intelligence — specifically exploration, geology, production, and caving intelligence — and provide a little insight into the future of mining intelligence solutions.
