We Used Data Analytics on Azure to Help our Global Mining Customer acheive Millions of Dollars in ROI

Executive Summary

vNEXT led the delivery of a modern data platform on Azure for our Global Mining customer, which helped in achieving millions of dollars in ROI. We helped our customer envisige the future, articulate the benefits of modernising their data platform, and helped them build a data culture that is powered by a self-service approach for data and reporting dashboards. 

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Industry Mining
Expertise Data Analytics, Machine Learing, Big Data
Technologies Azure Databricks, Azure Synapse Analytics, Azure SQL Database, Azure Data Factory, Azure Key Vault, Power BI, and Azure DevOps

Scenario

Our customer is a global resources company that mines and explores base metal resources around the world. They have five large mining sites across multiple continents including Australia, Africa, and South America.

Our customer like most organisations have IT systems that are managed by the Global Digital team, and several Operational Technology (OT) systems that are managed by the OT team on each of the mining sites. Each of these systems (in IT and OT environments) have large volumes of data that is critical to the operations of the organisation.

The OT team uses its local systems to drive its daily BAU operations, and they rely on their experience from historical events to optimise their operations. The Global IT team does not have clear view of the complex systems, their data estate, not to mention an easy access to these data sources.

Key Issues

The fragmentation of the data assets of our customer, and the siloed nature of their mining sites have made it difficult for their staff to do their work. Having to pull data on ad-hoc basis from the mining sites and store large volumes of data on local drive to inform planning, and exploration initiatives was not practical.

Our customer wanted the ability to bring their data together into a central hub to improve data accessibility, data integrity, and governance. The team also wanted to move beyond BAU reporting and into the realm of predictive analytics to improve safety and efficiency.

The ultimate goal was to help the organisation in creating a data culture where personnel can self-serve their data and their reports to inform and enhance the outcome of their work.

Solution

  • We worked collaboratively with our customer to define 30+ Use cases that can have an investment return of > $80M. Each of these use cases was broken down to small projects each with estimated ROI, datasets needed, complexity to achieve, and any other requirements like retraining of staff.
  • We worked with our customer to put a proposal for an initial foundation engagement to cover 2 of the use cases. This helped in lighting up the services, setting up the foundation, and delivering outcomes in the form of 2 Proof-of-Concepts (PoC) for 2 of the initiatives from the list.
  • The positive feedback from the metallurgist, the engineers, and data analysts was very encouraging. The delivery of the initial use cases sparked the imagination of the wider community within the organisation of what can be done with data analytics.
  • The next phase was to productionise the modern data platform to enable a self-service capability for data and reports. The solution was powered by Azure Data Lake Storage Gen2, Databricks, and Power BI.
  • Governance was another issue that our customer was concerned about. Considering the distributed nature of our customer’s business, we opted for a democratic model of governance rather than central. We helped our customer by setting up a Centre of Excellence for Data and Analytic and a Community of Practice. These two bodies were open to all staff from all sites and they oversaw the formation and implementation of the organisation’s data governance framework.

vNEXT collaborative and agile approach helped the customer in imagining the future and defining practical and achievable use cases. This was critical to demonstrating value quickly, building confidence in the new platform and central to the success of this transformation journey.

Outcome

Within a few weeks, our customer was able to start with a modern data platform and the beginnings of a data-driven culture. The delivery of the initial prototype was critical in building confidence and demonstrating value from data analytics. Our approach  enabled the customer to bring together different teams and departments to take a collaborative and active role in shaping the future. The solution delivered:

  • A modern data platform supporting the operations of the organisation as well as future-proofing it. The new data platform continuously ingests data from key systems including PI Historian, SAP, SharePoint, Fleet Management, and other systems.
  • Significantly improved data accessibility and data governance. Organisations that do not have a clear view of what data they have, where they have it, and its integrity level are destined to fail.
  • Improved compliance by having the planning and exploration initiatives use reproducible datasets for their work. These datasets were generated from the organisation’s central data hub.
  • Unlocked the potential for many more projects to use data analytics to improve efficiency, reduce down time, and shift from prescriptive to predictive operational model
  • Improved quality control through a centralised and automatic data import and transformation process.
  • Increased efficiency through better automation and integration, freeing up team resources for other projects.
  • We started by focusing on enabling our customer staff to have access to the data they need quickly and easily to enable them to discover the different data sources that they have. However, soon after we built the platform, we started seeing more benefits of the modern data platform. One of these was the Intelligent Digital Assistant that we developed and deployed to our customer’s Microsoft Teams tenant. The Digital Assistant has many capabilities to help answer employees’ questions quickly and easily in a conversational interface.

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