Insights into Mining Issue 12: Unlocking the value of D&A Data and Analytics (D&A) increasingly shapes our world. The use of advanced analytics is enabling better and faster business decisions, which is driving companies across all sectors to rapid investment in analytics technology and capability. Mining companies are becoming increasingly aware of the important role data generated by information technology and operational technology systems is playing in ensuring the improvement and success of their day-to-day operations. In order to gain a competitive advantage, mining companies are becoming more inclined to utilize data and analytics to unlock value for their organizations. D&A can provide a number of benefits to the mining sector: It can help assess risks and focus resources Provide greater insights into mining operations Provide information about operational expenses for more accurate budgeting and forecasting. Utilizing the data generated by Operational Technology (OT) systems (versus traditional, structured data in information technology systems, such as ERPs), mining companies can make more informed decisions about their entire operations. With the environmental, safety and health challenges that the mining industry is facing, there is an increased focus on performing more predictive and prescriptive analytics to enable companies to reduce costs, raise productivity, enhance safety and increase revenue.
2 Insights into Mining Issue 12: Unlocking the value of D&A The evolution of D&A There is an increased need for mining companies to understand their analytics maturity level which will inform their ultimate analytics strategy. There are five categories of analytics: Descriptive analytics Also known as Data Mining, this type of analytics is utilized to perform analysis on historical data so as to provide insights into operations based on past events. These types of analytics are time consuming and provide the least value to companies, however, they may be useful for detecting patterns as well as performing comparisons. Diagnostic analytics Utilized to identify anomalies in historical data and to pinpoint the cause of events or trends. Predictive analytics The most performed type of analytics, it is utilized to forecast possible future trends based on historical data. These enable companies to make more informed business decisions. Prescriptive analytics A valuable type of analytics, although it is the least performed. This type of analytics is utilized to answer specific questions by running data through various simulations so as to come up with the most optimized solution. This type of analytics leverages data, mathematical models as well as various business rules in order to simulate realistic scenarios. Adaptive analytics Adaptive analytics is predictive analysis with a twist statistical models are used to continuously correct identified errors and improve the predictive results in real time. They are adapted realtime as changes occur to ensure timely and relevant results. Looking into the past Looking into the future Adaptive How do we learn? Prescriptive How can we optimize? Value accessible Descriptive What happened? Diagnostic Why did it happen? Predictive What will happen? Describe the ecosystem under consideration using data to identify trends Understand why we get the performance we get and what caused it Model the ecosystem and simulate scenarios to predict likely future outcomes Prescribe actions to take for optimizing performance outcomes Learn from user behaviours to increasingly focus skilled people on exceptions Analytical sophistication
3 Insights into Mining Issue 12: Unlocking the value of D&A D&A trends During the Mining Indaba, KPMG engaged with mining executives on the importance of D&A within their organizations. Questions asked were based on the KPMG International Commissioned Forrester Consulting Survey and examined the power of trust in D&A by exploring organizations capabilities across four anchors of trust. The four anchors of trusted analytics Trust in analytics, like trust in products or people, is often driven by a combination of two things: its perceived trustworthiness and evidence of its actual trustworthiness. Trusted analytics is not a vague concept or theory. At its core are rigorous strategies and processes that aim to maximize trust. Some are well known but challenging, such as improving data quality and protecting data privacy. Others relatively new and undefined in the D&A sphere, such as ethics and integrity. We believe that organizations should take a systematic approach to trust that spans the lifecycle of analytics and is founded on four key anchors of trust: Quality Are the fundamental building blocks of D&A good enough? How well do organizations understand the role of quality in developing and managing tools, data and analytics? Effectiveness Do the analytics work as intended? Can organizations determine the accuracy and utility of the outputs? Integrity Is D&A being used in an acceptable way? How well-aligned is the organization with regulations and ethical principles? Resilience Are long-term operations optimized? How good is the organization at ensuring good governance and security throughout the analytics lifecycle? Integrity Quality Effectiveness Trust Resilience Building Trust in analytics: Breaking the cycle of mistrust in D&A, KPMG International 2016
4 Insights into Mining Issue 12: Unlocking the value of D&A
5 Insights into Mining Issue 12: Unlocking the value of D&A What are mining companies doing in D&A? Mining companies have commenced investigations into the best ways they can optimize their operations, reduce costs and increase revenue: Based on our experience there is an increasing amount of analytics performed by mining companies, mostly being descriptive analytics Data analysts work with business owners to use analytics to solve business problems Optimization of existing technologies and procuring new technologies to provide solutions to the challenges faced Relying on data collected from OT to make more real time decisions. D&A expectations within the mining industry In order to enable mining companies to adequately digitize their operations, they require a Digital Strategy along with a Cyber-Security Strategy to ensure that the data is protected. These strategies need to be governed by the board and driven at an executive level as integral parts of corporate strategy. Companies need to merge Information Technology (IT) and Operational Technology (OT) to put themselves in a position to be better able to perform more holistic advanced analytics across their business functions and mining operations so as to understand the data captured by the OTs. Robust cyber-security measures need to be implemented to ensure that the data is adequately protected. Quality of the data from IT and OT needs to be maintained to ensure that there is a single source of truth. Ownership and accountability of data need to be established, this will ensure that data is always readily available and the analysis that is performed will run quickly in order to provide real-time prescriptive results that can be utilized for decision-making. Mining companies need to embrace the usage of analytical tools to perform advanced analytics on data that is captured across all the operation touch-points. This will enable the company to make prompt real-time decisions that affect their operations and drive efficiencies. More so, mining machines need to be monitored utilizing data from OTs to predictively determine the status of the machines utilized in mining and thus prompt timely maintenance. Quality of the data from IT and OT needs to be maintained to ensure that there is a single source of truth. KPMG gained insight into the usage of D&A within various mining companies: 84% of the mining companies considered D&A to be of high or extreme importance 60% of the mining companies felt that their data governance can be described as informal because, although they are aware of the data governance requirements, little action has been taken to control their data landscape Source: Study conducted at the Mining Indaba on behalf of KPMG, February 2017 Despite regulatory change and uncertainty making the priority list for most mining companies, none of the companies surveyed indicated that they use D&A to monitor market changes or assist with complying with regulatory change. D&A utilization within the mining industry 50% 25% 12.5% 12.5% Improve operational efficiency and increase yield Process and cost efficiencies Maintenance and service of assets Support HR and workplace planning
6 Insights into Mining Issue 12: Unlocking the value of D&A Enhancing D&A in the mining industry There is a need for mining companies to trust Data and Analytics and the value it will add to their operations. The KPMG Data and Analytics survey highlighted that 60% of the organizations were not confident in their Data and Analytics insights and only 42% of the organizations felt they were effectively using D&A. This creates a huge opportunity for the mining industry to begin leveraging more sophisticated D&A techniques. However, the digitization stewards need to be aware that a prerequisite to seizing value from D&A initiatives is understanding their company s corporate strategy and aligning it to the D&A strategy, tools, capabilities, and data to support that goal. In short, they need to have a clear vision of what they want to get out of their digitization program and only then go looking for the data to meet it. The mining companies who want to utilize D&A effectively will need to strengthen the anchors of trust. Building trust in Data and Analytics is not a project, strengthening the anchors of trust is not a one-time exercise or a compliance tick-box. There are no roadmaps for driving trust, no software solutions or perfect answers. However, our D&A survey demonstrates that there are best practices and practical examples that all organizations can consider and adopt. The following are several ideas that may help mining companies create their own approach to building D&A trust: Start with the basics: assess your trust gaps. Undertake an initial assessment to see where trusted analytics are most critical to your business and then focus on those areas. Remember that key risks can often be reduced with some very straightforward changes, such as the use of simple checklists. Create purpose: clarify and align goals. Ensure that the purpose for your data collection and the associated analytics are clearly stated. Make D&A performance and impact measurable. The aims and incentives of the D&A owners should align with the goals of its users and with those who could be affected by it. Lack of clarity around purpose and misalignment of D&A goals can create mistrust, dilute ROI and open the door to inadvertent misuse. Raise awareness: increase internal engagement. Building awareness and understanding of D&A among business users is critical to breaking the cycle of mistrust. Involve key stakeholders and establish multidisciplinary project teams, combining D&A leaders with IT and business stakeholders across different departments. Build expertise: develop an internal D&A culture and capabilities as your first guardian of trust. Your D&A people are critical to being able to elevate the wider understanding of D&A across the organization. Identify gaps and opportunities in your current capabilities, governance, structure and processes. Ensure that you have expertise in analytics quality assurance: experimental design, A B testing and other means of validation. Ultimately, make trust in D&A a core company value.
7 Insights into Mining Issue 12: Unlocking the value of D&A Unlocking the value of D&A in the mining industry While D&A is increasingly becoming a topic of conversation and an area of experimentation at mining companies, its full value is yet to be realized. D&A presents a significant opportunity for mining companies to be able to better address business challenges, including operational efficiency, increased yield, and workforce planning. Lighthouse: KPMG Canada s D&A Centre of Excellence is helping mining companies to expand their approach to data insights and improve their day-to-day operations through the application of machine learning and predictive analytics. Using these advanced analytics techniques, mining companies can forecast optimal maintenance schedules for trucks, tires, and machinery, reducing unknown costs and increasing up-time.
Contact us If you would like to discuss this report in further detail or how D&A may benefit your company, please contact your local KPMG professional. Canada Shreeshant Dabir National Leader, Data & Analytics KPMG in Canada E: sdabir@kpmg.ca Heather Cheeseman Partner, Risk Consulting E: hcheeseman@kpmg.ca Global Jacques Erasmus Partner, Global Head of Mining KPMG South Africa E: jacques.erasmus@kpmg.co.za Kamban Vythilingum Partner, Technology Advisory KPMG South Africa E: kamban.vythilingum@kpmg.co.za Frank Rizzo Partner, Technology Advisory Data and Analytics KPMG South Africa E: frank.rizzo@kpmg.co.za Vaughan Mason Senior Manager, Data and Analytics KPMG South Africa E: vaughan.mason@kpmg.co.za kpmg.ca The information contained herein is of a general nature and is not intended to address the circumstances of any particular individual or entity. Although we endeavor to provide accurate and timely information, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one should act on such information without appropriate professional advice after a thorough examination of the particular situation. 2017 KPMG LLP, a Canadian limited liability partnership and a member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative ( KPMG International ), a Swiss entity. All rights reserved. 17166 The KPMG name and logo are registered trademarks or trademarks of KPMG International.