In 2012, a Harvard Business Review article proclaimed that Data Scientist would be the sexiest job of the 21st century. Fast forward 11 years, and this prediction has come true, with true data scientists in short supply and commanding large salaries. These experts help organizations leverage the vast amounts of data accumulating in nearly all industries to improve performance and maintain competitiveness. As Google CEO Sundar Pichai puts it, "The ability to turn data into insights and ultimately into decisions will be the defining competitive advantage of companies in the future."
That's all well and good, but where does a mining company or group begin when trying to harness data for optimization and lasting competitiveness? At MMS, we often borrow from Maslow's hierarchy of needs as a framework to help our clients understand their digital transformation journey towards creating meaningful value from data.
In the mining world, data and artificial intelligence (AI) hold the potential to revolutionize the industry, providing new insights and optimizations to ensure a sustainable, efficient, and profitable future. To achieve this value, we can draw a parallel with Maslow's psychological theory, which outlines the prioritized stages humans must fulfill to achieve self-actualization. Similarly, the mining industry must follow a bottom-up hierarchy to unlock the full potential of data.
This hierarchy consists of five essential layers: 1. Monitoring and Instrumentation, 2. Data Pipeline and Data Warehousing, 3. Data Validation and Cleaning, 4. Data Aggregation, Analytics, and Optimization, and 5. Learning Systems.
1. Collection: Monitoring and Instrumentation lays the Foundation
The first stage, monitoring and instrumentation, is analogous to Maslow's most fundamental physiological needs, such as food, water, and air. In mining, this stage involves establishing monitoring and data collection protocols and installing sensors, devices, and other monitoring equipment to gather raw data. A strategically placed network of data collectors is crucial for providing the necessary information to analyze, optimize, and predict various aspects of mining operations.
How we meet this need at MMS:
Meeting this need is a key strength of MMS, who offer consulting services in digital transformation for mining, metallurgy, metal management, metal accounting and mass balance reconciliation, among other areas. Key to these functions are data collection, and sampling strategies and methods, and having the data collection parameters and resolutions to meet the business needs.
The consultative process to optimize sampling and data collection methods for business needs and to reconcile partnering with technology solutions providers requires careful planning, evaluation, and ongoing monitoring to ensure that the methods are effective and aligned with the needs of the business.
2. Data Pipeline and Data Warehousing: Ensuring Stability
The second stage, data pipeline and data warehousing, parallels the need for safety and security in Maslow's hierarchy. A robust data pipeline ensures seamless data flow from instrumentation to storage, enabling accessibility and ease of use. Data warehousing provides a stable foundation for storing and organizing the vast amounts of data generated by mining operations. This stage is vital for maintaining data integrity and allowing AI systems to process and analyze the information effectively.
Traditionally, access to data has been a problem in the mining industry, with data generated in isolated digital silos and unconnected systems, severely limiting decision-making and cross-functional coordination. Data quality is another critical issue, with traditional data collectors not trained to apply business rules for data gaps or detect and adjust for bias in instrumentation. Data errors are also propagated by laborious manual data handling and copies of spreadsheets, with no auditability.
At MMS, we address this need with WIRE, built around a unified unified data warehouse or, as we like to call it, a single source of truth. Data is the foundation of all insight tools, reporting functions, and business intelligence that WIRE delivers, so we take data management very seriously. WIRE implementations use various integrations to automate data flows as much as possible, ideally through API integrations. WIRE applies business rules to data gaps and limits, and structures relational and time series data for rapid retrieval in an organized data warehouse. Manual workflows, where necessary, are also digitized and guard-railed for data quality. Our clients' data warehouse becomes a powerful asset, offering flexible and enduring benefits for business improvement. Data warehousing solutions are securely hosted, either on premises, or in the cloud.
3. Data Validation and Cleaning: Fostering Trust
Show your data some love and attention! Data validation and cleaning align with the need for love and belonging in Maslow's hierarchy. Just as humans require trust and acceptance in relationships, business intelligence systems depend on reliable and accurate data to function effectively. Data validation ensures that collected data meets specific quality and governance criteria, while data cleaning identifies and corrects inconsistencies, gaps, errors, and inaccuracies. This process is vital for generating accurate and meaningful insights.
This need is addressed by MMS's domain expertise, enabling the effective establishment of defensible techniques to handle data gaps and the creation of business rules that govern data handling, exceedances, or bias detection from instrumentation.
WIRE's auditability and traceability features offer users visibility into any changes made to data, calculations, and reports. The system's audit trail and version control mechanisms enable users to identify who modified specific data points, calculations, reports, and more. All changes made to the system are tracked and readily available for review. WIRE's role-based access allows only authorized users to make changes to mapped data, formulas, or reports, and all these changes are recorded for audit purposes.
Administrators can lock down historical data once the final sign-off has been given. This prevents users from changing historical data intentionally or accidentally. Audit reports can be stored on WIRE for future reference and external audits, ensuring that our clients meet governance standards and industry codes, such as AMIRA P754.
4. Data Aggregation, Analytics, and Optimization: Reaching for Excellence
The fourth stage, data aggregation, analytics, and optimization. In this stage, various data sources are combined and analyzed to generate actionable insights. Optimization algorithms can then be applied to improve mining processes, reduce costs, and increase efficiency. This stage unveils the true value of data and realizes the benefits of digital transformation.
The time to value for our clients is brief for this tier of benefits, typically only 3-6 months. Our team of engineers excel at transforming trusted data into insights through our no-code customizable IoT platform, WIRE, which offers performance dashboards, reporting, tools for insight and root cause analysis, and optimization and decision support functions like the advanced mass balance solver for mineral processing.
5. Learning Systems: Achieving Self-Actualization
Ultimately, learning systems represent the pinnacle of the hierarchy – self-actualization in Maslow's terms. In this stage, AI systems become adaptive, learning from historical data and continually enhancing their performance. These systems can predict and respond to changes in the mining environment, optimize processes, and inform decision-making in real-time. This advanced capability is the ultimate goal for AI in mining, enabling organizations to achieve operational excellence and unlock unparalleled value.
MMS-implemented data warehouses serve as a single source of truth, ready for machine learning. It is crucial to align the key objectives of machine learning model development and training with the fitness-for-purpose of the available data. While this is an exciting (and inevitable) area for benefits realization in many industries, domain expertise and data science experience are essential to actualize AI and intelligent mining and to avoid costly "white elephant" ML projects.
We believe that AI may be oversold in mining in the coming years unless there is proper progression through a needs hierarchy. In the mining production value chain, true digital transformation and preparedness for the future of work require the intersection of domain expertise, data engineering, and data science. A consultative process is necessary to balance problem definition, technical feasibility, cost-benefit analysis, and maintainability. MMS possesses the niche domain expertise and technology partners to make the right calls with our clients.
The 6th Element: Best-in-class Support and Infrastructure
There are also Maslow parallels that demonstrate the importance of meeting user requirements and creating a positive user experience. By understanding the different levels of needs and meeting them through effective software application support, software support teams help to build user engagement, promote user satisfaction, and support the growth and development of users:
The most basic needs in the data beneficiation hierarchy are reliable and stable infrastructure, adequate processing power, and sufficient storage capacity. These needs must be met to ensure that the software application can operate effectively and efficiently. MMS’ world-class software application support team responds promptly to user inquiries and requests for assistance. Support requests are acknowledged quickly, and issues are resolved in a timely manner to minimize downtime and ensure that users can continue to use the software effectively. The MMS support function is flexible and responsive to user needs, adapting to changing requirements and adjusting support processes as needed. This may involve providing customized support services, developing new solutions to address emerging issues, or adapting support processes to better align with user needs.
Safety needs in the Maslow hierarchy involve physical safety, stability, and security. Similarly, our users require security measures to protect against threats such as data breaches, malware, and hacking. Security measures such as firewalls, antivirus software, and encryption help to maintain the safety and security of WIRE.
In the context of WIRE application support, user engagement and building relationships with users, support teams communicate effectively, provide personalised support, and build relationships with users to help create a sense of community and foster user engagement. Communication is clear and effective with users, providing clear and concise instructions, updates, and feedback throughout the support process. This includes explaining technical issues in non-technical terms, and providing regular updates on issue resolution progress.
Esteem needs in the Maslow hierarchy context involve user satisfaction. WIRE meets user needs, is easy to use, and provides a positive user experience to help build user confidence and promote user satisfaction. The MMS support team has deep technical expertise in software application and related technologies. Support staff also have a thorough understanding of the software's features, functionality, and underlying technologies, as well as the ability to troubleshoot and resolve complex issues. This is achieved through proactive monitoring and identifying potential issues before they become critical. This may involve regularly reviewing logs, monitoring system performance, and performing routine maintenance tasks to keep WIRE running smoothly.
WIRE support helps users achieve their potential through innovation and advanced features, encourage creativity, provide opportunities for growth and development, and help users achieve their goals in line with business strategies. This is achieved through a commitment to continuous improvement, regularly reviewing support processes and seeking feedback from users to identify opportunities for improvement. This may involve implementing new tools or technologies, developing new support processes, or investing in training and development for support staff.
Conclusion
Just as Maslow's Hierarchy of Needs offers a roadmap for actualisation of human potential, the five-stage hierarchy for data beneficiation in mining provides a structured approach to unlock data and technology's full potential. By focusing on each stage – from establishing a solid foundation with data collection to achieving advanced capabilities with machine learning systems – mining companies can leverage data to optimize operations, reduce costs, and drive sustainable growth and competitiveness in the industry.
The world class MMS support team is dedicated to providing high-quality, responsive, and effective support services to users, with a focus on continuous improvement and a commitment to meeting the evolving needs of users and the business.