Digital Transformation in the Mining Industry and Energy Sector: Moving Beyond Legacy Excel Systems for Agile, Scalable Innovation

Breaking Free from Spreadsheets: Agile, Scalable Data-Driven Innovation for Mining & Energy

Contrasting Excel-based Workflows with Modern Digital Solutions - Miniotec — Contrasting Excel-based Workflows with Modern Digital Solutions

The Digital Imperative - An Introduction

Digital transformation in the mining and energy sectors is no longer optional, it is a strategic imperative. These asset-heavy industries have only scratched the surface of what is possible with modern technology. We estimate Mining, for example, to be less than half technologically mature than other industries. Likewise, energy companies have historically relied on legacy tools like Excel spreadsheets to run critical operations. In fact, from our client base, over 80% of businesses use Excel in day-to-day operations, a trend equally common in mining and energy. While familiar, these legacy spreadsheet-based workflows are holding companies back.

From supporting and talking to numerous organisations in these industries, we believe that a more agile, cross-sector approach to tech adoption is essential for mining and energy firms to remain competitive. Proven digital solutions from other verticals, whether it is advanced analytics from manufacturing or automation from logistics, can be adapted with speed and efficiency. Digitalisation can help to resource industries more effectively. Leaders must look beyond piecemeal fixes (such as layering Power Apps over old Excel files) and drive a comprehensive transformation of systems and processes.

Key insights we consider in this thought piece includes:

the urgent digital imperative facing mining and energy;
lessons from adjacent industries that have leapfrogged ahead;
the pitfalls of Excel-centric thinking in industrial environments; and
why low-code quick fixes (like Excel-based Power Apps) will fail to deliver scalable, high-value results.
We then outline strategic pathways forward, practical steps for executives to accelerate modernisation, improve data integrity and unlock new value.

The goal is to present an experienced, reality-based perspective: digital transformation can dramatically improve efficiency, safety and profitability without being alarmist. But it requires bold leadership to embrace the next frontier of innovation....now

The Digital Imperative for Mining and Energy

In mining and energy, the pressure to “do more with less” has never been greater. Commodity price volatility, stringent safety and environmental regulations and the relentless drive for efficiency are redefining how these industries operate. Both sectors have begun to digitise in bits and pieces, adding sensors to equipment, using software for planning, automating certain workflows, but most operations remain far from fully digital. Many frontline workers still rely on paper forms or Excel spreadsheets, struggling with siloed, manual processes even as new tech rolls out in pockets.

Consider mining: historically a technology laggard, it now faces a stark reality. Through our sector interactions, we consider mining is ~ 50% less digitally mature than other industries. This “digital deficit” represents a huge opportunity cost. If miners close the gap by adopting proven technologies from more advanced sectors, they stand to capture substantial gains in productivity, cost reduction and safety. Global mining executives are increasingly recognising this. Internal discussions are shifting from “if we should invest in digital” to "which technologies to adopt and when," because not adopting new tech is no longer a viable option for staying competitive. In short, mining companies are realising that digital transformation is an imperative, not a luxury, but the process of digital adoption is still agonisingly slow.

The energy sector (including oil & gas and power utilities) faces a similar turning point. Like mining, energy has been traditionally slow to adopt new software and digital innovations. But now, the convergence of ageing infrastructure, the transition to renewables and market pressures is accelerating the need for change. Leading consulting research projects that comprehensive digital transformation could cut operating expenses in energy by up to 25% while improving safety, reliability and compliance by 20–40%. These are transformative numbers for an industry with tight margins and high risk profiles.

The message is clear: to thrive in the coming decade, energy companies must modernise legacy systems and leverage data at scale.

Crucially, the digital imperative goes beyond technology for technology’s sake, it is about business value and resilience. Advanced analytics, IoT sensors, AI-driven automation and cloud platforms enable real-time decision-making and predictive capabilities that directly impact the bottom line. For instance, a mining operation with ubiquitous sensors and integrated analytics can predict equipment failures and prevent costly downtime. A power generation company with a modern data platform can optimise fuel use and maintenance schedules far better than one juggling dozens of disconnected Excel files.

Experience has shown that companies embracing digital tools can achieve impressive outcomes.

Another industry consultant found that miners deploying integrated digital systems realised a 20–25% boost in production output, a 30–40% reduction in maintenance costs, and 60% less unplanned downtime can be achieved through data analytics. They also identified significant safety improvements (injury rates down by ~50%). These figures underscore why digital initiatives are mission-critical: they directly translate to higher throughput, lower costs, safer operations and improved sustainability metrics. Mining and energy CEOs are tasked with meeting ambitious ESG targets and productivity goals simultaneously – a feat only achievable by leveraging modern technology.

Unsure how to deploy IIoT solutions? Read this article: "Top 7 Do's and Don'ts for Successful Industrial IoT (IIoT) Deployment and Long-Term Success"

It is clear that the status quo in mining and energy is not sustainable. The digital technology maturity gap relative to other industries means enormous untapped value remains on the table. Forward-thinking leaders in these sectors view digital transformation as the next frontier for value creation, on par with finding a rich new ore body or a major oil discovery. The imperative is clear: invest in robust, scalable digital solutions now or risk falling irreparably behind. The following sections explore how to do so effectively, learning from our experience and others’ successes and avoiding common pitfalls (or what we refer to as 'Digital Scars').

Lessons from Adjacent Industries in Digital Transformation

Mining and energy need not reinvent the wheel on their digital journeys. Other industrial verticals—from manufacturing to transportation, have navigated similar transformations, often with great success. By drawing lessons from these adjacent industries, resource companies can leapfrog costly trial-and-error and adopt proven solutions with speed and efficiency.

Manufacturing, for example, offers a blueprint for data-driven operations. Over the past two decades, leading manufacturers shifted from manual, Excel-based processes to integrated, cloud-based systems. They learned that putting data at the centre of the business unlocks huge efficiency gains. In manufacturing, the push for Industry 4.0 drove adoption of IoT sensors, real-time analytics and automation on the factory floor. These same technologies are readily applicable to mines, oilfields and power plants. A case in point: assembly-line predictive maintenance techniques can be adapted to mineral processing plants or drilling rigs to anticipate equipment failures. Manufacturers also embraced digital twins (virtual models of physical systems) to optimise operations, a practice now emerging in some advanced mines and renewable energy facilities.

Digital Transformation Merging Smart Factory & Open-Pit Mine - Miniotec — Digital Transformation Merging Smart Factory & Open-Pit Mine

Critically, manufacturing’s transformation involved moving beyond spreadsheets. We have evidenced that while Excel was long the “lingua franca” for engineers, it became an obstacle to true automation and agility. Companies that transitioned to centralised, data-centric platforms saw improvements in collaboration, data integrity and decision speed. They no longer depended on a few “chief spreadsheet officers” to run the show. Mining and energy firms can learn from this by leaving behind Excel habits and adopting data-centric systems that ensure one source of truth. As seen in manufacturing, this shift enables real-time collaboration across departments and locations, something essential for geographically dispersed mining and energy operations.

Another lesson comes from the logistics and transportation sector, which rapidly digitised to handle scale. Global shippers and airlines moved to agile software for tracking, scheduling and asset management, replacing manual logs and siloed databases. Mining companies can mirror this by using proven supply chain management solutions to coordinate the flow of ore or fuel. The oil and gas industry has even begun borrowing concepts from tech startups, embracing agile project methods and “fail fast” innovation cycles to deploy new digital tools more quickly. Historically, mining has indeed sought to follow other industries in adopting innovation, often adapting technologies from elsewhere. We see this in how autonomous haulage trucks in mines were inspired by self-driving tech in automotive or how remote operation centres for mines borrow best practices from aerospace and defence simulations. The cross-pollination of ideas accelerates progress.

A key cross-industry insight is the importance of agile technology adoption. In fast-moving sectors like software or telecommunications, an agile approach, iterative development, cross-functional teams and quick pilots has been crucial to realise value quickly and adjust course as needed. Mining and energy companies, in contrast, have tended to favour large, monolithic projects (and often analysis paralysis) over agile experimentation. However, that is changing. Some miners now run digital innovation hubs, applying agile sprints to analytics and IoT projects and co-creating solutions with technology partners rather than specifying every requirement up front. This more nimble mindset will help overcome the internal resistance to change that plagues many large organisations. Adjacent industries have shown that a “fail fast, learn faster” culture actually reduces risk in the long run by surfacing issues early and encouraging innovation.

Successful digital transformers in any industry focus on people and processes, not just tech. Mining and energy can learn from others by investing in training workers on new tools, breaking down silos between IT and operations and reengineering workflows to take full advantage of digital capabilities. There are cautionary tales from other sectors where expensive technology was layered on broken processes, yielding poor ROI. The lesson: true transformation may require redesigning how work gets accomplished (for example, moving from reactive maintenance to a proactive, data-driven maintenance program) rather than simply automating the status quo.

Mining and energy companies stand to gain immensely by studying how peers in manufacturing, logistics, utilities and other fields tackled digital change. Adopting technology proven in other industrial sectors can generate substantial value without having to start from scratch. The path forward can be de-risked by leveraging off-the-shelf platforms, engaging private sector experts and embracing modern management approaches. What is needed is the willingness to look outward, be agile and import best practices, then tailor them to the unique context of mining and energy. The result is a leap in digital capability at a fraction of the time and cost it would take if one tried to innovate everything internally.

The Pitfalls of Excel-Centric Thinking

One common thread holding back digital transformation in mining and energy is an overreliance on Excel-based legacy systems and spreadsheet-driven workflows. Excel is a fantastic personal productivity tool and indeed many operations professionals are deeply comfortable with it. But stretching spreadsheets to run mission-critical processes at scale introduces systemic issues that undermine efficiency, accuracy and growth.

As seasoned engineers who have seen “Excel culture” persist in plants and mine sites, we offer a clear warning: continuing to adapt and extend Excel (even with add-ons) is a poor long-term strategy in today’s data-intensive environment.

The limitations of Excel-centric systems are both technical and organisational:

Lack of Scalability and Concurrency:

Excel was never designed as a multi-user, enterprise database. It performs admirably for single-user analysis, but falters when many users need to access and update data simultaneously. There is no robust concurrency control – if multiple people open a spreadsheet or a Power Apps form tied to an Excel file, data collisions and overwrite issues are common. Expanding an Excel-based solution to tens or hundreds of users or millions of records is a recipe for broken links and sluggish performance. As one expert succinctly put it, “Excel is not designed for concurrent usage by multiple users” and it is not suitable for large datasets or complex applications due to performance concerns. In an industry context, this means a production planning spreadsheet might work at one site with one scheduler, but completely break down when scaled to a global operation with dozens of planners.
Version Control and Single Source of Truth Issues:

Ever hear the phrase “multiple versions of the truth”? That is what happens when critical data lives in Excel files circulated over email or shared drives. It is challenging to maintain one reliable version of data in spreadsheets. Each user might make local copies or introduce changes that others are not aware of. By the time a report is compiled, teams waste time reconciling discrepancies between 'Version Final' and 'Version Final_v2'. In fast-paced mining and energy operations, such delays and confusion are costly. Without rigorous version control, spreadsheets demand heavy, error-prone workflows to review changes. Many companies unknowingly rely on a few unofficial “spreadsheet gurus” who know how to tease insight from a tangle of workbooks. When those individuals leave or when a macro-laden workbook crashes, the entire process grinds to a halt.
Data Integrity and Errors:

Spreadsheets are infamously prone to human error. A minor typo in a cell, a mis-copied formula or an incorrect range can silently skew results. Our own experience supporting organisations across industries has found error rates in complex spreadsheets can be shockingly high. In mining and energy, this is more than a trivial inconvenience, it can lead to serious business and safety risks. An incorrect formula in a resource estimation sheet could misguide multi-million-dollar decisions. Unlike robust database systems, Excel has no inherent mechanisms to enforce data validation or relational integrity. One mining software CEO quipped that Excel often turns into “a one-of-a-kind piece of formula and lookup artwork” that is barely auditable. The more complex the spreadsheet, the greater the chance that a subtle error will propagate. In fact, manual data handling and Excel errors contribute heavily to data integrity issues, forcing engineers to spend more than 30% of their time just searching for or validating data. This is wasted effort that delays decisions and erodes trust in the numbers.
Integration Challenges and Siloes:

Excel does not play well as a backend for integrated systems. While it is easy to do an ad-hoc export or build a quick chart, spreadsheets are fundamentally siloed data stores. It is difficult to have Excel automatically sync with multiple source systems in real-time. Many industrial companies end up with “spreadsheet sprawl” – dozens of files exporting and importing data between various software, patched together with macros. This is brittle and hard to maintain. If one file is out of date or a macro fails, the whole chain breaks. Moreover, spreadsheets are not built for complex queries or joining multiple data sets. In an age where executives want a unified dashboard of operations, relying on Excel as a database means the picture will always be fragmented and out-of-date.

Excel is Fundamentally a Liability to Operations
Security and Data Governance Risks:

Security is by far the weakest point of spreadsheets. Excel files can be copied, shared, or modified by anyone who has access, often without leaving a trace, which poses risks in mining practices. Controls like password protection or cell locking are easily bypassed or forgotten. This poses huge risks in sectors where data is sensitive (e.g. reserve estimates, commercial contracts) or regulated. Insider threats are a concern, as a third of all data breaches involve internal actors. If a critical spreadsheet is emailed around, the company loses the ability to track who viewed or changed what. There is also the risk of unintended errors where an employee might inadvertently delete critical rows or save over a master file. Unlike enterprise databases that have audit logs, role-based access and backups, a spreadsheet can be deleted in an instant, potentially costing millions in lost information. Additionally, spreadsheet-based processes often fall outside formal IT backup policies. It is not uncommon to find that a multi-million dollar operation is running on a single workbook sitting on someone’s desktop or a network drive, with backups done (hopefully) manually. This lack of governance is a ticking time bomb.
Performance Bottlenecks:

As data volumes grow, Excel becomes sluggish and unstable. Large worksheets with tens of thousands of rows, complex formulas and VBA scripts will tax even high-end PCs, users experience long recalculation times, crashes or corrupted files. We have seen mining planning sheets that take 10 minutes to open and 5 minutes to save, severely hampering productivity. Real-time analysis is virtually impossible in such scenarios. Modern mines and plants generate huge streams of data (sensor readings, IoT devices, etc.) and trying to force-feed that into Excel in real time is futile. Spreadsheets cannot handle real-time, high-frequency data; they end up being static snapshots that are outdated the moment they are printed or emailed. Frontline workers then make decisions on stale information or have to manually cross-check with live systems, defeating the purpose of “digital” transformation.

The net effect of these Excel-centric pitfalls is invisible drag on the organisation. Day by day, hour by hour, valuable time is lost in workarounds: chasing down the latest file, fixing broken links, double-entering data from one sheet to another. For example, we have seen engineers at one mining company spending 2–3 hours per day in meetings resolving data discrepancies stemming from multiple spreadsheet versions. In another case, delayed updates in a maintenance spreadsheet pushed equipment repairs out by 4–8 hours, with each hour of haul truck downtime costing thousands of dollars. These are huge hidden costs of “sticking with what we know.” Much of this downtime is not reported or is considered standard practice or part of normal business! Over time, the compounding inefficiencies translate to millions in lost throughput, unnecessary maintenance overruns or avoidable safety incidents.

To be clear, Excel has a place as a personal analysis tool and quick prototype models might start in spreadsheets. But leaning on Excel as a crutch for core business systems is a strategic dead-end. Even layering Microsoft’s Power Platform tools on top (e.g. using Power Query or Power BI with Excel data) only masks the symptoms but does not cure the underlying issues. Following, we delve deeper into the allure of these “low-code” fixes and why, despite their promise, they often fail to deliver high value at scale in industrial settings.

Why Low-Code Is Not Always High-Value in the Energy and Mining Sectors

Confronted with the shortcomings of pure Excel-based workflows, many mining and energy companies have turned to low-code platforms like Microsoft Power Apps as a stopgap solution. At first glance, this approach is appealing: citizen developers can quickly build a custom app or form without heavy IT involvement, often using existing Excel or SharePoint data as a foundation. It seems like a fast way to modernise a legacy process, swap out the clunky spreadsheet for a slick new app interface, all with minimal coding. However, seasoned experience reveals that low-code does not automatically equal high-value, especially when these apps are built over the same old Excel or similar databases.

In practice, Power Apps layered on these types of database tend to inherit and sometimes exacerbate the very limitations they were meant to solve.

Let us critically examine the systemic issues with this approach:

Inheriting Excel’s Limitations: a significant barrier to effective data analytics in mining and energy operations.

If you build a Power App on top of an Excel file (for example, an Excel stored on OneDrive or SharePoint acting as a pseudo-database), you are still fundamentally constrained by Excel’s backend capabilities. All the scalability and concurrency issues we discussed remain in play. Microsoft’s own guidance strongly advises against using Excel as a data source for anything but the smallest, simplest apps. As we typically advise and note, using Excel as a database is a “Big No” for anything beyond prototyping. Power Apps itself has a default limit of retrieving no more than 2,000 rows from an Excel or SharePoint data source (the so-called delegation limit).

If an app needs to handle record 2,001, things can silently fail or require complex workarounds. This is fine for a tiny pilot project, but completely insufficient for enterprise-scale needs. The low-code app might give a false sense of progress (“look, we digitised the form!”) while the underlying data layer remains a ticking time bomb of performance and integrity issues.

Throughput and Performance Constraints:

Even with proper data sources, low-code platforms have baked-in limits. In Power Apps, connectors are throttled – for instance, a per-user/per-app license might cap you at 1,000 data calls per day. Reading or writing hundreds of records in one go can exceed these thresholds and cause failures or timeouts. Industrial applications often need to log thousands of sensor readings or transaction records daily; a simplistic low-code app will struggle with that volume. Also, while a Power App can technically have multiple concurrent users, heavy use (many people submitting data at once) can reveal latency and locking issues if the back-end is a single Excel workbook. We have seen scenarios where two field technicians submit forms nearly simultaneously and one overwrites the other’s data because the Excel back-end cannot handle concurrent writes. These kinds of glitches erode user trust or worse, causes a fundamental operational or safety related error. The frontline workforce quickly learns “this app is unreliable” and may revert to their old spreadsheet or paper as a fallback.
Limited User Interface (UI) / User Experience (UX) for Complex Workflows:

Low-code tools aim to provide a quick, generic interface, but they have UI limitations, especially for complex, industry-specific workflows. Power Apps Canvas apps, for instance, often require separate layouts for phone vs. tablet and even then the responsive design can be finicky. The result is often a compromise: the app might look decent on a desktop browser but be clumsy on a mobile device in the field (or vice versa). For field-heavy sectors like mining and energy, this is a non-trivial problem. If the app is not truly robust and easy to use on a mobile device in a pit or plant, adoption will suffer. Additionally, advanced UI behaviours (multi-layered forms, dynamic visuals, offline capabilities in remote sites with no internet) are beyond what basic low-code apps can do. By contrast, many modern industrial software solutions are designed with these scenarios in mind (e.g. specialised mobile apps for offline data capture). Simply put, low-code apps can hit a wall when requirements go beyond the basics.
Governance, Maintenance and Versioning Challenges:

One selling point of low-code is that “anyone can build an app.” But therein lies a double-edged sword. When non-developers create critical apps, often best practices for code management are overlooked. Power Apps, for example, historically had no multi-developer editing – only one person can edit an app at a time, making collaboration difficult. Proper version control integration is limited (there are ways to use source control with Power Platform, but they are not straightforward and sometimes require premium tools). This can lead to situations where a well-meaning engineer builds a maintenance scheduling app that becomes widely used, but there is no clear documentation or governance. If that person moves on, the app becomes the new “black box” that no one fully understands, reminiscent of the old spreadsheet nightmare, but now hidden behind a prettier interface. Moreover, as the app grows in complexity (more screens, more logic to handle edge cases), the lack of traditional development rigor can result in a fragile product. Maintaining a large Power App can become as difficult as maintaining code – some experts note that as apps grow, users often need to resort to writing custom code (e.g. Azure Functions, custom APIs) to fill gaps. This erodes the initial value proposition of “no code needed” and can and does introduce security and performance risks if not done carefully.
Licensing and Cost Pitfalls:

Low-code platforms often lure you in with the tools you already have (many organisations get basic Power Apps capability as part of Office 365). But to do anything truly enterprise-grade in mining and minerals processing, you often bump into challenges. Need to integrate with your on-prem ERP or a third-party service? That likely needs a premium connector not covered in the standard license. Want granular user access control or a portal for external users (e.g. contractors)? That is a different licensing tier with additional fees. These costs can multiply as usage grows, for example, Power Apps pricing per user per month can add up significantly when scaled to hundreds or thousands of employees. We have seen cases where an intended “low-cost” solution became more expensive than if the company had just invested in a purpose-built software platform from the start. The licensing model is also complex, which can lead to unforeseen limitations (for instance, an energy company built a Power App tied to a SQL database only to later discover their license throttled SQL connector calls, crippling performance until they upgraded all users to a higher tier).

To be fair, low-code platforms like Power Apps have genuine benefits. They can accelerate certain types of prototyping and empower domain experts to create tools that better fit their workflow. For niche, low-volume needs (say a custom inspection checklist at a single site), a PowerApp on SharePoint might be perfectly fine and far better than paper. The caution is that organisations must recognise the boundary between quick win and strategic solution.

A hastily built low-code app is not a substitute for a robust, scalable system when the process is core to the business. If treated as a stopgap or a stepping stone, low-code can be useful, just do not let it become the new legacy albatross.

In essence, “Low-code is not always high-value” because high value at scale requires solid architecture, disciplined engineering and often deep customisation, things that low-code by nature abstracts or limits. Mining and energy firms should be wary of thinking they have digitally transformed just because they stood up a few Power Apps. The truth will surface when those apps struggle under real-world loads or evolving requirements. A smarter approach is to use low-code tactically (to prove limited concepts or fill very small gaps) but plan for industrial-strength solutions for the long haul. The next section explores what those strategic pathways might look like.

Strategic Pathways Forward - The Future of Mining is Real-Time Data

Achieving true digital transformation in the mining and energy sectors calls for a strategic, experience-informed approach. It is about charting a roadmap that avoids the detours of past mistakes (like Excel entrenchment or one-off apps) and leverages the best of modern technology and cross-industry know-how. Having experience guiding multiple organisations through this journey, we recommend several pathways forward for mining and energy executives:

1. Develop a Clear Digital Vision and Roadmap

Start with the end in mind. What does a digitally transformed mine or energy company look like for your organisation? Define clear objectives such as “real-time visibility of operations,” “predictive maintenance across all assets,” or “data-driven decision making at all levels.” Use these goals to create a phased roadmap.

For example, phase 1 might integrate and centralise data (to eliminate spreadsheet silos) and install proven IIoT sensors on critical equipment, phase 2 might apply advanced analytics or AI on that unified data and phase 3 could automate decision loops. This roadmap should be aligned with business strategy (e.g. if rapid expansion is planned, digital systems must scale accordingly).

It is crucial to secure C-level buy-in and communicate a clear vision so that everyone understands why these changes are necessary. In particular, we have observed that miners often need better coordination and an enterprise data strategy to get more from digital initiatives. The same applies to energy companies. A well-articulated roadmap ensures that digital projects are not ad-hoc, but part of a cohesive push toward business outcomes.

2. Modernise Core Systems (Move Beyond Legacy Excel / Access / Point Solutions)

Make it a priority to replace or upgrade legacy systems that are linchpins of operations. If production scheduling is currently undertaken in complex Excel workbooks, evaluate mining-specific ERP or MES (Manufacturing Execution System) solutions that have that capability built-in. If maintenance tracking sits in an old Access database or a SharePoint list, consider moving to a modern asset management platform or at least to a cloud database (e.g. Azure SQL or similar) that can scale and integrate.

The idea is to invest in scalable industrial digital solutions that serve as reliable system-of-record platforms. These might include IoT data lakes for sensor data, cloud-based geological modelling software or advanced trading and risk management systems for energy supply chains. While such upgrades require capital and change management, they form the digital backbone on which more advanced applications (AI, digital twins, etc.) can be built. Remember the earlier stat: digital leaders can reduce downtime by 60% and cut maintenance costs by 30–40%. Those gains come from robust systems that capture data and trigger actions in real time, not from patchwork spreadsheets.

3. Leverage Proven Off-the-Shelf Solutions and Cross-Sector Tech

Resist the urge to custom-build everything. There is a rich ecosystem of technology developed for adjacent fields that can be applied to mining and energy with minimal tailoring. For instance, tools for geospatial analytics used in agriculture or forestry can help in mineral exploration and pipeline monitoring. Software from manufacturing for inventory management and fleet optimisation can be adopted for mine fleet and warehouse management.

The key is to thoroughly scan the market (and even unconventional sources) for solutions that address your pain points. Adopting an off-the-shelf solution, even if it covers 80% of your requirements usign 20% of your efforts, can deliver value far faster than a multi-year internal IT project to develop a perfect-fit tool. Gaps can often be bridged with minor customisations or integrations. As noted, there is substantial opportunity in adopting technology proven elsewhere, it de-risks the investment and accelerates deployment. Many vendors now also offer modular, subscription-based products, which lowers upfront costs and allows trials before full commitment.

4. Cultivate Agile, Cross-Functional Teams

Implementing digital tech is more about people as tech. Break down the silos between IT, operations, planning, engineering, maintenance, reliability etc., by forming cross-functional teams for digital initiatives. An agile approach, where representatives from each stakeholder group collaborate iteratively, helps ensure the solutions actually meet operational needs and gain user acceptance. Encourage a culture of experimentation: pilot new tools at one site or on one process, learn and refine, then scale up rapidly if successful. It is important to embrace quick wins where possible, as they build momentum and demonstrate tangible ROI, but balance them with longer-term foundational projects. Though equally as important is to 'fail' fast initiatives that are not working. This takes courage and leadership but is very important in any digital transformation strategy.

Moreover, infuse domain expertise into tech projects. For example, if deploying an AI drill optimisation system, involve experienced drill operators alongside data scientists; their experience will guide the AI to focus on practical insights. Conversely, bring data analysts into mine planning meetings so they understand the context behind the data. In our experience, this cross-pollination not only creates better solutions but also increases organisational buy-in, as people feel ownership of the transformation rather than having it “done to them.”

A Plan for Strategic Pathways Forward - Miniotec — A Plan for Strategic Pathways Forward

5. Strengthen Data Governance and Security

With great data comes great responsibility. As you modernise systems, establish strong data governance from the outset. Define data ownership (who is responsible for data accuracy in each domain), implement data quality checks and ensure there is a “single source of truth” for critical data (eliminating the proliferation of shadow spreadsheets). Invest in cybersecurity and access controls; the more connected and digitised operations become, the more they could be targeted or inadvertently compromised. This includes training employees on data security practices, after all, internal actors can often be the weakest link.

Modern platforms often have security features like role-based access, audit trails and encryption and make full use of them to avoid repeating the open-access nature of spreadsheets. Regulators and auditors will also appreciate a move away from opaque manual records to transparent, well-controlled digital systems. Trustworthiness of data is a pillar of E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) and by governing your industrial data well, you increase trust internally and externally (e.g. investors trusting your reported metrics).

6. Digital Transformation in the Mining Sector - Plan for Integration, Not Islands

One trap to avoid is implementing a bunch of new technologies that do not talk to each other. The true power of digital transformation comes when systems are integrated, allowing data to flow and processes to be automated end-to-end. For example, ideally link your IoT sensor platform with your maintenance work order system, so that a vibration alarm on a truck or conveyor can automatically create a maintenance task. Ensure your mine planning software exchanges data with the finance system, so changes in the mine plan reflect in updated cost projections instantly.

Integration might require middleware or using modern cloud architectures with APIs, but it is worth the effort. This is where enterprise architecture planning is key – map out how each new tool fits into the overall puzzle. Many companies find success using a data lake or warehouse as a hub, where all systems push critical data and then using analytics dashboards on top for unified insights. The bottom line: avoid creating new silos. Every digital project should answer, “How will this connect with our other systems?” Otherwise, you risk just creating a new generation of disconnected “digital islands” (today’s equivalent of spreadsheets that do not sync).

7. Manage Change and Upskill the Workforce

Finally, recognise that technology is only half the battle. The best system means little if people do not use it effectively. Invest in comprehensive change management. This includes executive sponsorship (visible support from top leaders), frequent communication about the why and how of changes and robust training programs. Identify digital champions at different levels of the organisation who can advocate for the new tools and help their peers. Provide on-site support during transitions (for example, when replacing a paper inspection with a tablet app, have support staff in the field to assist workers initially). Also, consider the workforce of the future, you may need to hire or develop new skills. Data analysts, automation engineers, UX designers and others might become as important in a mining company as geologists and metallurgists. Some companies partner with tech firms or universities to upskill employees and inject new knowledge. The goal is to cultivate an organisation that not only has new digital tools, but also the expertise and culture to leverage them. When employees at all levels see data and digital tech as allies rather than threats, the transformation becomes self-sustaining.

By following these strategic pathways, mining and energy companies can navigate their digital transformation with confidence. They will avoid the common pitfalls (like clinging to Excel or relying solely on quick fixes) and instead build a solid foundation for continuous innovation. The approach is analogous to developing a rich ore body or a productive oil field: you need a plan, the right tools, skilled people and a willingness to invest for long-term yield. When undertaken right, the reward is not just operational improvements but a fundamental shift in organisational capability – an enterprise that is agile, informed by data and primed for the future.

Embracing the Next Frontier

The mining and energy sectors stand at the brink of a long-overdue new frontier. Much like previous eras saw leaps in mechanisation or electrification, today’s frontier is digital. It is about harnessing information, connectivity and automation to run operations smarter and safer than ever before. The journey is not and will not be without challenges, change never is, but the case for action has never been stronger or clearer. These industries have immense untapped potential: most have digitised only a fraction of their workflows, leaving significant efficiency gains and cost savings unrealised. By embracing comprehensive digital transformation (and not settling for half-measures like upgraded spreadsheets), companies can unlock those gains and drive a step-change in performance.

The Pitfalls of Excel-Centric Thinking — The Pitfalls of Continued Excel-Centric Thinking

This transformation should be pursued with optimism and pragmatism, not alarm, especially in the context of mining digital transformation. The tone is one of opportunity: the chance to streamline workflows that were once bottlenecked by manual effort, to gain real-time insights previously impossible to attain and to adapt proven innovations from across the industrial world swiftly into mining and energy contexts. It is also the opportunity to elevate the workforce, freeing talented people from drudgery of data wrangling so they can focus on higher-value analysis and decision-making. The end game is an organisation that combines the deep experience of its industry (the on-site know-how, the engineering expertise) with cutting-edge digital tools. Such organisations will enjoy the benefits of both worlds: traditional expertise enhanced by data-driven precision.

Miniotec's Digital Transformation 80:20 rule - 80% of your outcomes (results / rewards) should come from 20% of your inputs (time, resources)

Thus the call to action for leaders in mining and energy is this: be bold and strategic in driving digital change. Do not let comfort with legacy systems or fear of the unknown cause you to cling to the past. At the same time, draw on lessons learned, both your own and those from other sectors, so that your investments are thoughtful and targeted. The competitive gap will widen between those who proactively transform and those who hesitate. But with a clear vision, cross-sector collaboration and a commitment to phasing out inefficient legacy practices, any mining or energy company can set itself on a path to thrive in the digital age.

The next frontier is here. It is data-rich, interconnected and intelligent. It favours the agile and the innovative. By decisively moving beyond Excel-bound thinking and low-value quick fixes and by embracing scalable, modern solutions, mining and energy companies can secure their place in this future. Those that do will not only improve their bottom line and safety record, they will redefine what operational excellence means in the 21st century.

And that is a legacy worth striving for, far more enduring than any spreadsheet. Digital transformation in the mining sector and energy industry is the key to modernising legacy systems and achieving sustainable success. Now is the time to embrace that next frontier, especially now with Artifical Intelligence, with experience, expertise and trust in the proven path forward.