Social performance by design: From frameworks to better futures
Social performance in South Africa, especially in the mining industry, is crucial for promoting socioeconomic inclusion in a country where many people were historically excluded from economic participation.
To promote inclusion, the initiatives of mining companies are strictly regulated by government bodies, and by the very community a mine operates as part of maintaining a mine’s social licence to operate. These efforts have focused on building capacity through education, bursaries, learnerships, and school improvement programs, creating pathways for local people to access employment, procurement and entrepreneurial opportunities.
In the latest episode of acQuire Connected, host Jaimee Nobbs is joined by Carinka van der Watt, IM Governance, Risk and Compliance Specialist at Thungela, and Maritha Erasmus to explore how mining companies can manage social performance data in a way that’s responsible, measurable and trusted to drive genuine social impact across mining communities.
Listen to the full episode here or read on for the key takeaways from the episode.
Designing social performance with purpose
Social performance in mining is far more than a reporting requirement. As Maritha shares, in the South African mining industry, social performance is deeply tied to the country’s history and its ongoing journey toward inclusion and equity.
“Social performance within South Africa is a critical aspect of socioeconomic inclusion… it’s about generating opportunities for people that are impacted by the mining industry long-term.”
Carinka adds that true social performance extends beyond regulatory requirements to building trust and shared value with communities.
“Social performance is not only a regulated requirement; it’s about earning and maintaining both legal and social licence to operate. It’s built on trust, transparency and shared value.”
This focus on building a shared purpose rather than regulatory box-ticking reframes how mining companies can approach social performance as a way to strengthen relationships, improve resilience, and contribute to better futures long after operations end.
Data as the foundation for responsible performance
For both guests, social performance data is the cornerstone of credible and effective social performance. Without accurate, accessible data, organisations can’t make informed decisions or build trust with their stakeholders.
“It all starts with accurate data,” says Maritha. “Without it, you have no sight of your risks or your opportunities. Reporting shouldn’t be viewed as an annual compliance report. It’s a key management tool to manage risks and leverage opportunities.”
Carinka highlights that strong data governance underpins both social and legal licences to operate:
“You need to understand both: the legal licence and the social licence. The two are independent pillars, and both are sustained by proper data governance.”
When designed with intention, data governance can become a framework for transparency, accountability, and trust.
Starting with purpose, not policy
Carinka highlights that effective governance begins with understanding your purpose. “When governance helps solve real business problems like operational efficiencies, safety, and ESG reporting, it becomes a strategic enabler.”
She also cautions against chasing perfection: “In mining, where data is vast and varied, perfection is costly and often unnecessary. Focus on fit-for-purpose data. Data that’s good enough to support the decision at hand.”
This mindset encourages organisations to prioritise practical, outcome-driven governance over rigid frameworks to enable progress and change.
Building trust through transparency and technology
Both Carinka and Maritha agree that trust is the ultimate goal and transparency is how you get there. But as Maritha points out, many operations hesitate to communicate results because they don’t fully trust their own data.
“Mines are hesitant to communicate results because they don’t trust their own data. Fragmented systems, silos and unverified data all impact credibility with stakeholders.”
Technology plays a key role in bridging this gap. By connecting data across functions and automating processes, mining companies can replace fragmentation with clarity and confidence.
“Technology doesn’t fix your process; it switches the light on for you,” says Maritha. “By replacing fragmentation with automation and repeatable processes, companies can focus on data that drives real impact.”
Carinka echoes this sentiment, reinforces that governance is as much about people and culture as it is about systems.
“Governance is not a project; it’s a capability. It evolves with the business. When people, purpose and progress align, governance becomes an enabler, not an obstacle.”
When people take ownership of data, processes are well-defined, and technology supports rather than complicates workflows, trust follows both inside the company and within the communities mining operations affect.
Listen to the full episode
Hear more from Carinka van der Watt and Maritha Erasmus on how to design social performance frameworks that deliver measurable impact and build lasting trust.
Read the full transcript below:
Intro (00:00): Welcome to acQuire Connected the podcast that is your compass in the world of data across environmental, social and governance.
Jaimee Nobbs (00:10): Welcome back to another episode of the acQuire Connected podcast, where we chat to thought leaders and tech experts who are tackling their data management challenges. I’m Jaimee Nobbs, your host for this podcast, and today we’re diving into the role of data governance in mining and how companies can use their data not just to tick compliance boxes, but to make smarter decisions and deliver on long-term goals. We’re joined by two experts in the field, Carinka Van Watt, IM Governance, Risk and Compliance Specialist at Thungela and Maritha Erasmus, who leads acQuire’s social performance solution, Insite. Today we’ll be covering how to manage social performance data in a way that’s both responsible and measurable, the challenges of working with scattered or unreliable data, what it really takes to set up a good data governance framework and how technology is making a real difference on the ground. It’s a big episode today, so let’s get into it. Hi Maritha, hi Carinka, thank you both for joining us today. I would like to start off by getting to know a little bit about you both. So perhaps Maritha, would you like to start? Could you please tell us a little bit about yourself and your role and how you’ve got to where you are today?
Maritha Erasmus (01:22): Thank you Jaimee, good morning everybody and welcome Carinka. Thank you for joining us today. My name is Maritha Erasmus. I am the Value Stream Lead for acQuire’s social intelligence product, Insite. I’ve spent much of my career at the intersection of social sustainability, governance and technology developing solutions that help organisations strengthen their data governance, ensure social compliance, and deliver real value to their stakeholders. So it’s always been this balancing act between delivering true impact, social impact to the communities impacted by the mining industry, particularly in South Africa and meeting compliance obligations through accurate reporting.
Jaimee Nobbs (02:09): Thank you, Maritha. And how about yourself, Carinka? Can you tell us a little bit about yourself and maybe introduce your role and how you got to where you are today?
Carinka van der Watt (02:18): Okay. Yes, Carinka van der Watt. I’m currently the IM Governance Specialist for a mining company. I have approximately 20 years of experience in the actual mining industry. Having worked with some of the largest mining companies globally, my career began at an operational level. I literally started underground and that was probably the biggest blessing because I have a very deep understanding of what happens on a granular level and I’ve gained hands-on insight into the realities of the mining environment. And over time I transitioned into governance, risk and compliance where I now specialise in information management risk and compliance. In my current role, I look at GRC frameworks and really align internal standards, external regulations such as for GDPR. And obviously with that mandate I can collaborate closely with the data and security governance teams to ensure digital ecosystem supports secure, compliant and efficient operations. So a vast exposure from shelf sinking projects, right to metallurgical plant upgrades, ESG reporting, integrated annual reporting, and anything and everything where I’ve ended up now. So I’ve been very blessed to have a very good understanding of what happens holistically within mining. And then at the end of the day how that all comes back to one thing, what we put in the integrated annual report and what we disclose at the end of the day. So in a nutshell, that’s me.
Jaimee Nobbs (03:57): I think with your background, this should be a very interesting discussion today. We will be talking about data governance frameworks, particularly with the lens of social performance and in the mining landscape within South Africa. So before we kind of kick off into the questions, would you be able to tell me Maritha, a little bit about social performance, what it means and why it’s so important to report on it in South Africa particularly.
Maritha Erasmus (04:26): Social performance within South Africa is a critical aspect of socioeconomic inclusion within the broader context and history of South Africa, where historically large groups of the population were not economically included in activities and the mining industry being a key player of utilising a labour to generate wealth without people in the communities most impacted by the resource industry getting any benefit long-term benefit from it. So that is why it’s so regulated within South Africa, but it’s also so important to generate opportunities for people that are impacted by the mining industry long-term and the starting point historically and still valid today. But we have made a lot of progress over the last 15, 20 years of having social and labour plans and other tools to enable transformation or social transformation is really has been on capacity building, enabling people to participate because this requirements within the mining industry, whether it is from a recruitment point of view into professional areas within the business or whether it’s from procurement, participating in the procurement opportunities available. People surrounding the mining operations in now semi-urban or rural areas did not necessarily have the capacity to participate historically.
So a lot of investment and long-term investment had to go into capacity building through learnership and bursary programmes and school improvement programmes in our communities to build a pipeline of capacitated people in our communities that can benefit and create intergenerational wealth through the resource industry in South Africa. That is why it had such a big emphasis both from a compliance point of view as part of the national emphasis on socioeconomic inclusion of all the citizens of South Africa, as well as an impact requirement, creating opportunities for people to benefit and generate wealth and opportunity using the resources that is mined within the communities that are directly impacted by it.
Carinka van der Watt (06:50): I think she’s really on point. Social performance in South Africa, specifically in the mining sector is not only a regulated requirement; I think the country faces high levels of poverty, inequality, unemployment and crime, and I think in rural communities that host mining operations that is more prolific. So that whole scenario really creates a complex social landscape where mining companies must earn and maintain both legal and social licence to operate. So it’s about sheet value, creating value, and it involves stakeholder engagement with the community’s governance and labour providers. I think I would like to add enterprise development, which is education. She mentioned that health and infrastructure initiatives, which is all a part and parcel of your SLP and social upliftment in the communities that we operate in. And I also think that on the environmental side it’s mitigating negative impacts. So once we’ve been in a community, it’s really important to make sure that once we leave that those impacts are mitigated and things like dust noise and displacement that is addressed. So I think with social performance and effective social performance companies can exponentially increase their value to the society, to drivers like education, critical infrastructure, healthcare and supplier development. I think it really speaks to what she said and at the back end that is what I think most mining companies try to achieve.
Jaimee Nobbs (08:25): I think you’ve both given very thorough answers, thank you. What I found interesting there is it’s not just from a legal perspective, the ticking the compliance box, but also the social perspective, creating value for the community and the environment around the mining operations. How do organisations balance the responsible delivery of social performance commitments from a social licence to operate with, say the more legal aspects of the compliance reporting requirements? How does a company balance them both?
Maritha Erasmus (09:01): For me, it all starts with accurate data. Without easy access to the data you need, you cannot generate accurate reports. Without accurate reports, you have no sight of your risks or your opportunities in terms of your social performance. But reporting shouldn’t be viewed as an annual compliance report to a regulator. It should be viewed as a key management tool that’s used to manage risks and leverage opportunities for greater impact. So it’s something that needs to be done on a regular basis and it can only be achieved really when there’s strategic alignment in organisation around social performance. So it’s not a tick-boxing exercise from an organisation’s point of view. The strategic alignment I’m referring to is leadership making a stand for social performance and impact, understanding the need for impact and that leadership position needs to be underpinned by key performance indicators for operational leadership.
So how do we measure success within our leadership structures and operation? And that needs to be supported through repeatable data governance processes. So it goes all the way down so the data is available, you can do internal management reporting so you can track progress, you have your target, your KPIs, you know what it is that you want to deliver and you have the resources to do that. So when the focus of an organisation’s commitment and efforts are on robust social performance implementation, accurate compliance reporting will be the outcome. So it’s not the aim, it’s just the outcome. The aim needs to be on robust implementation of social performance initiatives. That robust implementation will assure a compliance reporting outcome. That’s what I’ve seen in best practise.
Carinka van der Watt (10:54): Yes, and I have to agree with that. I think you need to understand both. Firstly, the licence to operate, which is obviously a formal legal permission granted by regulatory authorities to conduct your mining activities. And that speaks to environmental, safety and operational laws then comes in the social licence to operate component, which is more informal, ongoing approval from those communities and stakeholders. And it’s earned through trust, transparency and shared value. And it’s not granted by law but by social consensus. So I think the two are independent pillars and that will lead to sustainable mining underscored by exactly what Maritha mentioned now: proper data governance.
Jaimee Nobbs (11:38): That leads nicely into my next question. You talk about the importance of data to underpin all of the decisions that get made at a mining operation. Carinka, you’ve helped implement fit-for-purpose data governance frameworks in your career across a number of mining companies. Now, what are some of the biggest lessons you’ve learned along the way for this?
Carinka van der Watt (11:59): Oh, I have many. So I’m going to try and summarise it as briefly as possible. So over the years I had the producer of helping organisation implement exactly that. And I think while organisations are all unique and specifically in my case, I’ve worked for probably four or five of the biggest mining companies out there. There are some powerful lessons that keep showing up and they are not just technical. So we are talking about data governance today. I would like to put a different spin on it. It’s really about people, culture and purpose. And I think one of the first lessons that I’ve learned was start with purpose, not policy. So in terms of data governance, I think it should be anchored in business value. It’s not only about compliance or control – that will just not stick. But when it’s tied to operational efficiencies, safety, things like ESG reporting, if you think about it, anything that goes into an integrated annual report or unlocking data for decision-making, that’s when people lean into it.
I’ve seen it time and time again, when governance helps solve real business problems, it becomes a strategic lever or an enabler. And I would like to add to that, one of the biggest shifts that I’ve seen is moving away from chasing perfect data in mining where data is vast and varied, perfection is costly and often unnecessary. So instead of focusing on fit-for-purpose data, data that’s good enough to support the decision at hand, that is key. That mindset helps set teams move fast and trust the data they have. So in my view, governance that’s driven by purpose becomes a tool for progress, not just a rule set. I think that was the first thing I would like to mention. Very, very important executive buy in and sponsorship is key. That is not negotiable. So without leadership buying data governance does not get the traction it needs.
I’ve learned that positioning governance as a business enabler, not just in IM or an IT as we refer to it, or compliance as a compliance function is critical. When executive C governance supports safety, again, operational efficiencies and things that make their lives easier and make their jobs easier to do, again, they’re more likely to champion it. The other thing I would like to mention is culture. You can have the most elegant governance model on paper, but if the organisational culture doesn’t support accountability, transparency and collaboration, it won’t work. Change management and stakeholder engagement are just as critical as a technical architecture, if not more. You can have the best tools, the best architecture. If the change management does not happen and the stakeholders does not buy in, there’s not going to be a positive outcome. So in mining, we teams are often operationally focused.
It’s essential to build trust and show how governance help not hinder their work. And I’ve seen this across many companies. They usually have a very siloed approach. You can think about one operation versus another one, GM being in charge versus another one having a idea of what things should look like versus the other. And at the end of the day, when you sit on a corporate level, you need to bring this all together and structure it in a way that makes sense. So yeah, culture, very, very important. And I would like to say that culture change is a journey where I sat. Mining companies are traditionally siloed. So in my previous experience with a very large international company, that is what I found. We had three operations, three GMs with three very different personalities and everything was siloed. Decision making was siloed, data gathering reporting was siloed and it made it very, very difficult when it came to ESG reporting at the end of the day to pull all of this together and compare apples with apples.
In that case, data was seen as a control mechanism and that changing of that mindset took a lot of time. It took us a three-year period just to get people to understand why it’s important to embed the right principles and policies and why it’s important to get onto the same page. We had success when the teams were empowered to own their data and see that it’s a strategic asset that will help them do their work. And obviously again, to my previous point, training and change management is extremely essential in that case. I think the other thing I would like to mention is don’t over engineer take a fitful purpose approach again to our previous point. Mining organisation is very widely maturity, very important structure and data complexity. A one size fit all approach fails and is very, very difficult at the end of the day to pull together on an enterprise level, you cannot apply cookie cutter approach.
Frameworks must be tailored. Sometimes that means starting small with just a few key data domains and scaling as trust and capability grows. And in my view, and this is where I said I’ve learned to listen first and then design second, the other thing I would like to mention is architecture must be scalable and integrated. So managing generated complex data sets from sensors underground, for example, to satellite imagery is scalable. Integrated architecture is essential. To manage this, you need systems that support metadata, lineage and automated quality checks to ensure consistency and accessibility across the value chain. I think the takeaway on that lesson was fitful purpose means building what’s needed, not what’s trendy. And that is important to remember from a compliance perspective. And because I’m in compliance and I am the governance and compliance specialist where I set the regulations like and GDPR and best practise have been very powerful drivers of governance.
They force companies to think about data traceability and auditability, but governance helps us meet these obligations while also improving internal controls and transparency. So you can’t really pocket and put it aside. You kind of need to dovetail the two and understand firstly, why do I need to capture X, Y, Z? Why is data governance important? Is it a legal prerequisite or is it a business requirement? Is it a nice to have or is it something that’s really going to enable me to do my daily job? And then you need to package it accordingly. Governance is not a project, it’s a capability. I think it’s a mind shift thing. It’s something you need to implement. It’s not something that you need to implement and forget. It’s a living capability that evolves with the business. So again, as maturity grows, regulations change, new things are promulgated, subordinate measures come into place that you need to align with.
And obviously technology also evolves at the speed of light. That means building governance into the operating model, not just into documents and frameworks and standard, not a tick box approach, but really making sure that it’s a capability where you sit. And again, where I sit, I’ve seen most success when governance is treated like a core business function, not a side initiative. And hence my statement. It’s a journey, not a milestone. I think the other thing is operational efficiency gains governance is just not about control. It’s about performance. Doing the right things enable you to do things or enables you to become excellent in what you do. Companies that implement fit-fo- purpose frameworks get real gains, faster decision-making, reduced duplication, beat the cost control and efficiencies. Okay, so instead of the characteristic approach that this is where I sit and I typically sell this to the company that I work for. Governance, doing the right thing, and compliance enable you to be more efficient because you understand what you should and shouldn’t do and it’s fit for purpose. And I think to wrap up, ultimately lesson is that data governance is about people. It’s about purpose and progress. And I think when those three things align, the frameworks become an enabler, not an obstacle. And that in my view is where real transformation happens.
Jaimee Nobbs (20:14): There’s a lot to unpack there. There’s some really great takeaways from that. I think looking at starting with purpose and not policy is a really strong way to begin. And you mentioned there about listening first and then building and tailoring solutions after that. I think there’s a lot I want to unpack there. Before I do that though, you did touch on that people were the most important consideration I guess when building governance frameworks that are actually successfully implemented. Maritha, If we look at some of the common challenges you see people face with social performance, how do you see something like building trust among stakeholders and building trust in the data? How important is that for a company to be able to implement a successful social performance initiative?
Maritha Erasmus (21:06): Trust is all about transparency and credibility and you gain trust when you communicate credible results. And mines are hesitant to communicate results because they don’t trust their own data. So they don’t trust their own internal reporting to communicate externally. Why don’t they trust their own internal reporting? Because, Carinka touched on it, when she talked about the siloed approach and operations are siloed. Now we see this siloed not just between operations but even within the operation. And one of the challenges with social performance data is that it is fragmented. It’s fragmented across, so geographically fragmented between different operations. It’s fragmented between different functional areas, HR, training, procurement, it’s fragmented between different systems. Some of it live in ERP systems, some of it lives on Excel spreadsheets and others on the back of a cigarette box, who knows where that data lives. But come reporting time, it’s takes significant effort to gather all the data to get it into some form of a report that can now be communicated, whether it’s internally or externally.
So the silos and the data fragmentation is a huge challenge for social performance data, which makes people not trust the data because it comes from different places and there’s no easy access on the quality of all of the data fields, whether it’s comprehensive. That links to the unverified and unreliable data, which speaks to transparency and trust, which makes mining industry not communicate regularly and attacks their credibility with their stakeholders. So do you hear how all of that starts with the data. And if data is seen as a way of control, then people don’t want to even share. They don’t necessarily want to solve the problem always because it might expose blind spots in their risk management. They might’ve been reporting something that once you’ve addressed the data and the quality of the data, things that might come to light that was previously in dusty corners and didn’t get the attention that it needed and people might’ve been perceived to be managing all the risks, but they might be blind spots. We find a lot of blind spots and risk management and leading to efficiencies and cost reductions. So yeah, those are some of the most common challenges with social performance data is the operational misalignment and inefficiencies as a result of the data fragmentation and unreliable data, the hesitancy to communicate regularly, and then the lack of credibility with particularly community stakeholders, which directly impacts their social licence to operate.
Carinka van der Watt (24:09): Yes, if I can add to that, I think where we sit, the number one risk that we actually logged quite recently was trust deficit with local communities. And it’s not only internally and what we put into our ESG reporting, I think mining companies across the globe face a trust deficit with local communities, regulators and investors. And it’s a number one risk I think in the industry. And to your point, Maritha, it’s about transparency. It’s really about that and everything else that you’ve mentioned.
Jaimee Nobbs (24:48): It’s a really interesting aspect. So to build trust, you need transparency and to build transparency, you need quality data that people are willing to share and be open with. There’s definitely a flow on effect. Starting from that data, again, in all facets of the mining operation. Carinka, have you dealt much with scattered and unreliable data? You did mention siloed data was a challenge.
Carinka van der Watt (25:13): Yes, quite a bit.
Jaimee Nobbs (25:15): How have you tackled that in organisations that you’ve worked with? Like you said, governance is a living capability, it’s not a project. So how have you kind of managed that?
Carinka van der Watt (25:25): Okay, so again, with one of my previous companies, it was a baptism by fire dealing with scattered and unreliable data. I think at that stage was probably the biggest challenge I’ve ever faced. So if to give some background, one of the most persistent challenges I’ve seen, not only there, but in all mining organisations, just dealing with the scattered and unreliable data, it’s everywhere. It’s across legacy systems, it’s in spreadsheets, Marita mentioned, it might be on a piece of paper or whatever, handwritten logs, siloed departments. And again, when you cannot trust it, it slows everything down: reporting decision-making, compliance, and even safety, which in my view is the most important thing. So the steps that I followed was to firstly map the data landscape and with that start with the discovery exercise. What we did is we sat down with the teams from geology to finance right through basically with each and every department and mapped out where the data lives.
The tool that we used was a SIPOC tool, that is your supplier input, process output, customer model. And basically it shows you how data flows as a supplier. So where do you get it from? So it’s where there’s internally or externally, what inputs do you need? Data taxonomy, what type of data or information is it? How does it flow between HR and finance? Does it flow internally or externally? Does it go to regulators, HRD, whatever, Who touches it? So typically who’s got access to it, and then what is the output? Typically the artefacts that you generate, is it a report to a generator? Is it a financial report? So we really need to, if we had to sit down and understand the data, how it flows from A to B, B to C, C to D, who looks at it, who touches it, who’s privy to it, and then that helped us to understand the fragmentation and identify duplication actually inconsistencies and gaps.
This touches on efficiencies. We in this process realised that there were quite a few people that looked at the same thing and quite a few gaps that nobody stopped. Okay. So that was a very, very important first step. So you cannot fix what you cannot see. And when you map the data, it’s like turning the lights on in a cluttered room for the lack of a better saying. I think what I need to just highlight here is we had data stewards, and in my view they played a very, very vital role in this whole process. You’ve got data owners who usually sit at the head of department level and then you’ve got data stewards who have a more of an operational role. Typically, they are not defined by legislation like or any other data privacy law in South Africa. They’re not legally appointed, but they’re critical to operational data governance and they’re focused on data quality, integrity, and lifecycle management.
They will typically maintain metadata, data repositories, look at data accuracy, consistency and completeness, and then obviously look at data classification, access controls. And these people were part and parcel of this SIPOC exercise that we conducted. Once we stepped away, they acted as custodians of specific data sets. So this is where you put accountability and responsibility. So very, very important once you understand what you sit on, how it’s structured, who sees it, how long you retain it, for which reason is it the regulatory compliance initiative? Is it because you need to conduct your business in a certain way? These are the people who will typically be the data sheriffs in my view. So this is to my second point, assign ownership and accountability. And this, as I said, ties in with the previous point. Scattered data often means no one owns it. So we establish clear roles data owners which are your heads of departments, your stewards, and make sure they understand their responsibilities.
In one case we made a production manager, the owner of data, suddenly the data started improving because someone cared and someone owned it. So to this point, when someone owns the data, they protect it like it’s their reputation because it is in our organisation where I’m currently in, we actually link it to key performance indicators. So you will be measured on that ensuring that we put accountability where it should be. It’s clearly articulated in a performance contract, communicated and understood and linked to individual performance ratings. The third step was to define and monitor data quality rules. Now Maritha and I had this discussion previously with another audience: data quality cannot be understated. And to an earlier point, you’ve got silos, you’ve got this and that. Different rule sets different ways of capturing data. Data quality in my view is the most important thing when it comes to data governance because at the end of the day you need to report on it and compare apples with apples.
So what we did is we set up rules for what good data looks like: completeness, consistency, validity, and obviously accuracy at the end of the day. And then what we did is we used dashboards to monitor it. So for example, we flagged missing timestamps in production logs and traced them back to faulty sensor integrations. And we therefore engaged in similar diagnostic and remediation activities, particularly within its engineering and governance environments. Engineering dashboard and sensor health reports, for example, governance frameworks and incident handling protocols. For example, data integrity assessment and vendor accountability. And I would like to stop there. Vendor accountability, and this is something that I recently wrote into a framework for the company that I worked for. Data quality and data governance is a shared responsibility. We partner up with a lot of vendors and third-party suppliers and you need to also hold them accountable for data quality.
And again, in where I said it’s not only just about fixing error, it’s about preventing them from happening in the first place and then correcting possible errors. But it’s almost a partnership between the company, the vendors that you partner with and for them to understand this is the minimum requirement that we need from a data governance perspective. We need you to comply with X, Y, Z, and we will hold you accountable. And there are SLAs, MOUs and master service agreements and anything and everything you can think about that can actually strengthen that and make sure that you’re all in the same place or on the same page. The fourth step that we looked at was really to integrate and consolidate where possible. So sometimes the problem is too many siloed, disconnected systems. And so we feasible, we consolidated and integrated platforms in one project linking geological and production systems, reduce manual reconciliation and improve trust in the data.
So integration isn’t technical, it’s again cultural. It brings teams together around shared tools. And that’s one of the biggest lessons I’ve learned. And again, it touches on efficiencies. The other thing I think is important to mention is to create a single source of truth. Where possible, build central repositories or data catalogues for critical domains, not all of them, but critical domains like safety incidents or production volumes. This becomes the go-to source for reporting audits and decision making. And again, when people know where to find the tooth, they stop wasting time debating it. And that was one of the biggest lessons that I’ve learned is you can get lost in that debate and probabilities is you will not get it to the reflection of what’s happening at a very granular level. After that, we embedded governance into daily workflows. So we embedded governance into every process like reporting, change management and project delivery.
And in that way data issues are caught early and not during month-end panic or post X factor. So when typically something does go wrong, that’s when you scratch around for the relevant information and data. So governance works best when it’s invisible and it’s part of how people already work. And then I think the most important thing is once all of this is said and done, and this is what we did with our data stewards and everybody who were involved with that specific initiative, celebrate your wins. Fixing data is hard, but small ones built momentum and after cleaning up production data at one side, reporting errors dropped, and this was one of the companies, by 30%. That success helped other teams buy in. So if you fix my data and you make my life easier and I become more efficient, guess what?
I’m going to latch onto that and I would like to understand exactly what you did. So trust is built one clean data set at a time, if I can put it that way. So yeah, scattered data and reliable data is a huge challenge. I don’t think it’s something that’s going to be fixed overnight. You can ask Maritha, we are up against it in mining. I think it’ll take many years to get to a maturity level where we can actually say we are okay and we trust our data, but it’s not impossible. And I think with the right mix of people, technology and processes and culture, we ice it. I sit, I’ve seen organisations transform their data landscape and unlock real business value at the end of the day.
Maritha Erasmus (35:15): I can share listening to what Carinka said, there’s so much information that it’s like starting to pull a piece of string and you know that ball that just goes in all different directions and we can talk for days, but the image that I love that she used is putting a light on in a cluttered room. The room is cluttered. If you keep it dark, then everybody knows it’s cluttered but nobody can see it. And once you put the lights on and they see the level of clutter and dust, people naturally want to start fixing it. It’s very hard to not want to fix when you put the light on and fixing that as a process. And what we’ve seen with technology, technology doesn’t fix your process. It switches the light on for you. So what we’ve learned with the implementation of our solution, our social performance solution is that we can automate, one of the key challenges is the data fragmentation and technology is really good in replacing repetitive manual tasks with automation.
So we can automate your data aggregation, which will reduce the manual costs and give you increases in efficiencies as experienced staff members don’t have to spend time manually gathering all of this distributed or fragmented information. And by having data quality front of mind. So one of our key values within Insite is data quality front of mind for all of the users, administrators, as well as end users. We can partner with our clients to enable them to see, to have the light on and choose where they start tidying up. So it’s not overwhelming and understanding which of their data categories is material from a risk management point of view and cleaning that up first. So by having this light on your data quality, you can strategically work to improve your data quality and standardise your processes, your data governance processes, giving you this repeatability from a repeatable data quality management protocols is really important from a risk management or audit management point of view and the management of errors in your data.
So if you can replace fragmentation with automation and repeatable processes and start to identify the material risks within your organisation in terms of the data quality, you can put a scope of work in place or a process in place and a project with your teams to start tackling those data quality issues that matter most or matter first or will give you the biggest return on your first investment in terms of time and people. The other aspect that I really, really enjoyed that Carinka mentioned is the single, reliable source of truth really important. And if you can automate your data aggregation and then integrate multiple reporting requirements, internal and external reporting requirements, we give our customers a single reliable source of truth that will align internally. So it will improve internal alignment between teams impacting that or addressing that siloed approach between different operations, but also between different functional areas and enabling them to maximise the impact from the different commitments from every rand spent in terms of different social performance, but also compliance requirements within South Africa.
So it’s the alignment of commitments. Let’s think about capacity-building commitments in the community. If you’ve got access to your data and you understand the quality, you can align the requirements between, for example, your social and labour plan and your mining charter and your B-BBEE reporting to leverage the best result whilst having the highest impact in the community as well. Simply by understanding the type of capacity building programmes you need to commit to within the community to give you the most short-term but also long-term impact and compliance result. And that you get, once you’ve aggregated the fragmented data, automated your aggregation, you’ve integrated your reporting requirements, and you’ve given your team, your operations team, a dashboard where they can track the progress, basically live using technology. So it’s addressing all of those different things with fit-for-purpose technology and solutions that is not over designed but will provide you with the key requirements to build trust and credibility internally, but also from a reporting point of view.
So I think that is really for me, the next step and what we’ve seen in industry is understanding where to automate and how to get the best from your data by treating it more like an asset and less like a transaction or just information in a transaction or a subset of data as part of operational transactions taking place. But viewing your data as an asset for decision making and strategic intent. And in order to do that, it is important to take away the manual effort of aggregation, then structuring and then interpretation. So if you can automate aggregation, structuring and interpretation so operations can spend more time using interpreted data that’s already structured in the right way to inform decisions and communicate to stakeholders, then the data turns into an asset for your organisation.
Jaimee Nobbs (41:57): Absolutely. One thing that’s really clear is that people, process and technology and culture framework between the two of your answers. So technology, like Carinka said, technology doesn’t fix a cluttered room, but it does help switch the light on in getting your data organised, getting it together and making it easier to build reports and make more competent decisions that impact not only the operation but the community. That’s the technology side. But then also the importance of having those people, the stewards and the custodians responsible for and caring about the data as part of their role internally or vendors like you mentioned Carinka, and then the processes and the culture, making sure that you’ve got those processes in place and it’s supported by the culture of the business. I think there’s some really great ties into that people process, technology and culture framework that you both have drawn on.
I think that’s a really great spot to leave this podcast episode. I think Carinka, you’ve given a really thorough step-by-step guide on how to tackle if you’ve got that scattered or unreliable data in a business, how to actually move into implementing a data governance framework. And Maritha, you’ve talked a lot about building trust in data and how we can do that. So I think that’s a really good spot to leave the episode. But thank you both so much for joining. I learned a lot and I’m sure the listeners did too. But thank you both so much for your time.
Carinka van der Watt (43:32): Totally a pleasure. Thank you, Jaimee. Maritha, thank you again. It’s always nice to rub shoulders with you. I have learned a lot too. Thank you so much.
Maritha Erasmus (43:42): No, thank you. Thank you, Carinka. Thank you for sharing your years of experience and your deep understanding of the actual implementation and the work needed to be done. I loved your emphasis on people and culture with other people and the culture, the data is nothing really. And Jaimee, thank you for hosting us. I really enjoyed the conversation.
Jaimee Nobbs (44:06): Thanks for listening to this episode of acQuire Connected. If you found this episode valuable and I hope you did, don’t forget to click subscribe so you don’t miss out on the next episodes.
Outro (44:17): Thanks for listening to the acQuire Connected podcast channel. Find us@acQuire.com au.
