Chris Chapman of Wesfarmers Chemicals, Energy & Fertilisers on Dealing with Data Maturity Discrepancies
Alexandra Craggs, Conference Director at Corinium Global Intelligence caught up with Chris Chapman, Manager, Data and Analytics at Wesfarmers Chemicals, Energy & Fertilisers, to discuss the impact of data and analytics and how to manage business functions with different data maturity levels.
As part of his role, Chris Chapman, Manager, Data and Analytics with Wesfarmers Chemicals, Energy and Fertilisers, has responsibilities across data engineering, data science, business intelligence, and increasingly, data governance across chemicals, energy and fertilisers business units and a shared services function.
Building an Adaptive Approach
Looking after a number of different business units brings unique challenges for Chapman, as he and his team have to navigate different levels of data maturity and data literacy across teams. Moreover, they have different data needs, which means that data projects need to operate in a way that suits them, but also meets company-wide data requirements.
To tackle this, Chapman favours an adaptive approach and his team works closely with different business units to understand their problems and tailor a solution for them.
“I really try and encourage my team to go out, and really get to understand the landscape and what’s going on, so that they can scope out the best solution for that problem,” he says.
“That means we will build something that’s fit for purpose, maybe starting small, mapping it out, but really working with the business units to deliver what they need, with strong focus on value.”
Avoiding the Technology Trap
“What is important is to make sure that we define the problem properly, understand that and talk in terms of business problems. Don’t talk in terms of technology right up front,” Chapman argues.
Chapman points out that often, people get excited about a particular new technology, or hear about a new approach or solution, and want to immediately apply it to their own problems.
But a technology-first approach is not the way to deliver real value for business.
“Sometimes we will receive those kinds of enquiries from our business units, who are aware of certain solutions or approaches they want delivering, but this is not the approach we use,” he says.
“We need to understand the problem so that we can work together and build a solution. It’s not as simple as finding a data scientist and having the perception they can do some data science and solve all your problems.”
Data Literacy as the Foundations for a Successful Data Strategy
For Chapman, increasing data literacy across the businesses is a key part of the data strategy, but exactly what data literacy is, he argues, is not properly defined.
“For me, data literacy means when you can not only get actionable insights from your data, but also building trust in data, and its outputs, so that users can base their decisions on it,” he explains.
Additionally, Chapman believes that a key part of increased data literacy in a company, is the awareness of privacy, ethics, and security.
“It’s not just about trusting the data now, it’s about ensuring that almost everyone realises the impact of not using – or storing – data correctly. Building that experience in governance, privacy and ethics is key to data literacy,” he says.
If people don’t realise the potential implications of their actions with data, such as moving customer records to the cloud or not using proper security for data sets, this creates huge business risks and is a sign of low levels of data literacy.
Moreover, Chapman points out, it is not just about knowing what you can do with your data, but what you should do with it.
Explaining this in more detail, Chapman described how in fast-moving sectors such as marketing, there are lots of good solutions for automating your activities. However, doing this involves using your customer data. Although you can sign up for a service that promises marketing automation, if it requires a constant feed of your customer data, should you jump in without properly understanding the risks?
“Just having someone that’s data literate enough to not even explore that solution without looping the right people to understand the ramifications of using something like that is a good example of the advantage of data literacy,” he says.