The ABCD+ of technology: data

“Everything is data,” says Ian Pay, Head of Data Analytics and Tech at ICAEW. “Every mouse click, every move on your laptop is data. And it’s not just on your devices – it could be the way you tap in and out on public transport, or at your office. Every element of the way you interact with the world now has the potential to be data.”

For Ian, an important distinction to make is between data and information. “Data is everything everywhere, and information is about how you utilise that effectively,” he says. “So it’s about turning data into information. How do you take all that raw stuff that’s out there – it might be text, numeric, images, video, audio – and turn it into something valuable?” 

The most successful businesses are those that are utilising all the information at their disposal. Amazon is a classic example: they know how you’re moving around the website, what you’re looking at and how long you spend on those pages. “And what they’ve done incredibly effectively is to use that information to try and make it easy for you to find the products that you want to find, but ultimately to increase the likelihood that you will, at some point, buy something,” explains Ian.

What do I need to know?

Businesses have always used data and information to manage operations and support decision-making. The significant change over the past decade has been volume, driven by rapid advances in technology and computing power. In the 10 years that he spent working in audit at PwC, Ian went from processing data volumes where a few million transactional records was considered large to frequently dealing with clients with more than a billion.

“It’s all very well having data, but really you want to be able to analyse it. You want to be able to use it to make decisions and inform thinking,” he says. First, it’s important to get comfortable with the tools used to extract the useful information. “Businesses are increasingly working with data volumes that will exceed the capacity of Excel. So you need to know how you’re going to handle that data effectively.” That might mean getting to grips with Excel extensions like Power Query that can handle larger data volumes, or tools such as Power BI, Alteryx, KNIME and Python. 

For those working in large firms, there will likely be dedicated teams of data specialists – but it’s still crucial to build your own skills and knowledge. “It’s going to be the auditors, the accountants, the finance people in the businesses who are predominantly the data people – whether they want to be or not,” Ian says. “The biggest mistake finance professionals make is to think of data as an IT problem. IT professionals might maintain the systems, but they are not the guardians of the data, it’s the people in the business who are responsible for it – and that’s going to be the finance team for a significant amount of the most business critical data.” 

What are the opportunities?

The opportunities with data broadly fall into three areas. “What we always talked about at PwC, certainly from an audit perspective, was the triangle of quality, efficiency and insights – but it applies pretty much across the board in accountancy,” Ian says. 

In audit, data offers much better quality of testing because it enables you to move away from a random sample approach to full population-type testing to get a detailed picture of what’s going on. Once those data techniques are embedded, it’s then much easier to develop routines and automation, and improve efficiency. Finally, you can perform analysis that clients don’t have the capacity or capability to carry out themselves, drawing together threads from a wider perspective, finding more interesting connections between data points, and then presenting that insight back. 

There are four sequential types of data analytics: descriptive, diagnostic, predictive and prescriptive. Descriptive uses data to describe what has happened, and diagnostic to look at correlations and causality. Predictive uses data to look into the future, while prescriptive doesn’t just predict what’s going to happen, but identifies actions that will change a particular course. 

“The profession has successfully moved from descriptive to diagnostic – and you see this in the ACA with the data analytics platform that’s embedded there,” says Ian. “If you’re going to do well in the exam using those tools, it’s not just about seeing what’s in there and describing it; it’s about seeing what’s in there and using your own wider knowledge and experience – and that’s a big, important overlay – along with the other information that’s been made available to you to build a picture.” 

Finance professionals are increasingly performing modelling and predictive-type analytics – and this is something clients now expect, he adds: “They don’t just want their accountants to tell them what happened a year ago; they want their accountants to help them make decisions about where to go and what to do next.”

What are the challenges?

Quality of data is one of the biggest challenges. “Auditors and accountants are already aware of the concepts of completeness and accuracy – and it’s the same deal in data,” says Ian. “How do you know the information is complete? How do you know it’s accurate? You should start with those questions every time you’re working with a piece of data. 

“One of the biggest challenges many auditors and accountants face is getting good-quality information from clients. Data acquisition has become quite a big industry in its own right, because it is so important to get it right. We’re starting to see a lot of software providers getting into this space and looking at how to make it as easy as possible to get good-quality information from the client into the accountant’s or auditor’s platform.”

Things may have moved on from the days of inputting folders full of receipts – but we are still some way from a seamless exchange of accurate information where human error is completely removed. “A big step for businesses is likely to be e-invoicing, and we’re just starting out on the legislative journey for that in the UK,” explains Ian. “This is where data fits into the whole ecosystem: if you get the processes right, data quality improves. The information is richer, and you can do more with it.”

Data security is another huge challenge: “Finance professionals are handling a lot of very interesting data, and there is a need to maintain security of that, not just in terms of client confidentiality, but because that’s really valuable data for criminals.” GDPR gives a clear legal framework around the processing of personal data, but a lot of clients’ data falls outside of this scope. Ian argues that it should be handled with the same degree of care. “Client financial data is just as sensitive, and it would be just as bad if it got out. You may not be fined by the Information Commissioner’s Office, but you’ll lose clients,” says Ian.

Accountants need the skills not only to obtain, process and protect data, but to present it too. “There is an art and science to how you present information effectively, how you narrate what the data is saying and make it engaging,” says Ian. “Data visualisation is a massive area that more and more finance professionals will get into as Power BI continues to gain traction. As a student, if you can get yourself au fait with Power BI, you can make yourself useful for whatever you find yourself in once you’re qualified. If you’re good at Power BI, the world opens up to you.”

Where next?

Artificial intelligence (AI) is helping people work more effectively with data. For example, you can ask an AI tool to write a script in Python, rather than starting from scratch. “There’s what’s called the five Vs of big data – volume, velocity, variety, veracity and value,” explains Ian, “AI is impacting all of them. The volumes are getting bigger, the speed at which the data is coming at us is getting faster and faster, and the need for insights and accurate information is growing rapidly.” 

While AI is accelerating the speed of change, the advent of quantum computing will be truly transformational, he believes: “When that really starts to take off, it will just massively accelerate processing powers. With AI, things that previously would have taken hours or days or weeks to calculate, you can often do in a matter of minutes. But with quantum computing, it becomes almost limitless. The volume of data you have doesn’t matter anymore, because it will just process it instantly.” 

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