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Good data, bad decisions: limitations of accounting data


Promoted by CPA Australia.

While our ability to collect and analyse data becomes increasingly complex, the sources of error remain all too human.

Promoted by Tony Kaye, CPA Australia 4 minute read


According to the International Data Corporation’s Worldwide Big Data and Analytics Spending Guide, Australian companies spend an estimated A$2.7 billion on building data analytics capabilities.

Strong data analytics capability has obvious advantages, but the risks cannot be ignored; wrong or misinterpreted data can be extremely damaging to any business.

A recent survey by financial controls and automation software company BlackLine, of more than 100 Australian executives in companies with A$20 million-plus revenue, found that almost half were not confident of identifying financial data inaccuracies prior to reporting.

About 80 per cent believed their organisation had made significant business decisions based on inaccurate data. Three-quarters admitted that a company they had worked for had needed to restate its earnings due to financial data inaccuracies that weren’t identified prior to closing off its books and announcing its results.

We're only human

“It’s a combination of the ever-increasing number of data sources and data volumes, and keeping pace with that has a large part to do with it,” says Claudia Pirko, BlackLine’s regional vice-president for Australia and New Zealand.

“Add to that the human effort that’s required to make sense of it and, regardless of how competent the people are, there is going to be a degree of human error when you’re trying to do that work manually.”

Antony Ugoni, director, global matching and analytics at employment group Seek says data inaccuracy often comes into play when frontline staff enter incorrect data.

 “Back in head office, in an environment where we don’t necessarily get to see all that stuff that goes on at the frontline, we’ve taken the data at face value and used it in the way we assumed it was created. There are hidden glitches in the data all the time, and this can have wide repercussions.”

Good data, bad decisions

While data quality and accuracy are essential in business, so is the interpretation of that data. “It’s very easy for businesses to make poor business decisions by misinterpreting the data,” says Ujwal Kayande, associate dean and professor of marketing at Melbourne Business School, and founding director of the Centre for Business Analytics.

 “I’ve got hundreds of examples of managers making silly decisions because they focus on the data, but not on the data generating mechanisms behind it.”

Outdated accounting systems

A lack of automation controls and clunky technology in many accounting practices are contributing to data issues, says BlackLine’s Pirko.

“It’s still common for us to hear that people are still closing off their books in Excel. It’s no wonder you’re going to end up with a higher degree of inaccurate data and a lack of confidence in the results because of the human element. The data sources grow; it’s just what happens, so you need better ways to manage that.”

Pirko says substantiation of data between accounting and banking systems is still done manually within many practices, and can become complicated when organisations have no capacity to hire extra people.

“This is where automation comes into play to enable more efficient and proper reporting and analysis.”

Understanding data needs

Ugoni, who is also chairman of the Institute of Analytics Professionals of Australia, says it’s critical for every business, including accounting practices, to determine their need for data analytics.

 “One thing that’s been crucial around analytics and how we use analytics at Seek is being very clear on exactly why we are doing this. I’d hate to walk away from a conversation thinking all we need to do is hire some data scientists and everything will be OK. The first thing I’d say is be very clear on what you want them to do.”

 “The great thing about where we are today is that, given the plethora of open-source capability, even small organisations can afford to get into data analytics because it’s never been easier to capture data on your client base. It’s never been cheaper to store that data and it’s never been cheaper to analyse that data.”

Learn more about the future of accounting at CPA Congress 2019. For event dates and details, visit:


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