According to the Worldwide Big Data and Analytics Spending Guide produced by the International Data Corporation, Australian companies are estimated to be spending A$2.7 billion on building data analytics capabilities. At face value, the ability to source more data, to analyse it quickly, and to use it to achieve better business outcomes shouldn’t have any obvious downsides. However, the reality is that data can be extremely damaging to any business when it’s wrong or misinterpreted.
A recent survey by automation software company BlackLine, of more than 100 Australian C-suite executives, found that almost half were not confident of identifying financial data inaccuracies prior to reporting.
In addition to not having confidence in the accuracy of their financial data, about 80 per cent of survey respondents said they believed their organisation had made significant business decisions based on inaccurate data.
The survey results underscore that many companies continue to be challenged by factors such as human error in inputting data, the growing volume of data sources, and the use of outdated computer systems.
“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, knows about data errors:
“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.”
Of course, while data quality and accuracy are absolutely 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 they have available,” says Ujwal Kayande, associate dean and professor of marketing at Melbourne Business School, and the founding director of the Centre for Business Analytics.
“I think many times, what you see is because managers are not particularly informed about data analytics and what it can do and what it can’t do, they don’t actually ask the right questions. All they say is, ‘Here’s a set of data. Give me some insights’. Now, if you’re not actually shaping how those insights are generated – if you’re not asking the right and tough questions of your data analysts – you could get some pretty silly answers, and you could be misled dramatically.
“I’ve just got hundreds of examples of managers making silly decisions because they focus on the data, but don’t focus on the data generating mechanisms behind it.”
A lack of automation controls and clunky technology in many companies are also contributing to data issues, says BlackLine’s Pirko.
“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 the substantiation of data between different systems is still done manually within many companies.
“This is where automation comes into play to enable more efficient and proper reporting and analysis.”
Are you tired of spending days manually collecting data from different sources, only to question the outputs in your reports? Contact us today for your demonstration of Array and how we can help you streamline your data, converting it to a single source, and provide automated (and accurate) reports.