Data analytics explained

This article was first published in the February 2016 UK edition of Accounting and Business magazine.

This year marks the 400th anniversary of Shakespeare’s death, and one of the most frustrating things is how little we know about the most famous playwright who has ever lived. The few facts we do know are gleaned from the rare moments when Shakespeare popped onto the radar of officialdom – court cases, house purchases, his baptism, his marriage, his funeral. The paper trail is scant.

For any aspiring world-class playwright born in Stratford-upon-Avon today it would be a very different story. From medical records to shopping habits to what they think of Zayn leaving One Direction, just about everything they did would leave a digital trail thanks to ‘big data’ – volumes of data so massive and heterogeneous they require a new generation of techniques and tools in order to be analysed effectively.

Modern technology has brought about this explosion in the quantity of data. According to IBM, 2.5 quintillion bytes of data are generated every day – and 90% of all the data that exists in the world has been collected over the past two years.

This vast volume of data falls into two broad categories: structured and unstructured. Structured data is anything that is or can be contained in a row-column field, such as a database or spreadsheet (so far, so comforting). But most data (three-quarters of it, in fact) is unstructured – word-processed documents, photos, emails, Facebook updates, digital conversations, tweets, browsing history, GPS signals, and so on.

An eye-watering volume of customer information is collected by companies and organisations every day. But all this big data is only useful if there is something that can be learnt from it – and that is where data analytics comes in.

Breaking it down

Data analytics allows companies to gather historic, real-time or predictive insights from electronic data that can be internal or external to the organisation; it uncovers hidden patterns and correlations that allow companies to make better business decisions. Unless you are determinedly off-grid, you will see data analytics in action every day. Those adverts that appear on every web page you visit? They are the result of data analytics – an algorithm is furiously analysing your browsing history, selecting and showing you ads for things that it thinks you might like to buy.

Annoying as it is to be offered a mobility scooter because you once bought your mother Saga insurance, data analytics vastly improves many areas of our lives. For example, IBM’s Watson initiative – essentially computers that learn from experience – is being used in diagnostic medicine, in some cases to predict infections in premature babies before symptoms even appear. Traffic flows in major cities are regulated using predictive and time-sensitive information, including weather reports and social media information. And Toyota has just launched a trial service in Japan that uses data analytics to predict the likelihood of serious injury during a car crash, relaying that information to nearby hospitals to use in deciding whether to dispatch ground or air ambulances. 

Accountants are comfortable with data – the profession runs on it. Even basic electronic spreadsheets are analytical tools. However, accountants deal principally with structured data – less than 5% of the world’s unstructured data is proactively managed – so the question is, what does the profession gain from jumping aboard the big data bandwagon?

Impact on the audit

So far, the biggest impact of data analytics has been felt in internal audit and its ability to allow organisations to continuously monitor indicators and identify risks. External auditors have been slower to catch up, but analytics has greatly improved the process in three main areas:

• Analytical software allows auditors to test full datasets rather than sampling.
• It makes it much easier to identify anomalies and areas of risk. Auditors can easily search a dataset for duplicate invoices, journal entries made at suspicious times, or payroll payments made to fictitious employees, for example.
• It leaves a clear audit trail. 

As the use of audit analytics spreads, it is changing the audit profession. The processing side, for example, can now be done by software or shared service centres, leaving auditors to concentrate on what they see as value-added services, such as identifying inefficiencies and helping clients to manage risk. 

The consensus is that external auditors still have a lot to do to make the most of data analytics. In particular, tapping external, unstructured data – comments on social media, for example – could alert auditors to a problem within a company. For now, though, the focus is predominantly on internal information provided by the company itself. 

As far as companies are concerned, data analytics has huge potential. A 2013 study by management consultancy Bain found that large companies that had adopted big data analytics early on were significantly outperforming their competitors, financially and operationally.

Data analytics in a corporate context means anything from tracking the clicks on a homepage to mining customer transactions for insights, and analysing management and financial information. Analytical tools allow companies to gather data quickly and efficiently from a wide variety of sources and functions for use as a basis in making forecasting, budgeting and operational decisions – all with the confidence of traceability and, as long as the raw data itself is reliable, accuracy. Finance teams regularly need access to non-financial information (such as sales data) for forecasting and budgeting, and data analytics provides the power to accurately collect and collate everything they need in one place. 

A survey of 435 global CFOs by the cloud performance management company Adaptive Insights showed how important data analytics has become to finance departments. The survey, for the third quarter of 2015, found that a third of CFOs expect the amount of data they manage to increase by 50% over the coming five years, and 41% said that their finance team already manages data from between three and five source systems. The nirvana for CFOs, added the survey, was a set of consistent and accurate data providing a comprehensive picture of the business and which could be used as the basis of all decisions.

In terms of evolution, we are still in the Ice Age of analytics at work and in our wider lives. Used wisely, analytics can bring huge benefits. For many, the only question is whether handing over personal information in the process is a worthwhile trade.

Liz Fisher, journalist