Undertaking an effective data analytics and visualisation initiative can be vital to an organisation’s short and long-term success.
In fact, we think companies that aren’t yet exploiting the power of their data must make it a priority. The necessary expertise and technology are more affordable than ever before – and the value you gain massively outweighs the challenges that you can face during implementation.
“Numbers can tell you things you never even knew to ask. But they never speak for themselves.”[1]
This quote from a 2013 article in the Wall Street Journal, which we recently used when co-authoring Grant Thornton’s new book Data Analytics: Elevating Internal Audit’s Value,[2] neatly identifies the key challenge businesses are facing at today’s stage of the so-called ‘big data revolution’.
That is, how to improve their business performance or reduce risk by successfully analysing and interrogating their massive quantities of enterprise data.
Many organisations are already proving that data analytics can be used to identify growth opportunities, identify high-risk elements within the organisation and improve the quality of the decisions they make. But many others still have little understanding and less guidance about how to make the most of their data.
This is about to change. Professor Gary King, the Director of Harvard’s Institute for Qualitative Social Science, has pointed out that it is not the quantity of data that is driving the big data revolution. Rather, it is the fact that organisations can now use their data in a positive way.[3]
And, while it’s true that data-driven decisions are not always correct, there is a much higher chance that they will have far better outcomes than those based on habit or gut feel. That said, in our experience, it invariably helps that decisions based on data analytics have a ‘human review’ or pass “the eye test” before they are acted upon.
Professor King essentially means that the falling cost and growing power of technologies like data visualisation, predictive analytics, machine learning and artificial intelligence are opening up this brave new world to more and more organisations and individuals. As a result, more businesses are creating a culture of data-driven decision-making and successfully finding better answers to key questions.
Critically, getting these answers right can help organisations reduce their overall risk profile and become more profitable.
To take an example that’s close to home for us at Grant Thornton, a current client is the holding company for a portfolio of manufacturing, construction and industrial services businesses, collectively generating revenues of more than $2 billion. The organisation’s decentralised nature was making it difficult for the internal audit team to collect data for each business and then to run analytics to identify high-risk vendors, employees and transactions.
So the company brought Grant Thornton in to help. We worked with the business and the internal audit team to develop and implement a bespoke analytics and visualisation platform that takes end-user requirements and corporate management objectives into account. Now, internal audit can not only identify and investigate high-risk anomalies, it can also easily refresh the data pool and change analytics and visualisations in the light of new requirements at any time.
As a result of this project, the client has a much better understanding of its overall risk portfolio thanks to its enhanced ability to spot, and nimbly respond to, anomalies and outliers – a significant gain.
Obviously, the above example is a large and sophisticated business that has successfully established a lead in this area. But for those still just starting out, it is not too late to catch up. There’s little time to lose, however – and knowing where to start your analytics journey can be baffling.
So what should your first action be? In our experience, to prevent unnecessary delays it’s vital to start with a clear and specific analytical goal in mind. Ask yourself questions like: What business problems am I trying to solve? Am I trying to understand enterprise risk better or am I trying to improve profitability, revenues or both?
The answers will help you decide what types of analytics to run, what kind of data you need, how to visualise the results and how ultimately to extract the greatest value from your data pool.
The second step is clearly to specify, develop and implement an appropriate data analytics and visualisation platform. As we’ve seen from the above example, this can help the internal audit team identify more high-risk (and potentially fraudulent) elements than they have in the past.
The impact and benefits of analytics extend far beyond internal audit. Getting the right platform in place is one matter. The third step of creating an enterprise-wide culture of data-driven decision-making is quite another – and it’s of equal importance.
This means ensuring that analytics and visualisation should never be used in a vacuum but right across the business as a key touchstone for making the right decisions. And this broad scope means that businesses on a data journey must consider multiple needs across a range of factors. Goal-setting, data identification and formula verification for example, as well as data quality and analytics reviews, visualisation design, end-user review and data governance.
We have seen time and time again that this three-step approach has a dramatic positive impact on our clients’ performance and profitability.
Our team has a wealth of experience in analytics and visualisation design, creating solutions to meet our client’s specific needs and ensure self-sufficiency. To talk to us about how we could help you benefit from the data in your business, please contact Matthew Petrich or Mathew Tierney.