A recent Grant Thornton survey of over 300 senior executives found that 89% believe the CFO of the future will require much stronger data analytics skills – and fully 75% plan to upgrade their personal data analytics skills in the coming year.
But with all the talk about big data and advanced analytics, what do the terms even mean, and how specifically can companies use it to their advantage?
Merriam-Webster’s definition of Big Data provides a perfect launching point for discussion. Big Data, it says, is “an accumulation of data that is too large and complex for processing by traditional database management tools.”
This data can come in all shapes and sizes, from GPS monitoring of people’s movements (as Google does via smartphones) to eye tracking (to determine where audiences focus their attention) to simple number crunching to find relevant patterns in terms of workforce performance and sales figures.
All of that data, however, needs to be synthesised in a way that can be acted upon intelligently and to the benefit of the user. Analytics is the process of making these numbers meaningful and actionable, leading to prescriptive measures that can revolutionise efficiency within an organisation. Real-time data, applied to strategic decision-making, represents one of the very best tools available to improve performance and increase overall market share.
Precision vs Accuracy
Very often, genuine insight is revealed to companies that invest in analytics; this is why the new wave of intelligent, algorithmic ad placement has turned Google and Facebook into dominant forces within their industry.
But we are far from being ready to let the machines take over entirely. It is possible for algorithms to analyse data with perfect precision yet form conclusions that are wildly inaccurate. A highlights this story among several others:
Mark J. Girouard, an employment attorney at Nilan Johnson Lewis, says one of his clients was vetting a company selling a resume screening tool, but didn’t want to make the decision until they knew what the algorithm was prioritizing in a person’s CV.
After an audit of the algorithm, the resume screening company found that the algorithm found two factors to be most indicative of job performance: their name was Jared, and whether they played high school lacrosse. Girouard’s client did not use the tool.
The lack of basic intuition that characterises the current generation of AI is likely to be improved incrementally over time, but with an endless number of potential ‘false positives’ waiting to be found in any pile of data, it is safe to say that human expertise will play a major role in decision-making for a long time to come.
The power of teamwork
Because we can’t let algorithms run our companies, somebody in a leadership position needs to stand in the spot where management and computers meet. The CFO is a natural choice for this task, as their experience with strategy and decision-making recommendations can be combined with analytics training to deliver the best of both worlds to the organisation.
Optimisation of forecasting, resource allocation, cost and risk management, pricing, capital allocation and investment decisions can all be achieved through a streamlined ability to harmonise data-driven analysis with a wider sense of company priorities. Each of the above improvements carries with it an additional set of knock-on effects as well; intelligent forecasting reduces wasted time and energy, better resource allocation can increase productivity, and so on.
Risk management presents especially fertile ground for the benefits of increased data analytics, and is a major reason why the technology is receiving overwhelming support from CFOs. Operation costs, workforce management, and the need to vet new growth opportunities are at the top of the list of concerns regarding risk management, according to our survey; improvement in all three areas would represent a huge competitive advantage for companies.
What does effective data analytics look like?
Data analytics, rather understandably, requires effective data collection as its foundation. Advanced algorithms, using machine learning and artificial intelligence, form the next step in the process.
The results of the analysis must then be visualised through clear and straightforward dashboards and other reporting tools, where the machine operator (in our example, the CFO) can see at a glance the main conclusions. The person at the controls should be able to select a conclusion on the dashboard, and click through to see the data summary that led to the recommendation.
Armed with these data-driven insights, the CFO will have a real-time view of the company’s current situation. Reports can then be prepared automatically, and even be personalised for each recipient. Automated assistants can guide readers of each report to their recommendations and models for future growth.
Early adopters of such a streamlined system will find themselves in an excellent position to achieve a competitive advantage over others in their industry that are slower to make the leap.
Our future decisions will surely be guided in large part by the recommendations made by algorithms. But in this particular case, the equations have already been solved and the writing is on the wall: big data and analytics are indeed essential to the future of business management.