It all starts with data – getting a good handle on an organisation’s finance and tax data is the critical start point to enabling innovation.
Data analytics is not new, it’s been around for decades, but finance leaders tell us it’s still top of their agenda. When we asked which of the five technologies – data analytics, process automation, cloud, artificial intelligence and blockchain – will have the biggest impact in the next 2-3 years, data analytics, at 45%, was by far their biggest choice.
Feedback tells us that very few finance leaders are currently thinking about artificial intelligence or blockchain, but they should really start thinking about how they’re going to leverage these technologies now.
The second polling question we asked was, ‘what are the biggest obstacles to adopting new technologies?’ The two factors that stood out from the responses out are ‘leadership knowledge’ and ‘finding and retaining talent’.
This makes sense. If leaders don’t know how to embrace the technology and don’t have the skills to do it, how can they successfully drive innovation?
In a 4.0 world, the only way for the CFO to drive operational efficiency and reduce costs and at the same time provide better insights and value for the business is by embracing data analytics, intelligent automation and some of the other emerging technologies.
Lack of investment
Enterprise Resource Planning (ERP) systems were the last big wave of investment in finance, and, in general, we can see that finance functions have been under-invested in terms of technology for some time.
Many firms have focused on getting their ERP systems – their core financial operations systems – embedded, and are still waiting to see a return on that investment.
Finance systems usually yield a lot of management information, or business intelligence and standard reporting – but this isn’t the same as analytics.
When organisations start implementing analytics many go back and look at standard reports and find that not many people are actually using them, which begs the questions ‘if not, why not?’ and ‘where do business leaders get the management information they need to make better decisions?’
Data is the fuel
Implementing an ERP system is a massive capital outlay, but many firms put in systems for general ledgers, procure to pay, and order to cash without always looking at how to drive efficiency in those processes or how to automate them. Other areas, for example tax, are often not effectively catered for in these implementations, leaving manual processes outside core systems.
Data analytics is like the layer across the top – if you don’t have the ERP system you don’t have the source data to take advantage of the emerging technologies. The transaction data captured in the ERP or finance system is ultimately the raw material, or fuel.
If companies are not using a structured ERP system, then it is very hard to implement any data analytics or process automation, or take advantage of other emerging technologies.
Whatever you do down the line, data is always the foundation. To apply technologies like AI down the line you’ve got to have your data in order.
AI is really about training machines to automate decisions. Machine learning algorithms are ultimately based on statistical models, and these modelling techniques require data, so the better the data the better the opportunities.
From hindsight to insight
As soon as you start implementing data analytics, hindsight is the first piece you look at – i.e. what has happened over the last time period? As soon as you start showing business leaders what has happened, they start asking questions.
It’s the information your stakeholders are looking for from the finance function, and what guides your data analytics journey. Once you understand and start to visualise what has happened, you can start to explore why things are happening, then begin to predict and forecast what might happen in future, or what might happen given certain scenarios.
Data analytics is an iterative cycle – it keeps going. When behaviour changes, you have to keep monitoring and tweaking models.
The organisations that get best value from data are those that are good at consuming the output of analytical cycles to make decisions and take actions.