Digital Transformation » Big Data » Predictive analytics “more necessary than ever”

Predictive analytics “more necessary than ever”

Finance leaders have had to make business-critical decisions faster than ever before fuelling a wider focus on the role of Big Data and predictive analytics

Making efficient and strategic decisions has become more important than ever for finance leaders in a world where organisations are in the middle of their finance transformation and are pivoting themselves for growth.

As such, the role of predictive analytics has become a vital tool in their toolkit to help navigate the current uncertain waters and provide a competitive edge, says Fabio Italiano, head of applied innovation at Alteryx.

“We’re dealing with a shortage of goods and interactions with supply chain. The more you make use of your data and build simulations and scenarios, the more you can be prepared for such disruptions.

“A combination of predictive models and simulations will help paint the scenarios. […] It’s becoming more and more necessary as unpredictable events – from geopolitical to health to climate change to disruption in supply chain – all need to be accounted for.”

Predictive analytics is playing a greater role in strategic and operational decision making, with Italiano noting that “decision without data is of no value”.

In fact, 49 percent of senior executives assert that the greatest benefit of using analytics is that it is a key factor in better decision-making capabilities, according to a report by Deloitte. While 16 percent believe that its greatest benefit is enabling key strategic initiatives.

The role of a CFO, more recently, is to “balance the fine line” between managing resources and the ability to not hamper the growth of initiatives that are outside the realm of regular operations, explains Italiano.

“If you have data, you always want to have insights on what happened and to display the reasons why. You want to utilise the data to have insights into the future as much as possible. The predictive models will help you to get there.”

Supporting the finance transformation

The pandemic saw a shift in the CFO playing a bigger role in strategic decisions as a result of their participation in business model transformation.

“When you go through this kind of transformation, you almost always have to account for a dip in initial revenue and then forecast in the long run how that’s going to play out.”

The more analytical support you have, the better strategic and operational decisions a business is going to make which will limit the impact of the transformation, says Italiano.

Predictive analytics are often associated with supply chains, marketing, and HR but the office of Finance has a ton of room for improving and automating reports and analysis, he adds.

“CFOs are savvy enough to embrace automation and predictive. They’re savvy enough to understand that is necessity for upscaling resources but I’m not sure they quite see the predictive benefit in their own functions.”

Those applying predictive analytics to their own function, such as in cash, audit, and FP&A, will have a competitive advantage over their counterparts, he says.

However, organisations may need to upskill employees to support and reap the benefits of a finance transformation.

“It’s about upskilling people and establishing an environment where knowledge sharing is critical especially when the company has multiple divisions spread across globally, […] it will save a lot of time and build synergies across your entire group.”

Automating analytics provides a competitive advantage for organisations as it frees up mental resources which can then be used to focus on high value activities, says Italiano.

“If you put the right algorithm and the right process behind it, then the time to value is going to be much shorter [giving more] time to dedicate to more value-added activity to opening up the organisation to new opportunities and to look at the business from different angles.”

Companies nowadays are dealing with multiple data sources meaning it’s even more important for firms to be equipped to ingest the data, says Italiano.

“Building that data efficiency and data pipeline, […] that are effectively joining all the merging data sets and making that available to decision makers, finding insights and putting them together is the biggest challenge [for companies].”

However, when done correctly, “it’s the highest reward” for companies giving them an advantage over their peers, adds Italiano.

To find out more on the power of predictive analytics and how to use automation to get the most from your data, sign up to Alteryx’s webinar here.


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