Advanced analytics in finance is a major priority for CFOs and their teams – and it’s easy to see why. These sophisticated, AI-powered analytics can help finance professionals discover deeper insights, make more accurate predictions, and excel in their role as strategic advisor to the business. But even though more than 80% of finance organizations forecast increased use of advanced analytics in 2021, adoption rates remain low. Why? What’s standing in the way? Here are the five top-cited barriers to adopting advanced analytics in finance – and how to overcome them.

  1. Poor quality financial data

Finance teams collect massive amounts of data from their own systems, from different departments, and from external sources. But the quality of this data often leaves much to be desired.

“Business leaders largely agree that data from finance is often out-of-date, inconsistent, or incomplete.”

Clement Christensen, Senior Principal Advisor, Gartner

Complex data landscapes with multiple systems and siloed applications make it difficult for data to be merged, aggregated, and standardized in a timely manner – which is a crucial step before it can be analyzed. Analytics, and especially advanced analytics that use artificial intelligence (AI) and machine learning algorithms to comb massive data sets, need current, high quality data – otherwise they won’t produce high quality results. This issue of poor data quality is one of the top barriers between finance teams and advanced analytics.

There are a number of ways finance teams can improve data quality and lay the groundwork for advanced analytics, including:

  1. Fear of failure

Digital transformation projects, like implementing advanced analytics, can be challenging – and aren’t always successful out of the gate. For many finance leaders, the fear of failing, even a little bit, is holding them back.

This fear can be amplified in corporations that have a “failure-phobic culture” where people fear being stigmatized for their mistakes – or where blame or finger-pointing is a common response when things go awry. For those organizations, a shift in mindset is needed. Consider that unexplored opportunities are also a form of failure leading to stagnation and lack of innovation. And companies that can’t adapt and innovate risk being left behind.

That said, there are better ways to fail than others. To embrace failure the right way, “fail small” and “fail forward.” Failing on a smaller scale means tackling finance analytics projects in smaller increments. That way any failures don’t eat up too much time or significantly impact other projects. Failing forward means analyzing what didn’t work and then applying lessons learned to the next project. This type of “failure stage” should be built into every innovation project – as it is ultimately a driving force for success.

Woman working on laptop
  1. Need for executive and cultural buy-in 

Sometimes the biggest barriers to adopting advanced analytics in finance are problems of perception or approach. Any big initiative needs someone to spearhead and champion it. Funding needs to be secured. And new ways of working need to be embraced at a team level.

“2021 is the year to pivot from discussions about the future to making real investments, seeing short-term wins and cost off-set, and having a clear plan for the future.”

Alex Bant, Chief of Research, Finance, Gartner
  1. Lack of time for advanced analytics initiatives

Sixty-seven percent of CFOs and their senior finance executives say that too many of their resources are tied up with legacy systems and traditional ways of working – leaving little time to innovate.

While it’s true the finance function is under pressure to do more with less, there are ways to free up time. One solution is to outsource implementation projects to a partner agency.  

Another is to invest in cloud software and tools that streamline financial processes and day-to-day activities. Some cloud-based finance and FP&A solutions offer built-in machine learning, AI, robotic process automation (RPA), and augmented analytics that can both automate processes and fast-track new technology adoption.

  1. Lack of digital finance competencies

Implementing and using advanced analytics in finance requires a high level of technological literacy. But many finance departments lack the digital competencies and skills required. In a 2020 PWC survey of CFOs, 54% of chief financial services executives said that skills shortages have interfered with their ability to innovate effectively. Underpinning this skills gap is a fear that AI and other advanced automation technologies will make existing finance jobs redundant.

These challenges can be overcome by using a multi-pronged approach. Even if new financial talent with the right skill set is scarce, upskilling existing employees is a worthy investment. Not only will it help close the skills gap, it will contribute to their professional development, confidence, and job satisfaction. And as they expand their digital skills and learn how to use these technologies, they typically worry less about being replaced.

Besides training, encourage team members to monitor trends, attend industry technology events, and actively seek out new learning opportunities. All of this will raise your teams’ digital dexterity, a term Gartner defines as “a set of beliefs, mindsets, and behaviors that help employees deliver faster and more valuable outcomes from digital initiatives.”  

Break down barriers, drive deeper insights, and make confident business decisions with cloud analytics.