5 barriers to adopting advanced analytics in finance
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.
Business leaders largely agree that data from finance is often out-of-date, inconsistent, or incomplete.
Clement Christensen, Senior Principal Advisor, Gartner
- 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.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:
- Tackle the most pressing quality issues first
Data issues don’t need to be resolved all at once. Getting your data decision-ready can be achieved in targeted increments. Start with the common data that supports your core, strategic KPIs. By standardizing it, you will bring missing, incomplete, and duplicate data to the surface – giving you a manageable first target for your data quality improvement project.
- Focus on a “sufficient” version of the truth
The benefits of achieving a single source of truth, or centralized data shared across an organization, are well documented. But if fulfilling that goal is still years away, there’s an interim solution: pursue a “sufficient versions of the truth” strategy. This involves making trade-offs between the cost of bad data and the effort needed for additional governance.
Setting data quality requirements is an important first step. Which level is critical? Which data definitions, such as ROI and profitability, are most important to financial and BI reporting? And which can be set aside for a later project?
According to Gartner, organizations that focus on a sufficient version of the truth are “41% more likely to generate decision-ready data and twice as likely to improve the quality of decision-making and business outcomes” – making it a viable way to start tackling the data quality barrier.
- Distribute data governance
Even without a single source of truth, finance departments still need a framework to ensure data quality and consistency. But most teams don’t have the time to implement and run their own data governance program. With a distributed data governance strategy, finance provides guidance on which data to govern and how to govern it, but they let others do the actual governing. This type of framework makes it easier to gauge which data quality issues are most critical – and where to focus improvement efforts. Mutually agreed-upon frameworks also create trust that the data is sufficient for decision-making, even if it isn’t 100% perfect.
- Choose cloud-based finance analytics software
Cloud-based advanced analytics software can securely integrate with more data types and sources – financial and operational, internal and external – than on-premise solutions. They simplify how data is stored, cataloged, aggregated, and accessed – so finance teams can spend less time managing data and more on extracting insights and putting them to use.
- Tackle the most pressing quality issues first
- 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.
- 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.
- Champion change from the top
In the wake of the COVID-19 pandemic, many CFOs have been tasked with dueling priorities: reducing costs while also accelerating digital transformation initiatives and investments. Given this reality, demonstrating the value of advanced finance analytics projects is key to acquiring budget and buy-in.
Quantifying and conveying the value of these initiatives – including use cases, ROI, and time savings based on autonomous (or semi-autonomous) analytics – will help offset and justify initial investments. In addition, finance will be in a position to clearly demonstrate their value when their strategic business advice is based on deeper insights and more accurate predictions.
Another approach is to identify a list of advanced analytics pilot projects – with each geared towards solving a specific business problem and leveraging an available data set. Every successful pilot will provide the evidence and confidence needed to justify the next or larger project.
- Shift culture and get buy-in
Sometimes the barrier is ingrained into the culture of the finance department itself. It can be challenging to get teams to give up familiar tools and traditional ways of working. But the solution can be as simple as demonstrating how much time can be saved by transitioning to newer tools that can automate complex calculations and processes for budgeting, forecasting, and scenario planning.
- Assess your analytics maturity level
A certain level of analytical maturity is needed before companies can successfully implement more advanced analytics. The International FP&A Board has an FP&A Analytics Maturity Model you can use to assess your current level and determine next steps. It has five levels: basic, developing, defined, advanced, and leading.
Finance teams at the basic end of the spectrum don’t have formal analytical processes or business intelligence systems – and rely on rudimentary tools for planning and modeling. Whereas at the leading end of the spectrum, teams have tightly integrated planning processes and proactive, AI-driven analytics. Knowing your organization’s maturity level can help you create a roadmap of incremental improvements needed to advance analytical projects.
- Champion change from the top
- 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.
- 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.”
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
Advanced analytics for finance
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