Top Data and Analytics Trends for CFOs in 2022
Data Analytics / March, 21 2022

CFOs and Data Analytics – Moving Beyond Financial Management

Finance teams can no longer think of just management reporting and streamlining processes. Traditionally, the CFO role was about creating financial reports, managing investor relations, and steering the strategic direction for growth. Over the years, the CFO role has transformed from being transactional to strategy and business pioneering. As per Gartner, advanced data analytics is now the top priority for CFOs.

What do you think are the challenges for the CFOs in the ‘New Normal.’ Most of them still face issues with too many responsibilities, managing cashflows, and accessing accurate real-time information. During the pandemic, CFOs kept a tab on the real-time data from invoices, accounts receivables, advanced analytics on products and services, and critical value drivers differentiated themselves from the competition.

The shift to a data-driven culture is also evident across industries in recent times. Data-driven insights are not just the game-changer for the organizations but also the new currency for CFOs to transform and evolve their business strategy. Earlier CFOs and finance functions were at the center stage to redefine the evolving management reporting and business intelligence. Now, CFOs and data analytics facilitate the business functions speak to each other to analyze every business metric from supply chain tracking to consumer sentiment and personalized experiences.

How is real-time data changing perspectives? Digital transformation and rapid changes in advanced analytics now demand CFOs to focus on more real-time data. In the past finance, teams focused on historical data for management reporting but now the shift towards on-demand analytics is necessitating access to real-time data. With all these changes, CFOs now have the potential to transform their role into chief analytics officers for broader reach and strategic planning. But they still tend to spend 2.2 hours per day juggling through spreadsheets, says Deloitte. On a broader scale, all of us agree on the transformation with data and analytics but are still struggling with various internal issues.

Most importantly, CFOs need to adapt to the latest data and analytics initiatives before they mature, as they are now the producers and consumers of huge volumes of data. Let us look at a few data and analytics trends specific to the finance function.

Top Data and Analytics Trends for CFOs in 2022

CFOs are the new drivers of digital strategy and digital transformation. Hence, they should evaluate the must take digital investments on an ongoing basis to mitigate the risk of varied advanced analytics utilization across the organization. Let us take a deep dive into the data and analytics trends for CFOs in 2022.

  1. Data storytelling – Traditional dashboards and management reporting lack context, and stakeholders still face challenges interpreting them quickly. Though the business intelligence systems are sophisticated, users require AI and ML capabilities to augment them. In-context and real-time data stories powered by AI and ML reduce the manual effort spent on predefined dashboards and financial planning and analysis. By 2025, data storytelling will gain prominence, and augmented analytics techniques occupy the principal share, says Gartner.
  2. Intelligent data management – Finance teams face many challenges in providing current, consistent, and complete data that reflects business performance. A lot of effort still goes into organizing the data to use it for decision-making; more than 55% of finance teams still struggle with these needs. If automated leveraging AI and ML, the data management tasks can improve the scale and speed while also reducing the data-trust issues. Intelligent data management to reduce the efforts by financial analysts while also freeing their time up to 20% for high-value tasks.
  3. Extensive cloud deployment – Though it is fast-tracked, the cloud transition is still at an uneven pace. Finance teams face significant challenges in data governance and integration. Rather than considering the cloud as a cost optimization driver, finance teams can ensure the relative tradeoffs between performance and price. Moreover, CFOs should consider collaboration between other data and analytics teams for an integrated approach to moving towards the cloud.
  4. Unified data analytics tools and platforms – Analytics and business intelligence are less defined as advanced tools. Finance cannot invest more in fragmented data and analytics tools to avoid analytics silos and incompatibility for more extensive organization needs. For effective unification of advanced analytics tools and platforms, finance teams can expand roles and processes, anticipate data and analytics trends changes, and facilitate more internal collaboration.

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Effective ways to enhance data and analytics

The CFO reports and forecasts require a deep dive into every business function and customer level. As organizations can access a massive volume of data internally and externally, CEOs and other C-suite executives rely on CFOs and data analytics for on-demand reporting beyond the boundaries. So let us look at a few practices that CFOs need to implement to enhance their utilization of data and analytics.

Removing barriers between business functions – For some time, forecasts and insights were based on the data that the respective business function shares. With the pandemic, this scenario is changing; finance teams need to provide insights on demand. The finance team needs to be an integral part of the operations teams to find the critical information as the event triggers. Better collaboration can make data more intelligent and create value drivers and growth strategies for businesses. CFOs and data analytics in unison can understand the business functions better and leverage historical data to identify future trends. It is not that machine learning drives everything; interpretation and a keen eye for detail are still valued operations by CFOs and finance teams.

Real-time metrics – Finance teams were retrospective, centered around financial planning and accounting. The routine analytics centered around working capital, cash flow, and invoice management improved performance and efficiency, but not very much beyond finance. More on-demand insights like supply chain disruptions, inventory management, and personalized offers are now in the arsenal of CFOs and finance teams. Hence accessing the data in real-time and from a centralized repository is critical for finance leaders.

Centers of Excellence with rights skills, people, and technology – CFOs and data analytics need to move beyond the traditional mindset of management reporting and financial analysis. The data sets that produce advanced analytics are more fluidic than ever before. So, this brings in reluctance for CFOs to plunge into advanced data lake architectures. Fortunately, this can be overcome by setting up centers of excellence with the right people and technology mix.

Data Governance – Data governance is an effective strategy to ensure oversight and control of organizational data. Most organizations consider data governance out of the purview of the CFOs; the finance team needs to be involved in setting up mature data governance policies and frameworks. If done so, CFOs can trust the data utilized for reporting and analytics while ensuring enterprise data security, quality, and integrity controls.

Identify and address the skills gap – As the data analytics skills evolve continuously and the demand for digital skills continues to grow exponentially, maintaining the talent pool that can develop the right advanced analytics solutions is the biggest challenge. The skillset requirement also varies for finance teams. It is not just the understanding of financial concepts, but a digital proficiency focused on future perspective rather than historical analysis. Irrespective of the size of your organization, finance teams now need to have fluency with artificial intelligence and automation alongside the ability to improve data analytics for strategic direction continuously.

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