In my two decades of experience in analytics, I have witnessed a startling trend across various industries and ownership structures: companies consistently miss game-changing opportunities to leverage their data effectively for critical decision-making. Executives often attribute this shortcoming to a myriad of reasons:
While these challenges are legitimate, the question remains: why do these problems persist, and why do companies often neglect to prioritize and fund data initiatives appropriately?
The answer may lie in a fundamental misconception about the nature of data. As one CFO bluntly put it, “Data initiatives are not sexy! They are costly and don’t always provide a sufficient ROI. When I put in a new manufacturing line, I know revenue will come as a result.” This mindset reveals a critical need to reframe our thinking about data, especially in light of the modern CFO’s increasingly challenging role. The speed of business is faster than ever, while data continues to pile up, leaving the CFO caught in the middle and struggling to keep pace. We must recognize that data is a valuable asset — a currency that, when leveraged appropriately, can drive increased revenues, decreased costs, and enhanced profitability over time. Unlike immediate returns from tangible investments, the benefits of data require cultivation and patience, and the CFO plays a crucial role in advocating for and stewarding these long-term data initiatives.
To unlock the power of data, companies must address several key areas:
1. Reactive Business Intelligence: Moving Beyond the Rearview Mirror
While BI tools and dashboards provide essential snapshots of data for maintaining business pulse and understanding customers and products, they are often reactive and insufficient for proactive strategy. This limitation occurs due to an internal lack of understanding on what the data is supposed to solve for or an inability to leverage massive amounts of data in proactive analyses.
2. FP&A 2.0: Embracing Decision-Science
FP&A teams play a crucial role in translating strategic goals into financial plans and monitoring performance. However, most teams are reactive and fail to leverage decision-science, which requires using both historical and real-time data to identify trends, patterns, and correlations. This gap may be due to skill shortages or a lack of data scientists with statistical and machine learning expertise on the team.
3. Metrics Mayhem: Implementing a Robust Metrics Layer
Many companies rely on metrics and performance indicators to assess business health, but they often fail to invest in dedicated infrastructure to institutionalize these metrics effectively. Instead, they depend on scattered Excel files and reports, leading to increased costs and inefficiencies that result in excessive time spent on critical analyses. Furthermore, businesses frequently fall into the trap of creating metrics for the sake of having them, without ensuring strategic alignment. Metrics must be intentionally tied to business objectives to effectively communicate performance and drive meaningful results. When metrics are not aligned with the company’s goals, they can lead to wasted resources and a lack of focus on what truly matters, ultimately hindering organizational success.
4. Breaking Down the Silos: Democratizing Data
Decentralized analytics and data within specific departments often remain untapped by other teams that could benefit from them. This results in a lack of data governance frameworks and common platforms to manage and catalog data assets, hindering democratization and proper usage in relation to privacy and compliance.
The path to data-driven success is not easy, but the rewards are immeasurable. By treating data as a precious asset and investing in the people, processes, and technologies to harness its power, companies can unlock a wellspring of insights that drive profitability, innovation, and competitive advantage.
As leaders explore ways to become more data-driven, they should consider solutions like Cooper AI, born out of a desire to provide deeper insights into data by identifying the most impactful decision levers. By applying data science techniques and machine learning, Cooper AI uncovers patterns and makes connections faster than traditional methods, empowering leaders to make informed, proactive decisions that drive business success.
