Merger and acquisition activity in the financial services industry is expected to recover this year after a 19% decline in 2023. A successful recovery requires an increased emphasis on delivery speed across all organizational functions, especially finance, to enable effective management and direction through dynamic markets and enterprise growth.
Visionary CFOs are increasingly focused on creating value for their organizations by elevating their strategic decision-making processes, implementing predictive insights across functional teams, optimizing operations and encouraging an innovative workplace. Achieving these results requires a renewed focus on effective data governance and integrating modern technology architectures infused with tech-enabled solutions, including data analytics, automation and artificial intelligence.
Before implementing these technologies, leaders need to ensure that the solutions align closely with their company’s vision and that they address real business problems, like accelerating their close to 3-5 business days (from 7-10 business days), for example, and improving their day sales outstanding. By shifting automation and AI opportunities to the top of their strategic initiatives list, leaders can create capacity for an already stretched workforce, allowing them to focus on critical strategic initiatives that lead to immediate efficiency gains and a return on investment.
As part of this transformative process, organizations should implement robust data strategies that effectively govern and rationalize their data. This strategic approach ensures a return on investment through finance process automation while enhancing their overall data maturity. Strong data governance is essential in preparing an organization for future acquisitions, and it helps facilitate the seamless integration of new data and systems. Forward-thinking CFOs address these challenges in the following ways:
- By establishing robust data governance frameworks.
A crucial piece of maintaining data integrity and compliance with regulations is establishing clear roles for data stewards and custodians that includes defined policies, procedures and standards. This helps ensure data is effectively and appropriately collected, stored, managed and utilized. Without proper governance there is a risk of data breaches, legal penalties and a loss of trust from stakeholders. - By tracing data elements to understand data flow.
Documenting the transformation of organizational data across systems is essential for data accuracy and reporting, optimizing data taxonomies that reduce redundancies and latency, increasing efficiencies, and fostering new and improved reporting capabilities. Ignoring this can lead to increased data errors across systems, resulting in poor decision-making and organizational inefficiencies. - By unifying and optimizing technology architectural data flows.
Harmonizing data flows helps establish efficient and streamlined systems that facilitate the seamless transfer and processing of data across various verticals, including applications, databases, tools and devices, to minimize bottlenecks and enhance performance. Disjointed systems can increase costs and cause delays, impacting an organization’s ability to leverage data for critical insights. - By designing data and report rationalization strategies.
The development of these strategies will help standardize data, minimize data hoarding and ensure the harmonization of data silos across disparate systems. With rationalization strategies, organizations can prevent data overload and latency issues that make it difficult to extract actionable insights. - By streamlining and automating mapping processes.
Automation can significantly improve the integration and accuracy of data, ultimately improving the overall efficiency and accuracy of an organization’s data. Manual mapping processes are prone to errors and inefficiencies and can compromise data quality. - By developing meaningful reports and metrics.
This is key to measuring organizational performance, which helps guide business strategy while building scalability that allows an organization to mature to advanced analytics and AI/ML tools. Leveraging inadequate or incomplete metrics can cause misinformed decision-making and missed growth opportunities. - By facilitating continuous quality assurance and monitoring.
Implementing continuous data monitoring and quality assurance processes, like automated real-time data quality audit readouts, ensures that data is consistently accurate and reliable. Without ongoing quality checks, data issues could go unnoticed and affect the quality of actionable business insights. - By establishing data literacy and culture.
Fostering a data-literate culture empowers employees to make data-driven decisions by embracing and adapting to technological changes, creating upskill opportunities around tech-enabled solutions that drive further efficiencies and innovation. A lack of organizational data literacy can result in resistance to change, yielding a slower pace of innovation within the organization.
Data-driven readiness has been a priority for organizations for years, but many are experiencing mixed results. A Harvard Business Review article emphasized that executives find addressing cultural change—both at the worker and organizational levels—more challenging than solving technical problems. Further, excess levels of data and growing concerns over privacy and data ownership are making it more difficult for organizations to achieve their data goals.
Poor data quality continues to have significant financial implications for organizations, with businesses losing millions of dollars annually due to data quality issues. Additionally, many organizations do not actively measure the financial impact of poor data quality, causing reactive responses, missed growth opportunities, increased risks and a lower return on investment. Moreover, Asana found that 62% of the workday is lost to repetitive, mundane tasks, showcasing the impact of poor data quality on employee productivity and the importance of data governance in value creation. Achieving data maturity through a robust data governance framework protects institutions and shareholders and acts as a catalyst to unlock the untapped potential of organizational data assets.
AI and automation tools have become integrated into finance and accounting tasks, replacing manual efforts. While concerns about job displacement exist, these tools should be seen as opportunities for professionals to focus on higher-value activities. As organizations embrace these technologies and foster a culture of adaptation and growth, their workforce will be empowered to achieve new levels of development, ensuring they are well-positioned to leverage their data assets for future expansion and acquisition opportunities.
