A new generation of data management methods and tools is emerging, helping finance professionals to resolve complex data challenges in a short period of time.
Recent research from Deloitte shows how leaders can deploy digital financial skills in less time than before in their organizations. Machine learning and advanced analytics are just a few technologies that can help finance directors resolve data challenges without the need for large-scale investment or company-wide disruption.
According to the business, such technologies are already being utilized to improve corporate-level forecasts, automate reconciliations, streamline reporting, and generate customer and financial insights.
Today, more CFOs are becoming interested in data management as they have recognized the importance of using data-driven insights to make business choices.
In addition, CFOs and other C-level executives are becoming more directly involved in data projects, collaborating closely with their CIOs and data officers to drive data initiatives for the aspects of the business they are responsible for.
As businesses create vast volumes of data daily, finance teams have what appears to be an endless number of possibilities to gain new insights and increase their value to the organization. However, as the business pointed out, saying it is more straightforward than executing it. The difficulty is that the volume of data generated every day by numerous sources might be overwhelming at times. This is referred to as “the data tsunami” in Deloitte’s Finance 2025 series. Businesses require a viable means to gather, analyze, and act on massive amounts of data to manage it effectively.
These innovations can also aid in the reduction of the costs, effort, and risk involved with the shift to digital finance. As a result, if data quality is an issue—and finance leaders are weary of hearing the phrase “the systems don’t communicate to each other,” Deloitte suggests investigating other solutions.
Managing Data in Finance
Finance is changing due to digital technologies, which are cutting operational costs, boosting efficiency, and reducing risk while strengthening the analytic value and openness of financial information. Deloitte outlined how finance teams utilize these technologies to address data issues in their recent report.
Financial planning and analysis software like PCF can help unlock insights through advanced data analytics and improve collaboration on the cloud, making data available from any location, including the ERP system. Through PCF, organizations can improve the consistency of their data definitions across different divisions, regions, and information-gathering systems.
It also supports interactive reports that allow consumers to explore several layers of information at any given time.
At the core of effective data management is transitioning from spreadsheets and intuition to models supported by automation and analytics like Performance Canvas Financials.
Develop integrations between cloud-based planning systems and data lakes to satisfy internal and external data requirements.
Ensure that data categories are consistent and that federated aggregation procedures are implemented from the corporate core.
Finance and accounting operations
Establish hierarchies that are flexible enough to meet changing managerial, financial, and regulatory reporting requirements.
Simplify processes and automate reconciliations across projects to improve journal entries and audit response transparency while reducing costs and increasing efficiency.
Make use of sophisticated analytics, including machine learning, to identify exceptions and potential risks.
Making use of technology like Performance Canvas to Augment Your Data Management Needs
Breakthroughs provide CFOs with additional alternatives for data management, which is especially useful when an organization’s present systems are not communicating with one another. “Cloud-based architecture can organize and reconstruct data on the fly,” according to Deloitte. “Advanced analytics technologies enable you to derive inferences from data points spanning various platforms using a single set of algorithms. Machine learning and artificial intelligence help get organizations to apply controls and monitor hazards, allowing for better adjustments as they
Finance leaders should start by analyzing their existing capabilities and move toward automating and enhancing data generation, distribution, and consumption in the future.
This can be done by exploring and assessing your business’s intelligence needs. Consider the technologies available to gather, analyze, and disseminate data.
Make a plan and start building a data ecosystem that supports automation, integration, and access to additional self-service tools. PCF was created with this in mind, giving users access to world-class financial software without breaking the bank.
Book a free demo today and explore how PCF can help your business.