Data-Driven Financial Analysis Support: Enhancing Decision-Making with Actionable Insights

The Finance department of your organization is a critical component of company success. It provides the financial information that business leaders use to make decisions, from determining company goals and objectives to building dynamic profit and loss statements and streamlining budgeting and forecasting processes. Finance is also responsible for identifying and mitigating financial risks that could threaten the financial health of the organization. A finance data analytics approach empowers the CFO and finance team to advance decision-making, increase effectiveness and productivity, strengthen risk management capabilities and better anticipate future results by leveraging predictive risk assessment functionalities.

But for finance teams to successfully leverage data analytics, they need access to the right information at the right time. Many companies struggle to do this as data is stored across various systems and applications in different formats, making it difficult to access the full set of relevant data for analysis and creating a bottleneck in delivering insights. Modern cloud-native data platforms offer a single source of truth that brings together all the information needed to support data driven financial analysis.

Using a Data-Driven Financial Analysis Support such as Phocas allows your teams to access all the information they need in real-time and with the necessary granularity to craft effective strategies and to make smarter business decisions. For example, a Retailer that doesn’t track sales at the hourly and day-of level misses critical data points that inform cost saving measures and that help them to forecast revenue more accurately.

Inefficient data processes and dysfunctional reporting are estimated to cost US businesses $7.8 billion a year according to research by DataRails. The good news is that finance departments can reduce these costs by leveraging a comprehensive, end-to-end data analytics solution such as Phocas Software. Designed to streamline the end-to-end financial data processing and management process, it enables finance departments to eliminate manual steps and automate repetitive tasks to cut down on costly errors and boost efficiency.

By combining internal data sets with external market information, the platform allows users to analyze historical trends and forecast future performance and outcomes more accurately. Data visualization tools make it easy to uncover patterns and relationships within large datasets that would be otherwise impossible to discern manually. Machine learning can also evaluate customer behavior and identify broader trends to aid in the personalization of services or marketing initiatives.

The ability to rapidly analyze the impact of new business investments or expansion plans is another important function of a financial data analytics solution. With a clear understanding of the potential impact, the CFO and finance team can make more informed decision-making to support their company’s strategic direction. For example, a retail brand considering a major capital investment in a new line of products can model the results of a product launch using data on customer demographics and purchasing patterns and compare this to existing market data to determine whether the venture will be profitable.