Financial analytics, also known as financial analytics, provides different perspectives on financial data related to a particular business, providing insights that will facilitate strategic decisions and actions that will improve the overall performance of the business. In relation to business intelligence and enterprise performance management, financial analytics plays an important role in calculating profits, answering questions about a business, and making business predictions about the future, affecting virtually all aspects of a business.

Challenges with Financial Analytics

In many ways, CFOs (director of financial affairs) find themselves pursuing two opposite goals. As the cost center of the enterprise, finance must rely on strict cost reduction obligations and fixed budgets. But at the same time, increasing regulatory and governance requirements require CFOs to provide unprecedented levels of financial transparency and decision support.

CFOs are asked to integrate big data when their own financial institutions are probably not in complete order—or at least not in an order that provides the necessary, actionable insights that will lead to detailed results. Often, CFOs rely heavily on a network of unnecessarily complex, disconnected financial systems that require error-prone reconciliation and verification. This can lead to inconsistent or misreported results, as well as internal “data struggles” that explode when divisions have conflicting definitions of net income, gross margin, or cost of sales. The resulting controversies delay management decisions and negatively affect its quality.

As responsible for financial data reported to both regulators and external stakeholders, CFOs must intervene in breach situations and advocate for advanced data management practices that resolve disputes involving any information affecting financial statements. This is the only way to ensure that their company's overall company performance works from a single, reliable, transparent perspective.

CFOs need analytical skills more than ever because Ledger data is no longer sufficient to meet regulatory and stakeholder transparency demands. Financial, management, and regulatory reporting all require the ability to integrate more sub-ledger details than in the past (e.g. accounts receivable, inventory, and accounts payable) and the ability to integrate increasing amounts of non-financial data (e.g., collateral, supplier, and customer). With the right infrastructure and data-driven orientation, Finance can assist all units of the business in making more informed decisions. One of the main things we see CFOs being assigned is Order to Collection Optimization and Payment Provision Processes. Overcoming this challenge requires a detailed connection between the financial statements and the Sub-Ledger information collected in the Master Book. This requirement is not only incompatible with the traditional role of financial data controller of Finance, but it is also a natural progression in companies where CIOs (head of information) report to CFOs, and whose numbers are increasing in number.

Core Competencies of Financial Analytics

To ensure their departments are data-driven, CFOs need to work with IT to embark on a phased journey toward a simplified financial system architecture that eliminates redundancy, strengthens integration, and maximizes automation. Combining all of finance's diverse data sources—from point-of-sale devices, customer billing and mortgage lending systems to brand-name and native ERP's (enterprise resource planning), accounting centers, and rules-based cost allocation calculation engines—around a single, integrated data repository, transforms CFO organization effectiveness and efficiency can.

Many financial organizations realize this by redesigning their financial system architectures with five core competencies in mind: agility, sustainability, extensibility, predictability, and accountability.

· Agility: It's about the CFO responding to change and promoting change.

· Sustainability: Financial analyzes based on a decision-making environment that can be constantly updated and improved with minimal effort.

· Extensibility: architectures designed taking into account future data that can generate incremental business value coupled with initial data.

· Predictability: It depends on the interaction of revenues with costs, providing CFOs with detailed business insight to identify and act accordingly on priority activities that can increase future profitability and help avoid unnecessary costs.

· Accountability: a framework that regulates strategy and implementation across the enterprise, with the aim of executing the business on a fact base that is decided by common metrics.