Organizations use financial analytics to evaluate financial performance, build financial forecasts, identify risks that could impact stability, solvency, or liquidity, and to provide other financial insights.
Why is financial analytics important?
Finance analytics helps set financial policies, formulate long-term business plans, and identify investment opportunities. For example, a company can estimate its business valuation by analyzing revenue streams, asset values, investments, costs, and other factors. Projected profitability and cash flow drive operational decisions such as hiring, borrowing, and purchasing decisions.
How analytics is used in finance
Management can use financial analytics to justify growth and investment or to scale back operations.
Business operations that depend on financial analytics
The role of the CFO includes ensuring the business follows sound fiscal practices, approving major expenditures, and meeting regulatory compliance requirements. Operational decisions that impact revenue and profit require a business case that includes a financial analysis of costs, risks, projected growth, and return on investment.
Managing cash flow using financial analytics
As a CFO, knowing the cash flow in and out of the business is vital. Monitoring these flows and being aware of cash on hand and loan balances allow the CFO to calculate the cash burn rate, which is extrapolated to estimate the runway ahead. Managing cash flow is one of the executive team’s and board members’ biggest concerns, so they know when to tighten belts or how much to invest for growth.
IT financial analytics
Information technology Financial metrics play an essential part in managing information technology (IT) projects. Most financial elements of a project are related to headcount costs and asset costs. As project milestones are achieved, costs are reviewed. Missed milestones and scope creep will impact costs, so projects are phased, trimmed, or even cancelled if the slip is too great. Thanks to agile approaches, many small incremental milestones help control project costs with fewer surprises than traditional waterfall or monolithic projects.
When a business is getting started, it requires cash investments that can include a seed round from angel investors and multiple rounds from a venture capital firm or private equity fund. The critical financial analytics investors need to show return-on-equity and projections for when the business will become profitable. No entity wants to invest in a business that will not eventually turn a profit.
Analytics for financial risk
Businesses must constantly change to align with the economy, customer demands and competitive pressures. Each change carries risks that must be analyzed and quantified. Financial analytics can predict the impact of change and the magnitude of the financial hit if the change causes a company not to meet growth expectations. Best Buy, for example, tried to expand to the UK, costing it $200 million. After two years without the strong brand recognition it enjoyed in the US, the UK operation folded, resulting in a $77 million loss. Fortunately, Best Buy had sufficient reserves to survive the loss.
Financial analytics in lending
The lending industry relies heavily on analytics. Credit agencies set credit scores for individuals based on credit history. The credit score considers multiple factors, including the number of open accounts, settled accounts, credit balances, and missed payments. Metrics that drive affordability include income, outstanding debt, and the income-to-debt ratio. Lenders also need to see savings balances and other investments to gauge risk.
Financial analytics in fraud detection
Artificial intelligence (AI) techniques such as machine learning (ML) assess fraud risk for real-time purchases by considering factors such as historical transactions, geographic locations, and purchase types to look for anomalies.
Financial analytics in marketing
Marketing is increasingly a metric-driven business function. Every dollar of campaign spend is compared and segmented into buckets to assess the relative effectiveness. Channels, keywords, and messaging changes are measured in a constant refinement process to ensure spending is directed to campaigns with the best return. Metrics such as the number of visitors and click-throughs in emails and ads provide plenty of data for analysis.
Stock, currency, and commodities trading considers many metrics to assess opportunities and risk. SEC filings uncover asset values, revenues, losses, and profits.
Refinitiv is a company that provides information about companies, such as their PE ratio, investor news, and more, through its stock information subscription services. Refinitiv relies on Vector columnar database instances to ensure that updates are provided to its ELEKTRON service subscribers in a split second.
OpenROAD for financial analytics
OpenROAD’s fourth-generation programming language lets customers build portable, database-centric applications quickly and declaratively with minimal coding. OpenROAD provides a built-in library of financial functions, including the following:
- The CTERM function calculates the number of compounding periods required for an investment to yield a specified future value.
- The DDB function calculates double-declining depreciation for a specified period.
- The DEPR function calculates the depreciation of assets.
- The IPMT function calculates the interest portion of a payment for a specified period.
- The NPER function determines the number of periods required for an investment to mature or a loan to be repaid.
- The PMT function calculates the periodic payment for a loan, given the principal, interest rate, and number of periods in the life of the loan.
The Actian Data Platform
The Actian Data Platform makes gathering and analyzing data easy using its built-in DataConnect integration technology. Its Columnar Database technology runs analytic queries faster than comparable technologies. The Actian Data Platform provides a multi-cloud solution to run analytics on-premises and in the cloud.