What is financial data analytics?

Financial data analytics and business intelligence

Financial data analytics is a subcategory of business intelligence (BI) and enterprise performance management solutions. BI combines technologies and strategies to analyze raw data and provide reports that help management make critical decisions. Enterprise performance management involves management and evaluation processes to help organizations operate at optimal levels.

Financial analysts use tools and software to analyze real-time financial information and forecast, plan, budget, and produce reports. This helps enhance business processes by delivering relevant and tailored reports that enhance the decision-making process.

How financial data analytics are used in business

Financial data analytics are uniquely designed to be used by financial professionals and senior management. They are used in businesses to enhance the decision-making process. Organizations use financial data analytics to perform several critical functions, including establishing organizational objectives and goals, spending, budgeting, forecasting, and creating dynamic profit and loss statements. These goals have company-wide performance applications and provide a single source of valuable information for decision makers within an organization.

Analytics can be broken down into a number of tasks: financial modeling, financial reporting, predictive modeling, and descriptive modeling.

Financial modeling is the development of models that show accounting performance for all relevant financial factors, growth, and risks assumptions. There are also tools that provide capabilities to monitor ongoing performances and costs against revenues.

Financial reporting is the presentation of an organization’s financial situation and includes sections such as income statements, balance sheets, cash flow statements, valuations, sensitivity analysis, and significant assumptions.

Predictive modeling relies on techniques used to predict future performances and events using historical financial data. These techniques include artificial intelligence, machine learning, data mining, modeling, and statistics. These techniques are used to analyze current financial data to predict future performances.

Descriptive modeling consists of applying mathematical models that describe historical performances, business relationships, and suggest factors that caused these outcomes.

Why organizations use financial data analytics

Modern business organizations require timely information and insights to make crucial decisions. Organizations require prudent financial planning and forecasting to have longevity and remain competitive. Financial data analytics is designed to help organizations achieve these requirements. Recent advancements in technology and the promise of insights from big data make analytics more important than ever before. If an organization is not moving forward, they are falling behind.

In-depth insights on financial status

Organizations use financial data analytics to gain in-depth insights into their financial status and areas that need attention and improvements, such as cash flows, assets management, profitability, and business value. The ability to have reports that allow drill-down into granular detail means that not only can organizations be informed of trends, but they can track down causes of these problems.

Creating and maintaining dashboards

Financial data analysts use analytics to create and maintain dashboards and spreadsheets. These help organizations obtain necessary insights to maintain effective strategy, meet key performance indicators, and predict future achievement. Dashboards also ensure that everyone, from top management to junior associates, has access to the same information.

Develop suggestive approaches

Financial data analytics is used to develop suggestive approaches that will enhance performance. This is achieved through enhanced analysis of financial results, trends identification, and forecasting.

Creating budgets and plans

Financial data analytics is used to create budgets, plan for expenses, research competitor performance, and establish effective mechanisms to improve management.

Enhancing decision-making

Financial data analytics enhances internal decision making and provides exceptional financial knowledge and insights to help organizations adapt to a rapidly changing business environment. Apart from financial analysis and forecasting, financial data analytics also analyze risks, allowing stakeholders to identify and avoid risky investment opportunities.

Features of financial data analytics

Some of the key tasks of financial data analytics include:

Predictive sales analytics

Predictive sales analytics helps organizations extract accurate sales projections. Sales analytics provides organizations with informed sales forecasts that have technical and strategic implications. Predictive sales analytics involve approaches such as correlation analysis and other predictive models to predict future sales performances. These take into account factors such as historical performance, holiday sales, and even social trends.

Customer profitability

Customer profitability helps business organizations to analyze all customer groups and generate valuable insights concerning a client’s contribution to the organization. The assessment of a client's productivity can help reduce default risks and losses. This also helps inform organizations of their ideal persona, which can help with targeted marketing in the future.

Cash flow analytics

Cash flow analytics gauges a business’s financial health with essential ratio applications and real-time indicators such as cash conversion cycles and working capital ratios. This feature also includes regression analysis and related tools to predict cash flows. Organizations use cash flow analytics to manage cash flows and ensure they maintain adequate cash amounts to run critical business operations.

Value-driven analytics

Organizations use value-driven analytics to establish formal goals and list goals on a strategy map. This includes consideration of the organization's value drivers. Value-driven analytics helps identify the key elements required to realize strategic organizational goals. Overall, this component of financial data analytics ensures that value drivers can help the organization achieve its expected performance outcomes.

Shareholder value analytics

Shareholder value analytics helps examine returns on investments and how these investments affect the value of the company and its shareholders. Analytics demonstrates how an organization’s strategies affect a company's stock performance or shareholder value. This feature is used concurrently with profit and revenue analytics—and uses tools and measures such as economic value to measure strategy and performance impact on shareholder value.

Why do organizations need financial data analytics?

To help make important financial decisions

Business owners need financial data analytics for several critical reasons. The primary reason is to help business owners make important data-based financial decisions. No more guesses or instinct needed, as analytics provides clear evidence of past success, failures, and ways to move forward.

Timely overview of financial status

Another reason why business owners should consider financial data analytics is to obtain a timely overview of a company's financial status. Business owners may consider financial analytics when they need to examine financial performances dynamically instead of only utilizing static reports. If a trend starts to occur, systems can alert management before it becomes problematic, allowing action to be taken immediately to change the course.

Reduce organizational waste

Business owners need to use financial data analytics to minimize organizational waste and increase revenues. If there are bottlenecks, problems such as over-ordering stock, or problems in a manufacturing sequence—data analytics can find these problems. This allows the management team to fix the problem, reducing waste of materials and time.

Reduce risks and fraud cases

Banks, fintechs, insurance organizations, medical facilities, and other institutions can use credit scoring and customer analytics to identify fraudulent transactions or high risk customers.

Benefits of financial data analytics

Financial analysts use data analytics to filter and analyze sizable financial data volumes efficiently and quickly. This means that financial reporting provides accurate and reliable insights for businesses. There are several benefits of financial data analytics for organizations and their stakeholders.

Providing timely information

Financial analytics ensures that business owners and managers obtain timely information for decision-making purposes. Reporting can be in real time; yesterday’s data is often no longer relevant. For example, if someone fraudulently applies for a loan, financial data analytics provides accurate identification and stops the process before any money exchanges hands.

Measurement and management of tangible assets

Organizations can use financial analytics to enhance the measurement and management of tangible assets, including cash and equipment. This is achieved by analyzing efficiency ratios and financial data to measure an asset's ability to generate cash flows and revenues. Examples of such ratios include the fixed assets and inventory turnover ratios. Overall, financial data analytics helps organizations obtain in-depth insight on the financial health of a company.

Improve strategic management process

Insights help organizations develop effective strategies to improve cash flow management, profitability, business, and shareholder value. Regardless of the business model, financial data analytics integrates customer data and patterns to provide insights on financial health and potential future performances. Financial data analytics uses technologies and tools that have led to considerable evolution of the finance department's role from accounting to management. Therefore, the outcomes can generate insights that increase business value.

Increased confidence in decision making

More importantly, organizations become data-driven organizations, and this helps them make investment and operational decisions with higher confidence. Business owners and managers can obtain timely information required for decision-making purposes due to financial data analytics’ capability to compile and interpret financial data easily and quickly.

Challenges of financial data analytics

Numerous challenges hinder the full adoption and implementation of financial data analytics despite its notable benefits.

Poor financial data quality

The first significant challenge is the poor financial data quality from accounting systems and other departments. Such data can be inconsistent, out-of-date, or incomplete. Organizations that use advanced analytics such as artificial intelligence and machine learning algorithms to analyze data always need high-quality and relevant data.

Business organizations can start to address data challenges by selecting cloud-based finance analytics software. This ensures secure integration of different data types and sources, simplifies data storage, aggregation, accessing, and cataloging, and tackles data quality issues.

Furthermore, organizations must address data quality issues by implementing data governance programs and strategies. Organizations can ensure data centralization and documentation to guarantee quality and consistency.

Poor executive support and lack of cultural buy-in

Another challenge is the lack of executive and cultural buy-in. This is important since financial data analytics may require tools and software that need considerable financial investments. Cultural buy-in is crucial for addressing worker perceptions or problems.

The organization's executives must take a leading role to champion cultural change and positive acceptance of financial analytics tools and processes.

Lack of skills and competencies

The issue of digital finance competency could negatively affect the performance and expected outcome of financial data analytics. Competencies and skills shortages could affect adoption and innovation in using financial data analytics.

Organizations should invest in training the finance department's existing talent to address a skills gap. Training and continuous development of existing employees can improve job satisfaction and performance.

Financial reporting or financial data analytics?

Individuals can interpret financial data analytics and financial reporting to mean two similar processes in an organization's finance departments. The two processes are similar in that they rely on data and information from the finance and accounting departments. In addition, financial reporting and financial data analytics involve software that budgets and provides financial reports. The management and decision makers then use these reports to make decisions that impact the future performances and direction of the organization.

However, this is where similarities end. One significant difference is that financial reports are mandatory requirements, and organizations are expected to produce audited financial reports at the end of a given financial period. Conversely, financial data analytics is not mandated.

Traditional financial reports are static and based entirely on historical data. There is no ability to drill down into granular information, no way to extrapolate insights for the future, and they do not usually allow for ad-hoc reporting.

This is why financial data analytics, and the reports they produce, are so essential. They equip businesses with far more ability to create flexible, dynamic, and insightful predictions and data. For organizations that want to succeed, financial data analytics must be implemented and used wisely.