Financial analytics can elevate business success by helping optimize business operations, identifying market opportunities and business risks, and improving an organization's financial and business strategy.
Professionals who leverage big data technology to conduct advanced data analytics can make valuable contributions to business strategy, operational performance, and customer satisfaction for organizations across the industry spectrum, from financial institutions to manufacturing, technology, healthcare, and professional services.
This article will explore the benefits of analytics in finance and how you can leverage financial analytics to provide valuable strategic and operational insights to businesses.
Two Data Analytics Functions Related to Financial Performance
You can think of the work of financial data analytics as being in two large buckets: financial decisions and market insights. While each of these related work areas has a different purpose and may use different data sources, they both rely on data to derive insights and make decisions.
Understanding Analytics for Financial Decisions
Financial analysts typically measure historical financial data and performance metrics, such as cash flow statements, income statements, balance sheets, and accounts receivables, against key performance indicators to devise budgets, make investment decisions, and manage financial risks.
Types of Data Used in Financial Analytics
This type of financial analysis relies heavily, but not exclusively, on internally sourced data. Analysts also use external data, including economic indicators, investment data and regulatory filings, in making recommendations for financial decisions.
In addition to the organization-level internal data sources listed above, the finance team may use transaction data, payroll records, and other data specific to individual business units in its financial analysis.
Benefits of Analytics for Financial Decisions
Financial analytics uses big data technology to offer benefits in budgeting and forecasting, risk assessment and management, and resource allocation.
Not only does big data technology increase the speed of financial forecasting, but it also offers greater insight and accuracy in financial modeling and valuation. Using structured and unstructured data from various sources, you can run different scenarios using predictive analytics.
In addition, using machine learning algorithms in financial planning allows organizations to practice more effective risk management by helping uncover patterns, detect trends and identify potential risks in vast amounts of disparate but relevant data.
Using Financial Data Analysis to Improve Business Performance
The critical insights you can obtain from financial analytics extend past those functions, including financial planning and budgeting, investment and capital allocation, and risk management, traditionally considered within the scope of chief financial officers.
Powerful data analytics tools allow you to use financial data as a lens for optimizing the economic contributions of individual business units through optimizing business processes.
Predictive modeling combined with performance measurement can help executives create strategies and make informed decisions about resource allocations and business investments in each of these business functions:
- Sales and marketing
- Customer relationship management (CRM)
- Operations and production
- Human resources and workforce management
- Supply chain and inventory management
Understanding Analytics for Market Insights
Data analytics for financial decision-making is more internally focused, and data analysis for market insights is more externally focused.
You can use data analytics for market insights to better understand the competitive landscape, market trends, and potential opportunities for business growth.
Customer analytics can help you deeply understand your target customer's preferences and needs, an understanding that you can combine with product profitability analytics and predictive sales analytics to perform market segmentation and fine-tune product offerings.
As with financial performance analytics, the more detailed analysis made possible by machine learning platforms can improve your organization's data-driven decision-making and improve business results against key performance indicators.
Types of Data Used for Market Insights Analysis
Because of the types of insights sought, data analysis for market insights relies more heavily on unstructured data. And, even more than financial decision-making analytics, market insight analytics relies on multiple data sources.
The raw data could include everything from social media content, customer surveys, and call-center records to web analytics, competitor financial information gleaned from trade publications and annual reports, economic and demographic information from government databases, and data about other external influences, including political and environmental forces.
Benefits of Analytics for Market Insights
You can think of the benefits of using data analytics in generating market insights as touching three general areas: the customer, the competitive landscape and the overall market.
Regression analysis and correlation analysis can help you identify patterns and understand the strength of relationships between market conditions and business results or between product features and sales performance, among other business questions.
That understanding can help you exploit the weaknesses in a competitor's business model and mitigate risks in your own operation. You can also strengthen relationships with your customers through better customer segmentation and product positioning, as well as by refining your product development.
Choosing an Analytics Program
Data analytics professionals guide organizations through the increasingly complex and dynamic business environment. If you want to join their ranks, graduate education can help you develop the big data skills and business knowledge you need to succeed.
As you investigate which graduate analytics program best fits you, consider the curriculum, faculty expertise, and industry partnerships.
The online Master of Science in Business Analytics (MSBA) from the Seattle University Albers School of Business and Economics offers a business-focused data analytics education and a robust network of alumni and business connections centered in Seattle and spanning the globe.
One of the elite few AACSB-accredited online MSBA programs, the Albers online MSBA equips you to craft a reflective approach to the legal and ethical issues surrounding business uses of data and to excel in stakeholder communications and management.
Each course includes a data translation challenge that the innovative business leaders and thoughtful intellectuals on the Albers faculty designed to prepare you to successfully and collaboratively solve real-world analytics problems.
Lead Better Business Outcomes as a Financial Analytics Professional
Unlock the comprehensive benefits available to organizations through data-driven decisions. Learn to apply data analytics across business units and industries to make better business decisions that, in turn, create better outcomes for your employer and your career.
The online MSBA from Seattle Albers will support your growing data analytics expertise with the communication and collaboration skills you need to thrive in the modern work environment and the ethical training to help you create sustainable outcomes. Speak with an admissions outreach advisor or download a program brochure to learn more about the AACSB-accredited Albers MSBA.