Financial analytics is the process of collecting, analyzing, and interpreting financial data to gain insights and make informed decisions regarding an organization’s financial performance and strategy. Advanced financial analytics uses more sophisticated techniques, algorithms, and tools to extract insights, recognize patterns and make predictions from large data sets. Using advanced financial analytics, organizations can gain deeper and more actionable insights that help them uncover potential risks and predict and improve performance.

Barriers to Adopting Advanced Financial Analytics

Unfortunately, there are many barriers to advanced financial analytics that a business may encounter. Here are some of the common ones alongside a brief recommendation for how to overcome them.

Over-Reliance on Spreadsheets

Practically all businesses use spreadsheets to handle some aspects of their data analytics. However, spreadsheets don’t offer the integration, scale, real-time data, and advanced analytics required to realize the full potential of your financial data. For these capabilities, companies will need to supplement spreadsheets with a data platform and the right financial tools and techniques to meet specific financial analysis objectives.

Data Silos

Data availability is key to advanced financial analytics. It requires access to comprehensive data, both current and historical. This may include financial records in financial management software, sales data in CRM systems, external market data and economic indicators, news feeds, social media data, and more. To be effective, organizations will need to break down these silos, bringing together data to develop mission-critical insights.

Data Quality Issues

Business users who rely on advanced analytics to make important decisions need to know that they can trust the integrity of its results. While data quality challenges are prevalent across all types of business data, financial data is particularly prone to issues. This is due to manual data entry, complexities when dealing with multiple currencies, customers with multiple accounts, intricate financial calculations, and lack of standard data formats, measurements, and naming conventions. These are reasons why data quality tools to ensure that data is accurate, complete, consistent, reliable, and up to date are so important.

The Complexity of Advanced Analytics

Advanced financial analytics uses techniques such as machine learning and statistical modeling to make accurate forecasts and uncover patterns buried in large volumes of data. Deploying these techniques correctly requires expertise in data modeling and proficiency in programming languages such as Python and R. Even business users will need to shore up their skills in math and how to accurately interpret results.

Strict Regulatory and Compliance Requirements

Financial data is heavily regulated, and violations are expensive. Understanding and enforcing regulations can be challenging, compounded by the need to understand unique requirements across geographies and industries. Businesses will need to establish and enforce policies and processes for collecting, storing, using, and sharing information for advanced analytics.

Communication

Communication can be challenging since various stakeholders throughout a company don’t typically have any background in advanced analytics. Conveying insights and implications of any analysis in a clear and understandable manner is a crucial skill for financial and business analysts to hone.

Sourcing From Multiple Vendors

Data integration, data quality, and other management workloads add more costs and complexity when sourced from multiple vendors. This can limit further investment in financial analytics if its costs exceed the business value it delivers. When possible, look for one data platform that offers many capabilities.

Overcoming Challenges

Overcoming these challenges isn’t always easy, but it’s well worth the effort since advanced analytics can help your business derive valuable insights and make more informed decisions.

Financial analytics success starts with the right platform. Actian has 50 years of experience helping customers manage some of their most critical data. The Actian Data Platform simplifies how you connect, manage, and analyze your financial data. Its unified data management gives you the ability to integrate, transform, orchestrate, and store your data in a single, easy-to-use platform.

Related resources you may find useful:

Data Silos Suck. Here’s How to Break Them Down

5 Common Factors that Reduce Data Quality—and How to Fix Them

How to Maximize Business Value with Real-Time Analytics

Real-Time Data Analytics During Uncertain Times

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