Big Data in Finance: Unlocking New Dimensions by Merging Financial and Non-Financial Data

November 16, 2023

Managing big data in finance is critical to unlocking valuable insights that drive companies towards success. Combining financial and non-financial data provides CFOs with a more holistic view of the business, enhancing decision-making and strategic planning to adapt to the fast-changing business climate.

Financial data analysis: unifying financial and non-financial data

Whereas finance departments are aware of the importance of financial data sets involving balance sheets, income and cash flow statements, the impact of non-financial data on business planning and performance is still underestimated. The latter are obtained from sources such as customer and marketing reports, supplier information, operational data, etc. that are not related to a company’s finances. Therefore, incorporating them into financial data analysis provides greater insight into the company than what financial data can deliver on their own.

The limitations of siloed financial data sets

Silos hinder cross-functional collaboration, making it impossible to gain holistic insights and make informed decisions. This, in turn, leads to lower-quality customer service and missed business opportunities. Here are some common problems:

  • Incomplete financial data sets.
  • Data inconsistencies and inaccuracy mean poor data quality.
  • Security and privacy issues.
  • Lack of trust in data limits their use and business benefits.

Therefore, data integration with other systems is the most straightforward way to break silos down, allowing the streamlining of data collection and improving financial data analysis.

The power of big data in finance and data fusion

Simply put, big data is a collection of large data sets that can be in different formats and are so voluminous that they cannot be managed using traditional data processing software. To tackle big data challenges, new technologies and tools have emerged. Additionally, machine learning and artificial intelligence, including generative AI, have become invaluable in making sense of big data. These technologies enable automated analysis, prediction and decision-making based on patterns and insights extracted from large datasets.

Financial data aggregation involves collecting consumers’ financial information from various sources, relying on different technologies. The use of CPM software enables the consolidation of data from different departments into a centralized system. This not only enhances consistency, but also reduces the risk of errors, ensures data quality and, most importantly, transforms it into valuable business insights.

Advanced financial data analytics: actionable and predictive insights

Financial data analytics leverages historical data to deliver predictive insights for future planning. Conversely, actionable insights in finance arise from the analysis of big data, which includes vast amounts of both structured and unstructured data. Companies utilize these insights to pinpoint areas for performance improvement. Some of their benefits are:

  • Improved decision-making and efficiency in operations.
  • Big data tools automate data collection and analysis processes so that improvements can be made to streamline operations.
  • Valuable insights into customer behavior and preferences to make tailored offerings.

If you are seeking an agile planning approach, Talentia CPM software provides digital solutions for financial and non-financial data integration, making big data in finance a competitive asset.