Big data analytics helps companies across industries unlock value from the vast amounts of data created and collected every day. Of course, this includes complex expense data such as transaction details, receipts, employee expense reports, and vendor invoices.
The market size for financial big data analytics is expected to grow from $6.97 million in 2023 to $19.72 million in 2028, representing a CAGR of 23.11%. However, many organizations lack an understanding of big data analytics or have difficulty applying analytics to expense management.
How can companies use big data to develop a structured approach to expense analysis? Read on to learn more about the key role of big data analytics in expense management.
The evolution of big data analytics
The term "big data" was coined in the 1980s, but its impact has only recently become apparent. As the volume of data increases, companies need solutions such as data warehousing to meet most analytics and decision-making needs.
As new datasets emerge regularly and create new use cases, such as real-time big data analytics, existing approaches evolve rapidly to meet these changing needs. Over the past decade, frameworks and tools for large-scale data processing using real-time streaming have become increasingly prominent.
Cloud computing, artificial intelligence (AI), and machine learning further improve the ability to extract insights from big data with efficiency, speed, and high performance, and constantly improve output.
Big data analysis and expense management
By delving into your spending patterns, big data analytics goes beyond surface information about your spending. The analytics engine helps companies identify opportunities to cut costs, for example, by detecting supply chain inefficiencies that lead to excessive costs.
In an environment full of financial transactions, manually detecting irregularities or suspected fraud is a challenge. Spending data analytics tools are good at detecting anomalies that could indicate fraudulent activity. For example, the system could identify strange fees for further examination by continuously monitoring transactions and cross-referencing them with established trends. This not only protects your resources but also improves the overall integrity of your expense management system.