How to Improve Financial Efficiency with Data Analytics

Need for Data Analytics in Financial Management:

Data Analytics involves evaluating a company’s historical data to better understand the current and future financial health of a company. By tracking your financial performance alongside operational performance, you have access to your company’s health. For many companies, having data stored in disparate sources poses a challenge to doing this.

Almost three-quarters of financial professionals say they struggle to collect data from multiple sources and perform the analysis required to answer questions about financial performance

Computer Weekly


Data in the Cloud:

Legacy on-premises systems have several limitations that result in a lack of data visibility, inability to securely access critical third-party data, and incapacity to scale quickly. For this reason, organizations in many industries—not just finance—are moving to the cloud. The cloud’s unique capabilities enable financial services companies to grow their business without sacrificing security. The cloud is flexible and nearly infinitely scalable. And modern data analytics platforms can easily handle semi-structured data, essential for real-time analytics.


Financial services companies must be able to share data instantly and seamlessly so customers can perform necessary tasks with their accounts. Additionally, financial organizations must work together to facilitate services. For example, credit agencies must share information with banks for account and loan approval. 

Secure data sharing requires a centralized repository that eliminates the need for data extracts, which are inefficient and create security risks. Platforms such as Snowflake or Microsoft Azure allow financial institutions to easily determine who sees what data and ensure all business units and partners access a single and secure version of their data.

Use of Big Data for Efficient Financial Management:

The use of big data in finance combines tools that create, capture, manage, and process financial and other information with innovative approaches to convert the data into financial intelligence that guides business decisions. In addition to the size of the data pools that the tools work on, big data has two other characteristics that finance can benefit from:

  • Machine learning and other advanced analytics techniques are needed to account for variations within small data samples.
  • Natural language processing, speech recognition, and image processing allow the systems to work with unstructured data beyond the capabilities of analyses that are limited to data housed in spreadsheets, databases, and other structured sources.
Ongoing Research on the Use of Big Data for Efficient Financial Management:

To conclude, Data Analytics and Big Data can be a vital investment to better understand the incoming and outgoing finances of a company which can also be used to visualize the future trends and patterns thereby leading to early decision making and helps in avoiding potential risks.

[1] Maryville Online. 2022. Uses of Data Analytics in Accounting and Finance | Maryville Online. [ONLINE] Available at: [Accessed 28 September 2022].

[2] Phocas Software. 2022. How can financial data analytics enhance decision-making?. [ONLINE] Available at: [Accessed 28 September 2022].

[3] Shanawaz sheriff. 2022. How Data Analytics Help Improve The Accuracy Of Financial Reporting. [ONLINE] Available at: [Accessed 28 September 2022].

[4] Snowflake. 2022. Leveraging Data Analytics in Finance to Drive Growth | Snowflake. [ONLINE] Available at: [Accessed 28 September 2022].


Vivek nimmagadda

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