How does BIG DATA impact the FINANCE industry?

Today, more and more business organizations adopt a data-centric approach to optimize their business processes, leveraging the huge advantages of Big Data. Along with other techniques such as data visualization and process automation, many companies expect to deploy Big data solutions in the near future, especially in the finance and investment industry.

However, the full potential of big data is still not yet fully exploited. Also, for many organizations, Big Data is still at the consideration stage. 

The increased importance of Data as a new asset for enterprise and the urgency of effectively implementing Big Data within an organization provides an opportunity for finance and investment firms - who are already well-versed at using traditional tools and technologies to pull data from a number of different systems and extract insights from it - to move toward data-driven optimization within their organizations.


In the new digital era, the competition is becoming much more intense, and most business organizations are looking for an advantage to stay ahead. Among such advantages, is the adoption of new technology, which enables the opportunity to manipulate a large amount of data to discover hidden insights, using advanced analytics tools.

Among the use cases in which companies are implementing Big Data is investments evaluation. Many financial firms face significant difficulties in objectively evaluating the performance of business entities that are worthwhile in their investments. Leveraging Big Data to retrieve and evaluate a large volume of data needed to make such investment evaluations, make sense, for any investment firm.

Incorporating Big Data into investment strategy could lead to a more robust process and deliver a competitive advantage.


Similar to any successful transformation project, Big Data implementation, “done right”, requires businesses to pay extra attention to a number of key items. For starters, many businesses have quite a huge volume of data, and being able to identify problems that can be solved by leveraging the data is crucial for effective implementation.

Another aspect is considering a pilot project for implementation. Starting small and reaping quick wins is usually a good way to start your Big Data journey. By selecting a relatively simple project to succeed, the benefits of Big Data can be demonstrated, beget additional investment and adoptions. Closely collaborating with other business teams and departments to define those key projects is critical here.


When it comes to the data sources for Big Data projects, it’s somewhat likely that companies use existing data sources to kick-start their Big Data projects. The increased importance of Data and Analytics brings an opportunity for finance and investment professionals to develop on this competency. In addition, the challenge now is to be able to expand the sources and to explore the potential of using not only internal data but also data available externally, for data-driven decision making.

As stated before, many organizations, including financial institutions, have already executed their Big Data projects, but only a few employs the use of new, external, and unstructured data sources. When it comes to big data, it’s important to understand the need for incorporating data from new and external sources.


It’s also an equally important factor when building a data-driven organizational culture is to define the appropriate tools and techniques for advanced analytics. The journey doesn’t stop at purchasing the latest analytics tools. To truly become data-driven, organizations need to have in place the appropriate training programs, so that their employees can use those analytics tools themselves, and understands and prepare the needed report’s results.

The critical aspect is that management and executives must commit to taking actions based on the insights derived from data. Otherwise, the project investment could be a waste of time and money. In the worst-case scenario, if employees can discover hidden insights for improvements but get ignored because the business isn’t ready for change, it can negatively impact the morale and employee’s motivation to continue further with the technology.

Again, before starting your Big Data project, it’s essential to create effective data governance and data quality assurance. No matter the data sources, ensuring data quality and integrity is key for implementation. Since it’s very easy to be overwhelmed by the vast amount of data available, it’s important to build governance around the data needed for analysis.

When adopting analytics, having buy-ins from the top management is only half of the picture, participation from functional departments with key initiatives is also required for successful implementation. Initiatives that came from departments are usually narrower in scope and common as business use cases among employees.

Nevertheless, successful big data initiatives can significantly transform your organization and promote data-driven decision making, enable a culture of answering questions and gathering evidence, with top-down support from the management team.


Today’s business landscape differs immensely from the past in the sense that a huge volume of data is available in real-time, and can be accessed anywhere, anytime thanks to cloud technology. The availability and immediacy of this data present both opportunities and threats to the finance and investing profession. To stay competitive, finance and investment professionals need to take advantage of opportunities to create added value around Big Data.

This involves the use of data to derive hidden insights into the business trends and an organization’s operations, as well as addressing the need for data governance, and using Big Data to enhance risk management.


Data governance

Data governance is the basis for Big data and could be difficult to build, especially for large and complex structures corporates. 

Data governance involves customers' privacy, which is a big concern for most organizations in recent years, especially when new legislation such as GDPR and HIPAA come into effect. This means appropriate governance needs to be built by considering various aspects such as regulatory, and customer’s consent, as well as security and acceptable use of data.

The key to success with Big Data is building robust governance over data quality. Finance professionals can help foster a culture that keeps data sets more secure and appropriate, increasing data quality and value. By working together with IT operations, finance professionals can help to ensure that the data for analysis is consistent and from reliable sources

Gaining insights

Finance professionals can help provide a comprehensive view of the business’s current snapshot and enable management to understand the process take corrective actions based on insights reports.

Furthermore, finance professionals can help provide other functional teams with relevant information and reports, support planning and decision making. Specifically, they can offer financial and investment analysis, to help other business functions learn about the financial implications of their activities or plans - thus, enable business functions to improve their decision-making with better, more accurate financial data analysis.

Risk management

Finance professionals can also use Big Data to help their organizations anticipate and eliminate/minimize risks - protect their financial investments. For example, data from various social media channels and external websites can effectively show consumers’ sentiments toward a certain company’s products or the business entities themselves - and by incorporating diverse sets of data in their analysis, finance professionals can help organizations to better define the potential risks faced by the organizations and the corrective treatment plan. 


Companies looking to start their Big Data projects should pay attention to a number of key factors, including:

  • Starting with a simple pilot project to demonstrate success and Big Data benefits.
  • Expanding the sources of data used to include not only internal data but also external sources.
  • Provide data-based insights to those who need it, in a timely manner.
  • Encourage buy-ins for Big Data and Analytics initiatives from both top management and departmental levels.
  • Provide adequate knowledge transfer and training program to enable self-service analytics
  • Create data governance and quality to ensure data is suitable for analytics and produce expected results.

Need help getting started with your big data projects? Contact our specialists today.