CFOs Big Data Opportunities in Firms

Introduction
The growth of big data has had significant transformative effects on several industries including technology, agriculture, health, education, and finance. Over the past decade, the number of humans using smartphones has increased tremendously and this has created a big pool of user data as smartphone users stamp their digital footprint all over the web. The electronic data collected as a result of these activities yield Big Data that provide valuable insights to decision-makers looking for data points to inform their decision processes. The growth of Big Data has necessitated the development of computer algorithms and tools that can analyze the data at scale. Good analysis and interpretation of the data allow organizations to ‘co-create’ solutions alongside their target users and create new value that is highly tailored to the needs of the market (Turner, Schroeck & Shockley, 2013).
The financial services sector is one of the industries that have adopted Big Data at scale to inform decision making. In stock trading, algorithmic trading and big data help investors utilize historical data to maximize the returns on their portfolios. As the adoption of Big Data in the financial services sector continues, the landscape of the industry is likely to see significant changes in the coming years. Nonetheless, it is notable that organizations are still facing some challenges in capturing, storage, analysis, and interpretation of huge volumes of data (Chen, Chiang & Storey, 2012).
Big Data Opportunities
Traditional data management tools and models have posed several challenges to the finance industry in the past in terms of security and inefficiencies. One of the major security issues is fraudulent activity by insiders and outsiders. With the world increasingly becoming more connected, outsiders acting in bad faith such as hackers have more opportunities to gain access to company data. The traditional models do not have the systems required to secure digital assets as hackers become more advanced by the day. Therefore, this security problem should be overcome as soon as possible because of the tremendously big risk it poses to the financial services sector. The growth of Big Data and associated IT infrastructural development can cure this problem. Another limitation of traditional data tools and models is the ability to accurately analyze customer sentiment. Customer tastes are rapidly changing as they get more choices in the market. Industries must be able to fulfill these ever-changing needs and address them satisfactorily (Nath, 2019).
For companies to be able to know what their customers want in a timely manner, they need to collect and analyze a lot of customer data. Traditional data management tools and models do not provide the tools and techniques necessary to collect or analyze lots of data quickly. Also, the traditional tools and models do not have key functionalities such as the ability to segment customers by psychographic and demographic profiles. This inability for advanced segmentation limits businesses in their ability to accurately target their market segments when doing advertising. There is a growing need for a more advanced system that can help overcome these challenges especially in the financial services sector (Zhou, Fu & Yang, 2016).
The growth of technology and ever-increasing data proliferation has transformed how industries are run. In the recent few years, about 90% of the world’s data has been as a result of the world creating some 2.5 quintillion bytes of data every day. This data phenomenon is known as Big Data and it presents several opportunities in various areas including data collection, data processing, and data analysis. CFOs are increasingly spending their time on data analytics and analysis and the functions of their departments are constantly expanding and shifting. The average finance professional has to expand their skills to include data analysis and analytics to meet the demands of the new financial department. Therefore, CFOs are prioritizing the retraining of finance professionals working under them (Zhou, Fu & Yang, 2016).
It is vital that organizations put their focus on business areas that Big Data can provide disproportionate value. For most organizations in financial services, this implies that they have to analyze their customers deeply for them to have a deeper understanding of their customers so that they are able to anticipate their financial needs. This data can be used to generate leads, improve existing products, and explore new technologies and business channels (Zhou, Fu & Yang, 2016).
One of the most important facets of Big Data is predictive analytics. Predictive analytics are useful in foreseeing fraudulent activity and therefore stopping them before they cause harm to the organization. Further, predictive analytics are used to predict potential breakdowns in a financial system. As many industries are doing, the financial services sector can also use Big Data in marketing. The financial services sector must move to build good relationships with their clients and build platforms where they can easily interact with their customers. The value of such a move is informed, relevant, and timely interaction with customers which provides valuable real-time data for the organization (Chen, Chiang & Storey, 2012).
As noted earlier, Big Data is very powerful when it comes to market segmentation. Market segmentation is a vital concept in marketing that ensures that customers tailor their marketing messages to their audiences. When organizations are able to leverage Big Data to segment their marketing messages and utilize the scale of reach that the internet provides today, the potential impact on the bottom line is huge. As per an IBM survey, not less than 25% of financial organizations are utilizing Big Data to build competitive advantages. Big Data can help finance departments and organizations identify customer segments in real-time, detect and prevent fraudulent activity, improve promotional activities, accelerate growth, and track key performance metrics (Chen, Chiang & Storey, 2012).
Many CFOs’ utilizations of Big Data have had positive effects on productivity and business outcomes. Various facets of Big Data such as linkage of records, deep learning, and graph analytics have been proven valuable in fighting crime, reducing fraud, reducing abuse of gaps in systems, fighting identity fraud and theft, and several other important areas. It is also notable that many systems developed today are open source and provide decentralized single platforms that are easy to install, tweak, and manage. Most of these systems have in-built Machine Learning libraries that make it easy for organizations to do data analysis (Jin et al, 2015).
Big Data is creating opportunities for organizations to enter into industries that are related to their core offerings. For instance, big finance firms and financial technology companies are entering into the microfinance sector. The competition created by such new entrants is good for industries and customers as they lead to the creation of better products and better pricing. CFOs use big data to analyze whether such moves are financially feasible. Based on the huge volumes of data companies collect today, such feasibility evaluations tend to be largely accurate (Joshi, 2018).
Challenges
Big Data makes it a necessity to be able to analyze several different data types. Finance departments of organizations and the financial services sector lag behind in this area relative to other industries or business functions. In less than 20% of current Big Data efforts, the financial services sector plays surveyed utilized capabilities designed for analyzing texts in their natural state, phone call conversations being an example. The analytics are able to interpret and comprehend language nuances such as intentions, slang, and sentiment and are usually utilized to understand preferences and behavior and so improve customer satisfaction (Yin & Kaynak, 2015).
While the financial services sector has embraced big data, there are a lot of challenges being faced in the field. Notably, pooling unstructured data raises the question of privacy. People’s personal private can be gathered based on their health records, communications, and internet usage. The other big challenge for finance departments and CFOs is the accuracy of data analysis. Because of the great volumes of information and data that come with Big Data, greater sophistication is required to achieve statistical accuracy when doing data analysis. Critics have pointed out the grey area between noise and signal that can blur correlations. In the same vein, CFOs and financial model professionals use historical data to gauge future investment opportunities. Such predictive models are limited as they do not take into account other variables that may influence future returns on investment (Yin & Kaynak, 2015).
On the question of privacy, organizations may use people’s personal information and data without their permission. Sensitive information such as health records may be leaked to the public if not handled properly by an organization. In certain instances, companies and private citizens may be denied credit by other financial organizations if data on late-payments and defaults are made public without providing the context for such defaults. Further, since Big Data is useful in creating market segments, discrimination of various social groups may be an issue as consumers can automatically be grouped into social groups and be disadvantaged when accessing various products and services. This kind of profiling can lead to lawsuits in certain instances (Joshi, 2018).
Recommendations
To balance short-term and long-term goals of Big Data problems, organizations must be pragmatic. CFOs and finance departments can cost-effectively start building momentum by leverage already-available internal data and leverage the skills and tools available within the organization to create new value. With time, they can leverage external capabilities to address more complex data concerns (Turner, Schroeck & Shockley, 2013).
Most organizations have to make huge investments to bring their capabilities up to par with the current reality of Big Data. However, waiting for resources for such big investments can slow down progress and extend the timeframes that businesses fail to take advantage of Big Data. CFOs should sell the need to move fast and take advantage of already existing infrastructure and data to get projects, however little, underway. Big Data projects can be expanded with time to include greater data varieties and even bigger volumes (Turner, Schroeck & Shockley, 2013).
CFOs and other finance department professionals should train their focus towards getting the specialized skills needed within their industries and organizations. This should include the skills necessary to do a comprehensive analysis of unstructured financial data and present the data in a way other decision-makers within the organization can easily understand. Achieving these objectives will require the finance department to work closely with other departments, especially the IT department (Turner, Schroeck & Shockley, 2013).
Conclusion
Big Data has had several transformative effects on several industries, especially the financial services sector and the finance departments of organizations. CFOs have not been left out in appreciating the role Big Data plays and will continue to play in their operational and strategic roles in organizations. CFOs and their organizations are indeed becoming inundated with data but this is for the good of the organizations they work for. Due to the novelty of the field, Big Data is being embraced rapidly and this will lead to the improvement of processes that usually comes with scale. Increased adoption of Big Data by the financial services sector and various organizations’ finance departments will make CFOs work easier due to increased automation, the accuracy of statistical calculations, ability to collaborate, and sophistication in Big Data tools (Turner, Schroeck & Shockley, 2013).
CFOs understand that to compete in today’s data-driven market, their organizations must leverage all of their data assets to better understand their customers, markets, products, competitors, supply chains, employees, and other players in the industry. Big Data will prove useful to all business leaders, not just CFOs (Turner, Schroeck & Shockley, 2013).
References
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4).
Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and challenges of big data research. Big Data Research, 2(2), 59-64.
Joshi, N. (2018). How Big Data Can Transform the Finance Industry. Retrieved October 7, 2019, from https://www.bbntimes.com/en/technology/big-data-is-transforming-the-finance-industry.
Nath, T. I. (2019). How Big Data Has Changed Finance. Retrieved October 7, 2019, from https://www.investopedia.com/articles/active-trading/040915/how-big-data-has-changed-finance.asp.
Turner, D., Schroeck, M., & Shockley, R. (2013). Analytics: The real-world use of big data in financial services. IBM Global Business Services, 27.
Yin, S., & Kaynak, O. (2015). Big data for modern industry: challenges and trends [point of view]. Proceedings of the IEEE, 103(2), 143-146.
Zhou, K., Fu, C., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews, 56, 215-225.