The role of data analytics in the finance industry is profound and growing with each passing year. With data science, machine learning, and modeling, data analytics professionals can use these tools for organizations to minimize risk and create more efficient and profitable decisions based on fact rather than fiction.
According to highlights of a study from Dresner Advisory Services in Forbes, data analytics adoption in enterprises raised from 17% in 2015 to 59% in 2018, reaching a compound annual growth rate of 36%.“Telecommunications, advertising, and insurance are the three industries that say big data analytics is the most critical to their business intelligence initiatives. Health care and retail & wholesale industries rank big data analytics as essential to their ongoing business intelligence initiatives as well. 80% of all enterprises say big data is, at a minimum, important to their business intelligence initiatives.”
Yet there’s a shortage of trained professionals to analyze data to the finance industry. New roles created by the need for big data include job titles like artificial intelligence platform support engineer — jobs that didn’t exist a few years ago but are now needed in an ever-expanding industry of data science.
Why Data Analytics
Technology has advanced at such a rapid rate that former supercomputers are now everyday desktop tools. The processing power needed to gather, analyze and extract data is available to almost anyone. Managers in the finance industry and other fields realize the benefits of data analytics but are sometimes unsure how to utilize the processed data.
After all, what good is a lot of data if no one can read or disseminate it into palatable reports for making informed decisions? Presenting the data in simplified reports and visualizations, discovering patterns and trends, and then predicting future events can be challenging for organizations new to this kind of technology
The finance sector has taken the lead in the development of data analytics in many ways. The need to eliminate risk and predict market trends has always been the goal of financial professionals on behalf of their companies or clients. Data analytics can even detect fraud or mismanagement of resources to reduce the likelihood of audits, operational shutdowns, or criminal activity.
Data Analytics Careers
As noted before, data analytic professionals serve many industries, some within the finance industry and some to provide oversight and management of finances for other industries. Data analysts may work for public companies, colleges and universities, nonprofits, health care organizations and government agencies — all of which rely on analytics to provide budgeting, financial reporting, spending analysis and investment opportunities to leadership and key decision-makers.
According to Ohio University, “Data and business analysts who have deep technical knowledge of different programming languages—such as SQL, Oracle and Python applications, as well as analytics tools—are armed with skills that can be applied across the business landscape. Most data analysts possess a high level of mathematical ability, analytical and problem-solving skills, and the capacity to analyze and interpret complex data. The combination of technical knowledge and relevant soft skills allows a data analyst to process, interpret, and analyze data and apply problem-solving skills to support decision-making.”
The U.S. Bureau of Labor Statistics predicts that operations research analysts, those that use advanced mathematical and analytic methods to develop complex answers, are expected to grow 26% from 2018 to 2028. That’s much faster than the average for all occupations. Of the 109,700 operations research analysts employed in 2018, 30% worked in the finance and insurance sector.
Data Analytics for the Internet Age
Data analytics can also be used for marketers and salespeople to show website traffic and product purchases in digital form. Using tools like Google Analytics, these professionals can gather data from past, present and even future customer interactions to provide key measurements and indicators to management.
For example, companies like Quandl are literally “scraping” the internet for information, looking through websites for valuable data to give financial firms an edge against the competition. This technology can also be used to mine social media for trends in customer behaviors and reactions. Although this may seem advantageous for companies, there are restrictions on copyright and user privacy that must be considered. Those organizations scraping the web too deeply can be caught and punished by governmental agencies like the Federal Trade Commission.
The Future Role of Data Analytics in the Finance Industry
The age of adding machines and hand-written ledgers has given way to tools named Python, SQL and Tableau. Imputing, tracking, analyzing and presenting vast amounts of data are now commonplace in the finance industry and elsewhere. Data science will continue to grow as the benefits of this technology are seen by more industry sectors and companies. Those with a passion for numbers and data will thrive using these applications to create opportunities and financial rewards for organizations and the professionals that work with them.
