Data Analytics: Driving Better Business Decisions

To be successful, modern businesses need to leverage data analytics. Analytics can be conducted in a multitude of ways to help businesses shed light on how they are performing and what improvements they can make for the future.¹

Data analytics, by definition, is the science of analyzing raw data through extraction and categorization in order to identify patterns, relationships, connections, and trends, in order to make accurate conclusions.^2,3

Data analytics has come a long way from its origins in ancient Sumeria. Three thousand years ago, the first databases were kept on clay tablets.⁴ Data keeping—and analyzing—has since moved from clay to digital databases. Buoyed by the rise of electronic computers in the 1940s and 1950s, analysts began using technology to make high-speed calculations.⁵ In the 1980s, as data storage became affordable, large data sets were able to be collected. By the 2010s, the term big data was commonplace and is now paving an important way for the future.

In today’s data-intensive business world, data analytics has also become an important tool for every industry, including the financial industry. While computers perform much of the data analysis, there are a large number of data applications that make complex data analysis possible, and the process of data analytics has been highly automated, in the evolving frontier of big data people still play an important role.

Read on to learn how data analytics and big data are being leveraged in the modern financial and business sectors, what kind of lucrative career opportunities there are for those who have advanced data analytics and financial skills, and where these top skills can be gained.

Big Data: What It Is and Why It’s Important Today

Data analytics and its business applications have moved well beyond the limited canned reports and structured data capabilities of the 1960s.³ Today’s data reports deliver powerful insights gleaned from big data, “an architecture that allows the storage and retrieval of exponentially large amounts of unstructured data.”³

The process of big data involves analyzing vast amounts of “complex, variously formatted data generated at high speed, that cannot be handled, processed by traditional systems.”⁶ Due to the sheer volume and data being created by smart devices across the globe on a daily basis, it is estimated the total volume of data will reach 175 ZettaBytes by 2025. For perspective, a zettabyte is expressed as 1,000,000,000,000,000,000,000 or 10²¹ bytes. This growth trajectory is exponential. Based on current trends, the amount of global data being collected is expected to double every two years.⁴

Such large amounts of data require the latest data analytics programs. There are a number of data analytics programs available to perform cutting-edge analyses, manage large data sets, and find compelling insights:

• SAS
• Tableau
• Python
• Spark (Apache)
• Splunk
• SQL Server (Microsoft)
• Hadoop
• R Programming³

How are data analytics and big data used in business?

While the amount of data and programs we have to conduct analytics with is impressive, what does the availability and variety of data mean for businesses which are looking for real-world applications and solutions?

In short, it means companies that can leverage their data properly now have limitless possibilities to improve their businesses. It also means they can conduct real Business Intelligence (BI). BI is the process of gathering all data a business generates and boiling it down to easy-to-digest reports that management can understand and act on. With the right BI, companies can do everything from reduce costs to improve employee satisfaction.⁷

Businesses can use big data to go beyond canned reports and make emergent, transformative, and model-based reporting and analyses. Predictive forecasting is now also possible, based on our ability to vast amounts of data across multiple, disparate sources. Big data also allows companies to:

• Discover and pursue new business opportunities
• Drive innovation
• Increase revenue, customer lifetime value, conversion rates
• Reduce churn, bounce rates
• Improve business processes; improve operational efficiency and cut costs
• Gain an advantage in the industry
• Be proactive about risk management
• Streamline production
• Improve customer experience
• Optimize pricing, billing, claims, returns, compensation
• Be proactive about maintenance
• Define their customers
• Develop new services and products^,8,9

To be effective and to make real-world application possible, big data should be able to manage the five Vs, or major characteristics of data:

• Volume: The huge amount of data being collected
• Variety: The heterogeneous information sources that can be structured, semi-structured, and unstructured
• Veracity: The inconsistencies and uncertainty in data that can be difficult to control
• Velocity: The rapid speed at which data is being collected
• Value: Data needs to be converted into something valuable to extrapolate useful information¹⁰

Data Analytics in Finance

In finance, big data is used to track and monitor the financial markets and catch illegal practices. The finance industry also relies on analytics to:

• Combat money laundering
• Create accurate customer insights
• Mitigate fraud
• Uphold enterprise risk management³

All areas of business can benefit from well-run analytics, including healthcare, oil and gas, telecommunications, retail, manufacturing, and financial services.

What are the four types of business analytics?

There are four types of data analytics, descriptive, diagnostic, prescriptive, and predictive, that businesses across all types of industries use to increase overall performance at all levels of operations. These four types of analytics all work together and can be used together to help improve business performance.

Descriptive Analytics

Descriptive analytics can show “what happened” and is the foundation of data insights. It is the interpretation of historical data to better understand changes that have occurred in a business, according to Investopedia. This type of analytics can be used to gain an overall picture of how a business is performing and is often used alongside predictive and prescriptive analytics. Common insights include year-over-year comparisons, number of users, and revenue per subscriber.¹¹

Diagnostic Analytics

Companies can use diagnostic analytics to find out “why things happened.” Common diagnostic analytic techniques/insights include drill-down, data discovery, data mining, and correlations.¹² Companies use this data to identify patterns of behavior and make deep connections within the data they have collected. In order to be effective, diagnostic data must be detailed and accurate.

Predictive Analytics

Businesses use predictive analytics to “see the future” and predict “what is likely to happen.” Existing data, modeling techniques, and statistical modeling are leveraged to generate predictions about performance and future outcomes. This type of data is especially useful for marketing and insurance companies which need to make decisions based on what could be coming up. Common processes in predictive analytics include decision trees, neural networks, and regression models.¹³ Compared to descriptive and diagnostic data analytics, which is fairly common in most businesses, predictive analytics is more intensive and many companies are not leveraging this type of analytics yet.

Prescriptive Analytics

Prescriptive analytics, analytics driven by AI (Artificial Intelligence) systems, helps companies make decisions and determine “what they should do next.” This is the most in-demand type of analytics today, however, it is talent and resource expensive: Few companies have the skilled employees and resources to conduct it.

This type of analytics is on the leading edge of the analytical landscape and requires sufficient investment and commitment across the entire organization that wishes to perform it. Big data players like Apple, Netflix, and Facebook are currently conducting prescriptive analytics successfully. AI itself falls within the category of prescriptive analytics. It requires tremendous data and continuously updated data to help it learn, refine its decisions then communicate and act on these decisions in a business setting.¹

Data Analytics for Business Careers: Getting the Right Skills

Demand for employees who can conduct data analytics in the financial business sector is on the rise. The U.S. Bureau of Labor Statistics (BLS) estimates financial analyst roles will grow 6% through 2030, with those in related positions earning an average salary of $81,410.

For those with the right data analytics and finance skills, there are tremendous career opportunities including:

• Financial Analyst
• Financial Manager
• Chief Financial Officer (CFO)
• Private Wealth Management
• Chief Analytics Officer (CAO)¹⁵

When it comes to learning the latest data analytics skills the financial business sector is looking for, advanced master’s-level skills are recommended. SCU’s Leavey School of Business has a financial master’s curriculum that teaches the latest in business skills and strategies—including the data analytics skills for business, the cornerstone of today’s most forward-thinking organizations.

SCU’s IDIS 3802 (MSIS 2407), Data Analytics With Python course teaches the programming skills that are essential for today's data analytics including open-source Python, Jupyter Notebook, NumPy, Pandas, Seaborn, scikit-learn, and Colab. Students of the MSFA program also learn how about:

• Importing, cleaning, and transforming data
• Algorithmic thinking
• Data grouping, aggregation, reshaping, visualization
• Statistical modeling and time series
• Data exploration
• Communication of results

Additional courses in the program will help students become adept at navigating the heavily-quantitative realm at the intersection finance, business, management, and analytics occupy today.

Master Finance and Analytics for a Career Boost

With its robust academics and Silicon Valley focus, SCU’s online master’s of finance and analytics prepares you to thrive in data careers that are in demand in corporate finance settings and investment-focused organizations.

SCU’s Online MSFA is ranked the #12 Best Online MBA for Finance program according to U.S. News and World Report. SCU Leavey business school has been ranked #1 for Academic Experience and #1 for Career Outcomes by Poets&Quants.¹⁶

The Online MS in Finance and Analytics from Santa Clara University’s Leavey School of Business is a great way to sharpen your analytics skills and build a powerful Silicon Valley Network. Learn more about our world-class business faculty, and consider how their connections and accomplishments can be vital building blocks for your career.