Business Analytics: An Inside Look at the Skills Needed to Advance in the Era of Big Data

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According to Forbes, “Big data applications and analytics [are] projected to grow from $5.3B in 2018 to $19.4B in 2026.” More and more businesses are emphasizing the importance of big data, taking information about their organization’s activities and processes that may have been previously ignored or overlooked and using it to help develop strategies for growth and opportunity.

This growth in the emphasis on big data has created an enhanced need for professionals with an expansive skill set in analytics. From data analysts to data scientists, individuals who are able to not only analyze data but present their findings in a way that others can comprehend will be invaluable additions to companies in the coming years.

One doesn’t need to be an expert data analyst to take advantage of the heightened emphasis on big data. Having a deep knowledge base and thorough business analytics skills will help prepare professionals for success in this expanding field.

What Is Business Analytics?

According to the technology research firm Gartner, “Business analytics is comprised of solutions used to build analysis models and simulations to create scenarios, understand realities, and predict future states. … These analytics solutions often come with prebuilt industry content that is targeted at an industry business process.” For example, data analysts in the digital media or digital publishing industries may use an analytics solution known as Chartbeat, which displays real-time information about how many individuals are visiting a webpage at a given time. Solutions like these are particularly beneficial to digital media and publishing professionals because they deliver data that can be analyzed, such as determining which content is performing well and what steps should be taken based on that knowledge.

Understanding the types of Business Analytics

In the health care field, insurance professionals might use analytics models to determine how pricing changes to health insurance plans could impact profits, examining data such as current and past revenue, changes to insurance rates over the years and anticipated market growths or reductions. They might determine that a change in price would negatively impact revenue, and forgo altering rates, or that a price adjustment could raise profit levels but only if initiated on a certain date, and delay the change.

Ultimately, these professionals help to guide a company into the future while improving and enhancing its overall processes and capabilities.

Business Analyst Skills and Knowledge

Succeeding as a business analyst requires a deep knowledge of data analytics.
This begins with understanding the four main types of analytics: descriptive, diagnostic, predictive and prescriptive. Descriptive analytics allows professionals to look at data to evaluate what has happened. This could include determining how much money was generated or lost by a company in a given year, or how many prospective customers a particular marketing campaign reached in a given week.

Diagnostic analytics also focuses on past events, but uses data to determine why things happened. For example, while descriptive analytics determines revenue, diagnostic analytics can pinpoint specific reasons behind a growth or a loss, such as prices being lowered or changes to supply chain procedures.

Predictive analytics uses data to predict potential future events. An analyst interpreting revenue figures can take into account how different factors — such as new government regulations or workforce adjustments — will likely impact overall production and profitability.

Prescriptive analytics similarly uses data to model potential future outcomes, but goes further by helping to provide suggestions and potential courses of action. A professional using prescriptive analytics could determine that new government regulations would negatively impact profitability, and suggest potential courses of action to reduce the impact for the business, or that while layoffs would boost short-term profits, reduced productivity would hamper business operations over the long term.

Alongside a strong background in analytics, specific business analytics skills are necessary to excel in the field. Business analysts handle data that relates to profit, revenue, growth and other finance-related information, requiring financial skills. Additionally, good communication skills help analysts present and explain their findings. While business analytics are becoming increasingly important and valued across multiple industries, not everyone in a given company or organization understands analytical processes and tools, making it imperative that analysts can clearly communicate their ideas and insights in a manner that others can understand and appreciate.

Other areas that business analysts should have knowledge of, according to CIO, include requirements engineering (defining specific requirements when designing and implementing an analytics algorithm); cost/benefit analysis (weighing potential financial gains against resources used); process modeling (breaking down necessary actions within an analytics process); as well as a strong understanding of databases, networks and different analytics tools and platforms (including which are best suited to certain situations).

Data Mining for Business Analytics

The technology organization SAS states, “Data mining is the process of finding anomalies, patterns and correlations within large datasets to predict outcomes. … Its foundation comprises three intertwined scientific disciplines: statistics (the numeric study of data relationships), artificial intelligence (human-like intelligence displayed by software and/or machines) and machine learning (algorithms that can learn from data to make predictions).”

Consider a commercial bank where more than 1,000 customers have mortgages. Every one of those mortgages involves a large dataset, including monthly payments, interest rates and loan durations, as well as information on the mortgage holders, such as age, income, neighborhood, assets and debt. All of this data is valuable, but it can be difficult to pull meaningful insights from such a large set.

This is where professionals with a business analytics background employ data mining techniques to extract meaningful information. That can include a bank analyst who uses algorithms and artificial intelligence (AI) tools to interpret and analyze the data, potentially discovering a downward trend in the income level of new mortgage holders, or that higher interest rates are correlated with greater or weaker financial performance in some areas.

Artificial intelligence and machine learning are immensely helpful in data mining. Artificial intelligence refers to human-designed computer algorithms, such as a computer program created to track trends and provide insights. Machine learning is a subset of artificial intelligence that refers to AI processes that don’t require human guidance and that are capable of organizing and modeling data in useful ways without being given set guidelines or parameters by humans.

Ultimately, artificial intelligence and business analytics can benefit any industry or organization that produces large quantities of data. A national restaurant chain could use AI or machine learning tools to improve its marketing strategies or find insights to make informed adjustments on prices or menus. A global automotive retailer could use AI and machine learning tools to parse through consumer feedback and make necessary adjustments to its vehicles’ features. If large datasets are available, these tools can be beneficial.

Business Intelligence and Analytics: Similarities and Differences

Business intelligence and business analytics are both fields that involve data gathering and interpretation. While they share similarities, it is important to understand what distinguishes one from the other.

Business intelligence primarily focuses on events that have happened in the past or are happening in the present. A year-to-date revenue report is an example of business intelligence, as is information about a corporation’s current stock price or how many vehicles a major shipping company currently has on the ground in comparison to a competitor.

In contrast, business analytics “[mine] data to predict where your business is heading and prescribe actions to maximize beneficial outcomes,” according to CIO. While information about a corporation’s stock price is helpful in the here and now, business analytics uses that data to predict future outcomes, such as how new government regulations or shifting market trends may impact the stock price. Some organizations use the term business intelligence interchangeably with business analytics. More broadly, though, analytics takes the information gathered for business intelligence a step further. Both are valuable to companies as they each can help to reveal insights.

Business Analytics Tools and Resources

Several data-gathering, organizational and analysis tools are available to assist professionals in their business analytics processes and procedures. Microsoft Excel, for example, is a spreadsheet program that concisely organizes key business data, such as a history of transactions conducted over a given time period. It is also capable of more advanced functions, such as determining profit in a particular time period and comparing it to other periods.

Tools such as Adobe Analytics and Google Analytics provide a robust understanding of a website’s performance. For example, Google Analytics can help determine how many visitors a website receives, as well as visit frequency and duration and visitor demographics.

Cloud analytics platforms — online tools that help to manage and interpret large datasets — can also benefit organizations with business analytics needs. SelectHub, a technology information and review site, provides a list of recommended cloud analytics platforms, including Tableau and Sisense, that allow analysts to visually map data insights.

Benefits of AI and Machine Learning

All businesses must determine what tools they need to produce useful business analytics. For example, a small bakery may not need business analytics software to know that fewer customers come in on Tuesdays and preparing less food that day will save money by preventing waste. A larger bakery with a national presence, though, might not know the specific purchasing trends at each of its locations. Analytics tools can map out a deeper understanding of trends at locations on a national scale — such as how marketing or pricing impacts sales — and can help the company develop broader solutions to its problems. By using analytics, the national bakery can reap rewards such as higher profits, improved perception among the public and better employee morale.

Business Analytics Jobs and Salaries

Business analysts are professionals who use analytical skills and technological tools to improve businesses in a number of ways. They may use analytics to reduce inefficiencies in a production process, improve a product or reveal pain points within a service. According to the compensation site PayScale, the median salary for a general business analyst was around $60,000 as of January 2020.

Professionals in the business analytics field can pursue a range of positions with varying salaries. The U.S. Bureau of Labor Statistics gathers data concerning salaries and position descriptions for a variety of business analytics jobs:

  • Management Analyst: These individuals work to improve workplace operations and efficiency. Their median 2018 pay was $83,610 per year.
  • Computer Systems Analyst: These professionals are tasked with analyzing an organization’s computer systems and procedures while developing solutions for improvement. Their median 2018 pay was $88,740 per year.
  • Financial Analyst: These individuals use analytics to interpret financial data, finding trends, developing insights and proposing new courses of action. Their median 2018 salary was $85,660 per year.
  • Market Research Analyst: These professionals are tasked with mapping and projecting how different factors may impact sales and marketing activities. Their median 2018 pay was $63,120 per year.
  • Operations Research Analyst: These professionals use advanced mathematical data methods to solve problems and advise decision-makers. Their median 2018 salary was $83,390 per year.

Learn More About Business Analytics

Business analytics is a growing, competitive field. Top-tier companies will turn to professionals who possess the skills to succeed in business analytics roles and hold the credentials that certify they are ready to lead.

Rider University’s online Master of Business Administration with a concentration in Business Analytics provides aspiring and current analytics professionals with a robust understanding of the analytics landscape and the necessary skills to succeed. With advanced courses in subjects such as business intelligence, tech-data mining, applied data management and managerial business analytics, graduates leave prepared to analyze and interpret data in a variety of functions that benefit organizations.