4 Business Analytics Examples of Techniques Professionals Use

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Today’s businesses are finding they have more structured and unstructured data than they know what to do with. From customers’ buying patterns and how they make purchasing decisions, to mechanical data sets that may indicate when machines are likely to break down, big data is everywhere. Consequently, there is a clear and growing need for professionals who understand how to sort and analyze data sets in ways that can provide insights and drive decision-making.

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The Bureau of Labor Statistics (BLS) classifies people who work with big data or data sets that can be used to determine patterns or trends, as data scientists, computer programmers, or statisticians. It should be noted that the latest data from the BLS suggests overall employment in this field is projected to grow by 33 percent through 2026. This is much faster than the projected rate of growth for other occupations. This, in turn, suggests professionals who choose to pursue a master of business analytics degree may be well poised to advance in their careers.

Coursework within a master of business analytics degree program is designed to help professionals gain the skills to examine and interpret data sets and provide the analytical tools to identify business trends and solve organizational challenges. Those who enjoy problem-solving, statistical analysis, and using data in different ways may find a master of business analytics degree program aligns well with their professional goals.

Below are just 4 examples of business analytics in the corporate world today.

1. Predictive Analytics

Predictive analytics is the practice of sorting and analyzing data in ways that enable an organization to predict future events that may impact its business strategy. Professionals working in this field use artificial intelligence, data mining, machine learning, and statistical modeling to determine likely outcomes.

Companies that are integrating predictive analytics into their decision-making processes often find they can use historical data sets in ways that help detect and mitigate business risks. They’re are also able to use data to help transition from making educated guesses, to more accurately discern what a likely business outcome will be.

Today, predictive analytics helps retailers, for instance, decide which marketing campaigns will be most effective based on their consumers’ demographics. Amazon, for example, uses this type of model to suggest items to customers based on their past purchases.

Predictive analytics is also being used within the energy sector to determine when machines are likely to fail. This allows businesses to schedule preventive maintenance in advance and minimize unexpected downtime.

2. Machine Learning

Machine learning is a branch of artificial intelligence that teaches computers to learn from experience. It involves using data sets and algorithms, which are computer programs that perform calculations to identify patterns, and ultimately drive decisions, without the need for human intervention.
Software developers often suggest companies use machine learning when they have a complex problem with many variables, have large volumes of data, and lack a mathematical equation that will provide a solution. For example, machine learning is used by Netflix, Hulu, and Amazon Prime to recommend movies to viewers. Spotify and Apple Music use it in a similar capacity.
These algorithms often automatically improve their own performance over time. Today, machine learning is being used in a myriad of applications across a number of industries, including aerospace, automotive, energy production, and finance.

3. Artificial Intelligence (AI)

The use of artificial intelligence in business is becoming increasingly prevalent. From chatbots, which are computer programs designed to simulate online conversations with visitors to a website, to the use of virtual assistants such as Apple’s Siri and Amazon’s Alexa, incorporating the use of AI into business models can increase organizational efficiency while reducing the risk of human error.
According to a recent Forbes.com article, “Artificial intelligence can be used to solve problems across the board. AI can help businesses increase sales, detect fraud, improve customer experience, automate work processes and provide predictive analysis.” Thus AI software performs many of the functions of the business analyst role, at least on the surface. The big difference, however, is the human factor. As efficient as it is, AI technology is not expected to minimize the significance of the business analyst role, but rather to enhance its importance. Organizations looking to add robotics programs, for example, will turn to business analysts at the outset to identify key processes, pinpoint problems, and suggest solutions before the AI software application is even chosen. And once a judiciously selected AI application evaluated by a company’s financial analyst is up and running, performing simple repetitive tasks with precision, its presence will free up valuable time for business analysts to engage in critical thinking and deep subjective analysis no machine can perform and every company prizes.

4. Data Visualization

One of the most important business analytics examples being used today involves data visualization. While business analytics involves collecting, sorting and analyzing data, data visualization creates a framework that viewers can use to visualize data relationships and make decisions.
Pie charts, scatter plots, tree maps, and histograms are just a few of the techniques used by data scientists to visually communicate insights. A 2017 article published by the World Economic Forum found the world produces 2.5 quintillion bytes of data each day. For this reason, data visualization methodologies are key. They meet the continued need to segregate and contextualize data so it can be transformed into information and used by organizations to inform more intelligent decision-making.

Data Management

Businesses today have access to an unprecedented amount of data, which means finding ways to collect, keep, sort, and store it is more important than ever. This is where data management comes in.
Managing an organization’s digital data involves a wide range of policies, procedures and tasks, because if structured or unstructured data isn’t managed properly, analytics projects are unlikely to be successful. So today’s data scientists need to stay up to date on current best practices. This enables them to make changes to their database design, allocate database memory and storage, and optimize responses to queries.

A Career in Business Analytics Awaits

Professionals who are interested in improving their business analytics career opportunities may find a master’s degree can help them toward that goal.

The Online Master of Business Analytics curriculum at Ohio University is designed to offer students the statistical and analytical preparation that can help them stand out in a myriad of professional roles. Graduates often find they are well suited to apply for positions in data science and data engineering, including business analytics director, business intelligence director, business analytics manager, business intelligence manager, and more. Discover examples of business analytics in action and how the online Master of Business Analytics program at Ohio University can help propel your career path to the next level.

Recommended Reading

Ohio University Blog, “Online Master of Business Analytics Program Overview”
Ohio University Blog, “How to Empower Decision Making with Predictive Analytics”
Ohio University Blog, “Video: Student Support Team”


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Bureau of Labor Statistics, Mathematicians and Statisticians Job Outlook
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CIO, “What Is Predictive Analytics? Transforming Data Into Future Insights”
Datamation, “Top Predictive Analytics Examples, Analytics for Business Success”
Forbes, “Business Intelligence and Machine Learning: Data Matters, Not Just the User Experience”
Forbes. “Predictive Analytics Terms Business People Need to Know (No Hype Allowed)”
Forbes, “Preparing Your Business For the Artificial Intelligence Revolution”
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