As a data lover, you’re at a professional crossroads. Do you stay in your current—and familiar—role, or do you shift and move in an entirely new direction? More specifically, how do you choose between becoming a business analyst vs. data analyst?

Both roles would allow you to capitalize on your love of “all things data,” and both would appeal to your affinity for problem solving. Both positions would also pair well with an in-depth knowledge of data science. However, the roles of business analyst vs. data analyst require different skillsets and focuses, making it necessary to choose your path carefully.

What Is a Business Analyst?

A business analyst identifies technology solutions to solve oftentimes amorphous business problems. They work in a variety of industries including healthcare, transportation, manufacturing, finance, banking, software services, and telecommunications. The International Institute of Business Analysis defines a business analyst as an “agent of change,” who identifies and executes new opportunities for businesses to capitalize on technology. Business analysts often specialize in one of the following roles: business systems analyst, systems analyst, functional analyst, service request analyst, or agile analyst, depending on one’s area of interest. For example, a functional analyst helps organizations use and integrate their technology with other systems. A service request analyst handles user inquiries and system enhancements.

Successful business analysts possess strong foundational data science skills as well as an ability to develop strategic business and project plans, identify key performance indicators, create use-case scenarios, and engage and communicate with stakeholders at all levels of the organization. They must be able to take a holistic view of a business problem or challenge and work with various individuals to get the information necessary to drive IT changes. Those transitioning into a business analyst role may have previously worked as software developers or project managers.

A business analyst’s daily responsibilities may include reviewing data about current work habits, interviewing users to identify technology challenges, preparing documents that outline detailed functional requirements needed to address those challenges, creating flowcharts for programmers to follow, designing and executing test scripts or scenarios, and managing change requests related to the project.   

What Is a Data Analyst?

Data analysts, on the other hand, use specialized analysis techniques and tools to determine how businesses can use data to make more informed decisions. This may sound very similar to the role of business analyst; however, data analysts work more directly with the data itself. They’re responsible for identifying important business questions, applying the appropriate statistical techniques to harness structured and unstructured data, and performing complex data analysis to extract useful information and develop conclusions. Data analysts are also responsible for protecting an organization’s data and ensuring that all data repositories produce consistent and reusable data. Data analysts and business analysts work in many of the same industries and particularly those that rely on technology. Data analysts and data scientists are also increasingly employed in other industries such as agriculture, travel, food, oil, and auto insurance—each of which has only begun to tap into the power of big data.

Successful data analysts are those who can extract and analyze big data as well as present results to executive management or departmental managers. This requires a unique balance of technical data knowledge and business acumen—a skillset professionals can gain or hone in a data science degree program. Those transitioning to a data analyst role may have previously worked in fields such as accounting, healthcare information management, database administration, computer science, or business.

A data analyst’s daily responsibilities may include culling data using advanced computerized models, removing erroneous data, performing analyses to assess data quality, extrapolating data patterns, and preparing reports (including graphs, charts, and dashboards) to present to management.

Business Analyst vs. Data Analyst: 4 Main Differences

Although business analysts and data analysts have much in common, they differ in four main ways.

1. Overall responsibilities. Business analysts provide the functional specifications that inform IT system design. Data analysts extract meaning from the data those systems produce and collect. Data scientists can often automate the business analyst’s tasks and may be able to provide some of the business insights as well.
2. Salary. Data analysts earn an average salary of $70,246, according to Indeed.com. Business analysts earn a slightly higher average annual salary of $75,575. Business analysts tend to make more, but professionals in both positions are poised to transition to the role of “data scientist” and earn a data science salary—$113,436 on average.
3. Skillsets. Business analysts require data science knowledge as well as skills related to communication, analytical thinking, negotiation, and management. Data analysts require similar skills with a more in-depth focus on technical data manipulation.
4. User interaction. As project facilitators and managers, business analysts often have more direct interaction with systems users, customers, system developers, and others than data analysts do. That’s because business analysts may frequently interview people to learn more about how technology can be improved to help business processes. They work collaboratively with others throughout the duration of a single project. Although data analysts may consult initially with internal subject matter experts to identify important data sets, the bulk of their work is done independently.   

How to Prepare for a Business Analyst or Data Analyst Role

If you’re thinking about transitioning to a business analyst or data analyst position, consider earning a Master of Science in Data Science online from the University of Wisconsin. The 12-course curriculum focuses on building both technical data science skills and “power” skills such as leadership, communication, and project management—skills that are beneficial in either position. Plus, a data science master’s will help you advance to a high-paying, in-demand data science role. To learn more, see our page “What Do Data Scientists Do?”

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Big data is making big waves in many industries. For example, the airline you flew last month probably analyzed data to ensure your safety. Your favorite chain restaurant looked at big data to create that new specialty item on the menu.

Industries of all shapes and sizes are starting to tap into the power of big data. This bodes well for savvy data scientists who can turn this data into actionable insights. Here are five industries that are being transformed by big data.

Agriculture

Data science is the “next revolution in sustainable agriculture,” according to an article recently published in Ag Professional. That’s because it plays a critical role in helping farmers increase crop yields using the same number of acres, enabling them to meet the growing population’s agricultural needs. By measuring variables such as temperature, wind speed, and rainfall, data scientists help farmers predict potential outcomes as well as make decisions that reduce the impact of field variability and improve yield.

Farmers are also using data analytics to proactively manage dairy production. For a real-world example of this, see the following video, which is viewed and discussed by UW Data Science students in the course DS 745: Visualization and Unstructured Data Analysis.

Travel

“It would be almost impossible to overstate the transformative potential of big data to the travel industry,” writes Thomas H. Davenport in a report titled “At the Big Data Crossroads: Turning Towards a Smarter Travel Experience.” To inform his research, Davenport interviewed 21 different travel companies to learn more about how they use big data to their advantage. For example, KAYAK uses data analytics to enable flight price forecasting that predicts whether the price of a flight will go up or down in the next seven days. It also provides a statistical confidence level behind these predictions. Marriott Hotels uses data analytics to predict the optimal price at which to fill its rooms.

Airbnb uses machine learning to detect host preferences and compute the likelihood that relevant hosts will want to accommodate a guest’s request. Then, Airbnb serves up likely matches more prominently in the search results.

Mark Ferguson, a management professor at the University of South Carolina, told CIO.com that cruise lines usually collect a substantial amount of customer-related data, because they track onboard spending habits over the entire trip. By offering a discounted base price to customers who frequently spend extra money aboard the ship, these companies likely see higher profits overall.

Airlines have also begun to use data science for a variety of purposes. For example, by analyzing data created by jet engines and sensors that monitor the environment (e.g., temperature, humidity, and air pressure), airlines can predict when various parts of a plane are likely to fail so they can take preventative maintenance actions. Following are several other ways in which airlines are starting to use big data:

• Identify profitable new routes by analyzing customer flying patterns
• Avoid accidents and delays by analyzing the geo-location of storms and other severe weather conditions
• Enhance customer service by analyzing a variety of customer data, including travel itineraries, social media, and frequent flyer status to improve front desk and on-board services. Southwest, United Airlines, and Delta are also using data analytics to improve the customer experience, according to the Fortune article “For the Airline Industry, Big Data is Cleared for Take-off.”

Food and Dining

Restaurants are also starting to tap into the power of big data to drive menu changes, improve services, identify new location opportunities, and more. In a report titled, “Big Data and Restaurants: Something to Chew On,” the National Restaurant Association encourages restaurants to take advantage of data analytics. The association says the data from a variety of systems, including point-of-service (POS) (e.g., sales by time, size of party, and menu items), accounting (e.g., payroll expenses, credit card sales versus cash, and gas or electric bills), and employee scheduling is a “vein of gold just waiting to be mined.” Other sources of data to mine include OpenTable, Facebook, Twitter, Yelp, TripAdvisor, Foursquare, Urbanspoon, or Instagram.

Some restaurants are using technology with their POS systems to gather information about customers (e.g., whether someone is a new or repeat customer, what they ordered, what they tipped, and how long they were at the table) and create profiles that include favorite drinks and food so they can target promotions and loyalty programs accordingly.

The association report also features various restaurants that have already taken the leap into the world of big data. For example, Chicago-based Levy’s restaurants use big data to understand the correlation between sporting events and food and beverage purchases. Panera tracks guest purchases and habits through loyalty cards and then leverages this data with primary marketing research and third-party data to guide its brand strategy, drive new customer acquisition, retain existing customers, and assist in real estate planning.

Oil

The rising cost of extraction and the turbulent state of international politics are two of the reasons why the oil industry has begun to rely on data analytics to drive business decisions, according to an article published on Forbes. In the article, contributor Bernard Marr writes that Royal Dutch Shell, one of the largest oil and gas companies, has developed a data-driven oilfield that reduces the cost of drilling for oil. The technique requires the ability to monitor low frequency seismic waves beneath the earth’s surface to determine whether they’re distorted as they pass through oil or gas.

“Data from any prospective oil field can then be compared alongside that from thousands of others around the world to enable geologists to make more accurate recommendations about where to drill,” writes Marr.

In addition, oil companies are using big data to monitor and improve machine performance as well as streamline the transport, refinement, and distribution of oil and gas.

Auto Insurance

According to the McKinsey article “Unleashing the Value of Advanced Analytics in Insurance,” auto insurers already analyze a variety of information, including real-time data about driving habits, to set premium rates and discounts. They also factor behavior-based credit scores from credit bureaus into their analyses, because there is empirical evidence suggesting that people who pay their bills on time are also safer drivers.

Allstate’s Arity unit, an independent subsidiary focused on connected-car data, uses data from telematics initiatives to develop models and products for auto insurance as well as ridesharing and roadside assistance, according to Digital Insurance.

Looking Ahead

These five big data industries are just a handful of those starting to realize the power of data analytics. (Here are several more.) As we look ahead, the power of big data will become even bigger. Companies both large and small will continually look for ways to capture and analyze customer data to improve profits and drive business intelligence.

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If you’re a data scientist, you probably recognize the names DJ Patil and Jeff Hammerbacher. Not only are both often credited with popularizing the term “data science,” but they also exemplify the modern data scientist—that is, one who applies his or her data-savvy expertise in any setting that demands it, including healthcare, e-commerce, social media, and journalism—just to name a few.

Patil, the chief data scientist at the United States Office of Science and Technology Policy, boasts an extensive resume that includes stints at LinkedIn, Greylock Partners, Skype, PayPal, and eBay. [Read more…]