Businesses of all sizes rely on data to make decisions—and in an uncertain and fast-paced world, they need to make decisions quickly. Whether business leaders need to set the price of a product, determine how many salespeople to hire, or explore acquisition opportunities, they need to be able to gather, analyze, and interpret the right data to make the best decision for the organization.
That’s where business analytics comes into the picture.
What is Business Analytics?
The IT analyst firm Gartner defines business analytics as the use of a set of software applications to build statistical models that help leaders look at data on past business performance, understand the current situation, and predict future scenarios.
The focus on future outcomes separates business analytics from disciplines such as business intelligence. According to the data analytics company Tableau, business intelligence emphasizes the what and the how, so that organizations can continue what’s working and change what isn’t. Business analytics, on the other hand, focuses on why things happen to enable educated, data-driven predictions. Business intelligence might help a company decide to manufacture more of a certain product to keep up with increased sales, while business analytics would explore the factors that led to increased sales in order to drive additional sales of that product or generate ideas for boosting sales of other products.
Keep reading to explore the common uses of business analytics, the necessary skills for success in a business analytics role, and the career paths for those who earn a master’s degree in business analytics.
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Common Applications of Business Analytics
The data analytics company MicroStrategy identifies four typical uses of business analytics, ranging from the least to the most complex.
Descriptive analytics summarizes data to explain what has happened or is happening. Statistical techniques such as data aggregation (collecting and filtering data) and data mining (using statistical techniques) enable business analysts to identify trends in data. A descriptive analysis is most commonly presented in a report that uses visual aids such as charts and graphs to make the analysis accessible to a wide range of internal stakeholders.
Diagnostic analytics looks at what has happened to try to determine the root cause of those events using mathematical functions such as probabilities, likelihoods, and the distribution of outcomes. This information tends to be displayed in a business dashboard, which is a software application that provides multiple data visualizations in a single screen and offers filters so users can drill down into specific data sets of interest.
Predictive analytics typically combines statistical models and machine learning algorithms to predict the likelihood of various outcomes, such as whether consumers will like a new flavor of sports drink or how much healthcare costs will increase. Because these analyses are used to create sales and marketing forecasts, they tend to be presented in highly detailed reports.
Organizations can make the move directly from descriptive to predictive analytics if they have both machine learning expertise and technology in house.
Prescriptive analytics provides recommendations for the actions that will provide the best results. Accomplishing this requires iterative analysis, ongoing testing, and deep learning. (The software company MathWorks describes deep learning as a subset of machine learning that enables computer models to analyze data and perform complex tasks.) Use cases for prescriptive analytics include audio speech recognition, driverless cars, and e-commerce recommendation engines.
The discipline of business analytics is closely related to that of data analytics, but there are some notable differences. The data analyst is typically responsible for maintaining the database and cleaning up the data so that it can be utilized in reports, while the business analyst uses the data for strategic decision-making. The business analyst role also involves evaluating existing business processes to find ways to improve efficiency or cut costs—something that is not the responsibility of a data analyst.
Top Business Analyst Skills
At a minimum, a business analyst should know how to work with data sets that are increasingly growing in both size and sophistication. This requires two key skills: Understanding popular programming languages associated with data analysis, such as Python, R, and Julia, and using common data visualization software such as Tableau, SBSS, or SAP Business Objects.
It’s also helpful to be familiar with the data sets that are most pertinent to the industry you work in—claims data for insurance, material costs for construction, and so on. A general understanding of the nuances of these data sets helps business analysts get a head start on statistical analysis and interpretation, as they have a general idea of what to look for.
A background in statistics, engineering, or computer science is clearly valuable for professionals interested in business analytics. However, as the work of coding and developing data models are often the responsibility of the data analyst, a business analyst needs strong skills in management, communication, and leadership in a corporate environment.
The news site Business Analyst Times describes several key soft skills for someone in a business analytics role. Analysts should know how to:
- Communicate effectively: As you engage with stakeholders, it’s important to avoid jargon and technical terminology. This helps in four ways: to define the objective of a project, to ask clear questions that lead to the right answers, to communicate your findings to business leadership, and to prepare reports and requirements documentation.
- Lead meetings: While email provides a valuable written record, some stakeholders will be more comfortable discussing details in a face-to-face setting. You’ll need to know how to keep everyone on time and on topic and deliver follow-up as needed. If the meeting is a workshop or presentation, share your agenda in advance and stick to it, as this will ensure that participants arrive prepared and are not surprised by unexpected content.
- Actively listen: Try to understand not just what someone is saying but also the context behind it. For example, what motivation might a particular department have for making a request—is the team short-staffed, underfunded, or low on resources? Did a previous report cast the team unfavorably? This information will guide your fact-finding.
- Manage their time: Business analysts need to deliver projects on time—before the quarterly earnings call, for example, or in time for the annual executive retreat. This requires both multitasking and priority setting, as certain tasks will be more important than others. If you work on a business analytics team, this may also involve project or personnel management.
- Manage stakeholders: A business analyst engages with stakeholders across an organization, from executive leadership to front-line workers. Each will have varying requirements for and expectations of the project—along with varying levels of support for your work. Meeting everyone’s objectives will be a delicate balancing act.
- Create visual models: Business analytics projects often produce a lot of information. Presenting this as text can make it difficult to understand and interpret, especially for busy executives who need to make time-sensitive decisions. Visual representations of data will provide a high-level overview while also allowing for additional levels of granularity for those stakeholders who will want additional details.
Other valuable skills for a business analyst include:
- Collaboration across the organization: As noted above, a business analyst needs to build consensus with a wide range of stakeholders. This requires being an advocate for the discipline of business analytics and how it aligns with existing goals and strategies. Creating a multidisciplinary analytics team that includes representatives from technology, operations, legal, and HR, can help demonstrate the value of analytics for individual departments and the business as a whole.
- Making use of all available data: According to Microsoft, organizations only use 40 percent of their data for business analytics, and some use as little as 25 percent. An effective business analyst will be able to identify unused data sets that can help a company to address business needs such as financial performance, strategic management, or operational efficiency. This is especially valuable for small businesses and nonprofit organizations that don’t have the budget to acquire external data sets or research reports.
Career Paths for Master’s in Business Analytics Graduates
Obtaining a Master’s in Business Analytics prepares professionals for leadership roles where their understanding of data, statistics, and predictive modeling helps an organization make data-driven decisions with confidence.
A search on Indeed.com found more than 12,000 open job titles that include the term “business analyst. Indeed also reported an average annual salary of nearly $80,000, though larger companies in the energy, technology, and finance industries reported salaries above $100,000 per year.
Specific day-to-day tasks may vary depending on the type of company. For example, a business analyst at McKinsey, the consulting firm, can be expected to present the results of an analysis to a client’s executive leadership and then help the client implement agreed-upon recommendations. Meanwhile, business analysts at companies that use Salesforce software can expect to create and develop reports to support sales, marketing, and other departments.
Some professionals use a master’s degree as a path toward earning an MBA. For example, students at Northeastern University’s D’Amore-McKim School of Business who earn an MS in Business Analytics may be able to apply their credits earned toward an eligible MBA at Northeastern. They can also take advantage of the Double Husky Scholarship and receive a 25 percent discount off of their remaining MBA courses.
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