In the current business landscape, information is one of the most important resources for maximizing profitability, minimizing risk and keeping customers happy. Historically, companies only had to compete with brick-and-mortar businesses in their area and a handful of national brands. However, thanks to the rise of the internet and globalization, companies of all sizes are able to market their products and services to an international audience.
To capitalize on this new frontier, for-profit businesses are investing in new data visualization tools, incorporating statistical methods into their decision-making and hiring an army of data scientists to ensure they’re running at peak efficiency. This push toward business analytics has provided a wealth of insight into day-to-day operations, investment opportunities and customer preferences. But what is business analytics, exactly, and how does it directly benefit companies’ bottom line?
Defining business analytics
Business analytics has several overlapping definitions that highlight how widespread the practice has become: It’s a toolbox of statistical analysis processes, a field of academic study, a profession and a subset of information technology. One of the most comprehensive overviews comes from the International Institute of Business Analysis, which defines business analytics as “a practice by which a specific set of techniques, competencies and procedures are applied to perform the continuous exploration, iteration, and investigation of past and current business data, for the purposes of obtaining insights about a business that can lead to improved decision-making.”
Rather than relying on guesswork, business analytics allow organizations to transform raw data into evidence-based insights that can be acted upon in real time. For example, a company could use predictive modeling to gauge future product demand based on past sales metrics, leading to more accurate inventory management. In this way, business analytics often influence every facet of an organization, and not just those focused on turning a profit. Government agencies use data analytics to track population demographics and trends. Nonprofit groups rely on analysis tools to forecast resource needs, reduce operating costs and ensure social programs are cost-effective. Almost every industry leverages data in some way, from manufacturing and transportation to health care and retail, but business analytics is a bit different from standard data analysis.
Although data analysis plays a leading role in business analytics, the two terms are not interchangeable. Data analytics involves sifting through massive data sets to identify patterns, formulate conclusions and support organizational decisions. Business analytics is a much broader set of practices that focuses on answering specific questions and locating evidence-based solutions. For example, a data scientist could spend weeks poring over spreadsheets and formulating conclusions that do not have a direct impact on day-to-day operations. In contrast, business analytics experts would first develop a set of questions or needs that drive their data mining and analysis activities. While subtle, this difference is crucial for understanding how business analytics produce tangible results for profit-minded organizations.
Why is business analytics important?
Business analytics help companies make sense of the raw data they collect on internal processes, sales performance, customer preferences and more. In the past, business decisions were often made at the very top and were loosely based on historical information and trends. However, as organizations’ data collection and analysis capabilities improved, it became increasingly difficult to leverage the massive amount of information they were gathering. This era of “big data” has been both a boon and a constraint for businesses across industry lines, as making the best use of this information requires the right combination of business analytics programs, IT infrastructure and data scientists. In fact, research from cloud software developer Domo found that over 2.5 quintillion bytes of data are created each day – by the end of 2020, the firm anticipates 1.7MB of data will be generated every second for each person on earth.
When integrated effectively, business analytics offer a variety of benefits for today’s fast-paced companies, including:
- Cost savings: By collecting and analyzing data on internal processes, supply chains and employee performance, organizations can meaningfully reduce their overhead costs. For example, HR leaders can identify shortcomings in their hiring processes that lead to unnecessary delays in onboarding new candidates. Data scientists could then use statistical analysis to determine how much the business is losing to dropped productivity. Supply chain managers can use this same process to compare the prices of different suppliers and third-party vendors to reduce sourcing costs for raw materials.
- Higher revenue potential: Business analytics has also played a key role in optimizing marketing and advertising campaigns. Using consumer data, companies can create accurate customer profiles and personas that embody their target demographics. These profiles can then be used to personalize marketing materials and increase brand awareness with high-intent shoppers. Tactics like these are just as useful for B2B retailers as they are for consumer-focused companies. Understanding the intimate needs of businesses can help organizations outcompete others in their industry and position themselves as leaders in their market.
- Improved adaptability: The business world is constantly changing as new products and services hit the market. Economic trends and global emergencies can also have a major impact on a company’s bottom line, as demonstrated by the COVID-19 pandemic. According to research from McKinsey & Company, it could take up to five years for the most affected industries to recover and return to pre-2019-level contributions to the GDP. During the interim, companies will need to leverage business analytics to find short-term solutions that will help keep them afloat. In the long term, analytics will be crucial for adapting to new market conditions, rebuilding customer bases and streamlining internal sales and management processes.
These are just a few of the benefits of business analytics, yet they help illustrate how transformative data can be for profit-minded companies. Prioritizing data-driven decisions over intuition and guesswork can enable real-time demand forecasting, predictive modeling and other statistical methods essential to modern business performance.
Breaking down business analytics methods
While business analytics processes differ between companies, the core methodology is the same regardless of the industry, market conditions or organizational needs. As noted by the IIBA, there are 5 essential practice domains for data analysis that every business should be capitalizing on. This step-by-step approach ensures companies are taking full advantage of the raw data at their disposal and generating data-driven decisions that deliver a tangible return on investment.
1. Identify research questions
Before data scientists can start analyzing raw information, business leaders must first identify and define the specific problem or opportunity they’re addressing. Through collaboration, business analysts, managers and C-suite executives formulate detailed research questions that need to be answered. Then, this cross-department team will plan out the specific business analytics approaches they plan to use and set clear expectations for the project.
2. Source the data
Once a research question has been formulated, the next step is to determine which data sets are relevant. Data scientists must organize this information in ways that minimize the risk of false positives, duplicate data entries and incomplete forecast models. Technology plays an essential role during this stage, which is why many business analysts are trained in data visualization software, data transformation and SQL. After the data has been collected and validated, analysts are able to start their statistical analysis and predictive modeling with increased confidence.
3. Analyze the data
Depending on the specific questions or opportunities being researched, data scientists will choose a set of data analysis techniques they plan on using. Business analytics incorporates a wide range of statistical methods that each serve a different function, including:
- Descriptive analytics: This method is used to describe or summarize a company’s existing data to understand past and present business performance. Often considered the simpletest form of data analysis, descriptive analytics can help locate operational strengths and weaknesses, build accurate customer profiles and improve sales efficiency. As noted by IBM, this form of business analytics is typically leveraged when companies want to know what has happened within their organization and how it’s currently operating.
- Diagnostic analytics: Whereas descriptive analytics is focused on the “what” of business performance, diagnostic analytics is concerned with the “how” and “why.” By leveraging data discovery and mining, business analysts can determine the root causes of inefficiencies and events, Garnet explained. Generally, this type of data analysis does not deal in absolutes, instead relying on probabilities and correlations to identify trends and patterns.
- Predictive analytics: While also based on probabilities, predictive analytics seeks to forecast future events using statistical models, business analytics programs and applications, machine learning and other advanced IT tools. When used effectively, this data analysis technique can be used for predictive modeling of economic trends, customer sentiments and more.
- Prescriptive analytics: This business analytics method goes one step beyond predictive analysis by offering detailed recommendations based on the identified trends. Prescriptive analytics drives better outcomes by keeping key decision-makers informed about market fluctuations and other events that impact their company’s profitability.
These four business analytics techniques are often used in tandem to create the most accurate picture of a company’s performance, outstanding business opportunities and customer demographics. However, bringing together insights generated from different statistical methods requires in-depth knowledge of data analysis and experience with analysis platforms.
4. Interpret and report the findings
After the hands-on analysis has been completed, data scientists and business analysts must work together to organize their findings in detailed reports. Since these documents are used by business-minded professionals, they often have to interpret the data for audiences that may be unfamiliar with the nuances of business analytics. The IIAB notes that identifying specific stakeholders is crucial during this stage, as it can help communicate findings in ways that resonate with professionals from different backgrounds.
5. Leverage the results to improve decision-making
After the reports have been created, business leaders use the presented information to formulate data-driven decisions that have the highest likelihood of a positive outcome. For example, a marketing manager could leverage behavioral data and customers profiles to create targeted advertising campaigns based around current events. In many cases, business analytics expose weaknesses in how companies operate, leading the C-suite to make sweeping changes that impact every facet of their organizations. Developing a detailed implementation plan with clear key performance indicators can help guide these improvements and ensure data is a driving force behind any optimization efforts.
Careers in business analytics
As a whole, business and financial professions are projected to expand 7% between 2018 to 2028, according to the Bureau of Labor Statistics, which is slightly faster than the average for all U.S. occupations. This demand is being driven by long-standing globalization trends and the growing reliance on data analysis to maximize business performance and profitability. The field of business analytics is full of opportunities for individuals with the right skills, knowledge and experience, which is why a Master’s in Business Analytics can be a valuable stepping stone for students and mid-career professionals. Below are just a few popular career paths for those interested in data analysis, statistical methods and business management:
- Business analyst: Business analysts are responsible for gathering, organizing and analyzing data to help companies answer specific questions about their financial performance and operations. Using statistical analysis, they develop solutions to a variety of challenges, from employee productivity issues to supply chain inefficiencies. According to the BLS, employment of management analysts – a category that includes business analysts, is expected to grow a considerable 14% between 2018 to 2028, with the median wage falling around $85,260.
- Marketing specialist: Big data is at the heart of modern marketing, which is why many companies look for skilled data scientists to supercharge their advertising strategies. Marketing specialists study customer demographics, market conditions, past sales performance and other relevant data to understand how to advertise to specific audiences. They also conduct SEO research to identify trending, high-volume keywords that can be acted upon. The employment of marketing specialists is forecasted to expand by an even greater 20% between 2018 to 2028, according to the BLS. The median annual salary for this role stood at $63,790 in May 2019.
If you’re looking to pursue a career in business analytics, the online Master of Business Analytics program at the University of Nevada, Reno offers the foundational and specialized knowledge you need to stand out in the job market. This MSBA degree provides hands-on learning opportunities in business analysis methods, data visualization, predictive modeling and other statistical analysis techniques. Students also gain first-hand experience with data analysis software used by companies around the world.