Business analytics (BA) is a set of disciplines and technologies for solving business problems using data analysis, statistical models and other quantitative methods. It involves an iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis, to drive decision-making.
Data-driven companies treat their data as a business asset and actively look for ways to turn it into a competitive advantage. Success with business analytics depends on data quality, skilled analysts who understand the technologies and the business, and a commitment to using data to gain insights that inform business decisions.
Before any data analysis takes place, business analytics starts with several foundational processes:
Initial analysis is typically performed on a smaller sample data set. Analytics tools range from spreadsheets with statistical functions to complex data mining and predictive modeling applications. Patterns and relationships in the raw data are revealed. Then new questions are asked, and the analytic process repeats until the business goal is met.
Deployment of predictive models for business analytics involves a statistical process known as scoring and uses records typically located in a database. Scores help enterprises make informed, real-time decisions within applications and business processes.
Business analytics also supports tactical decision-making in response to unforeseen events. Often the decision-making is automated using artificial intelligence to support real-time responses.
Different types of business analytics include the following:
Some schools of thought include a fourth approach, diagnostic analytics. It's similar to descriptive analytics, analyzing the state of a business and diagnosing why certain events and outcomes happened.
The terms business intelligence (BI) and business analytics are often used interchangeably. However, there are key differences.
Companies usually start with BI before implementing business analytics. BI analyzes business operations to determine what practices have worked and where opportunities for improvement lie. It uses descriptive analytics to do this.
In contrast, business analytics focuses on predictive analytics, generating actionable insights for decision-makers. Instead of summarizing past data points, business analytics aims to predict trends.
The data collected using BI lays the groundwork for BA. From that data, companies can choose specific areas to analyze further using business analytics.
Data analytics is the analysis of data sets to draw conclusions about the information they contain. Data analytics doesn't have to be used in pursuit of business goals or insights. It's a broader practice that includes business analytics.
Business analytics involves using data analytics tools in pursuit of fundamental business insights. However, because it's a general term, data analytics is sometimes used interchangeably with business analytics.
Data science uses analytics to inform decision-making. Data scientists explore data using advanced statistical methods. They allow the features in the data to guide their analysis. The more advanced areas of business analytics resemble data science, but there's a distinction between what data scientists and business analysts do.
Even when advanced statistical algorithms are applied to data sets, it doesn't necessarily mean data science is involved. True data science uses custom coding and explores answers to open-ended questions. In contrast, business analytics excels at solving a specific question or problem.
The key benefit of business analytics is that it can help organizations identify patterns in data and generate new insights. With this information, they can improve existing processes and identify new strategies. Some other benefits of business analytics include the following:
Businesses might encounter both business analytics and BI challenges when trying to implement a business analytics strategy:
There are several business analytics and BI tools that can automate advanced data analytics functions. In many cases, they require few of the specialized skills or deep knowledge of the programming languages used in data science.
These tools help businesses organize and make use of the massive amounts of data that modern internet of things and enterprise cloud applications generate. They come as part of supply chain management, enterprise resource planning and customer relationship management applications.
Business analytics tools are used in many ways. For example, they can identify customers who are likely to cancel a service offering subscription. A company would first take aggregate data from enterprise applications, using a DataOps analytics platform like DataKitchen. Then it would use a business analytics tool to present that data to employees and help them identify customers at risk of canceling a contract or becoming less engaged with the company.
When choosing a business analytics tool, organizations should consider the following:
A good business analytics tool is intuitive and user-friendly. It also provides a full suite of features for more advanced analytics.
A sampling of business analytics tools as compiled by Gartner includes Amazon QuickSight, Domo, Google Looker, Microsoft Power BI, MicroStrategy, Qlik Sense, SAP BusinessObjects BI Suite, Sisense Fusion Analytics and Tableau.
Business analytics professionals' main responsibility is to collect and analyze data to influence strategic decisions that a business makes. Some initiatives they might provide analysis for include the following:
Business analysts must have a mixture of hard skills and soft skills. A business analyst doesn't need a deep understanding of IT but does need to understand how systems work together. Some business analysts start their career in an IT-centric role and then move into a business analytics role.
When recruiting for these jobs, employers typically look for detail-oriented people with good communications skills. They need to have capabilities in some or all of these areas: cost-benefit analysis; process modeling; stakeholder analysis; analytical problem-solving; and IT systems, particularly databases, data visualization, data management, business analytics and business intelligence.
Certifications for business analysts and related careers include the following:
There are several ways to prepare for business analytics certification exams. One option is to take an online course offered by self-directed learning sites, such as Udemy. Users can subscribe to these sites to access educational content, including structured bootcamp courses. The time needed to complete these courses varies from days to months.
Another route to certification is to pursue a master's degree in business analytics. This path is more expensive and time-intensive, taking a year or two to complete.
There are several career paths for a person with a business analytics background. Some common job titles and annual salaries as of 2024, according to PayScale and Salary.com, include the following:
Given the rise of big data, business analysts will continue to be important in the coming years. More data means a greater need for analyses, which all but assures job security in this field.
The Bureau of Labor Statistics (BLS) doesn't collect data on business analytics roles. However, it does monitor related analyst positions, such as operations research analyst and computer systems analyst. Jobs in operations research are expected to grow 23% from 109,900 in 2022 to 134,600 in 2032, according to the BLS. Computer systems analyst positions are expected to grow 10% from 531,400 to 582,500 in that same time frame. Both are above average growth rates, according to the BLS.
The capabilities of data science tools have expanded dramatically in the last few years. Learn more about the most up-to-date data science tools to add to your skill set.
25 Apr 2024