What is business analytics?
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.
How business analytics works
Before any data analysis takes place, BA starts with several foundational processes:
- Determine the business goal of the analysis.
- Select an analysis methodology.
- Get business data to support the analysis, often from various systems and sources.
- Cleanse and integrate data into a single repository, such as a data warehouse or data mart.
Initial analysis is typically performed on a smaller sample data set of data. 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 iterates until the business goal is met.
Deployment of predictive models involves a statistical process known as scoring and uses records typically located in a database. Scores help enterprises make more informed, real-time decisions within applications and business processes.
BA also supports tactical decision-making in response to unforeseen events. Often the decision-making is automated using artificial intelligence to support real-time responses.
Types of business analytics
Different types of business analytics include the following:
- descriptive analytics, which tracks key performance indicators (KPIs) to understand the present state of a business;
- predictive analytics, which analyzes trend data to assess the likelihood of future outcomes; and
- prescriptive analytics, which uses past performance to generate recommendations for handling similar situations in the future.
Some schools of thought also include a fourth approach, diagnostic analytics, which is like descriptive analytics. It analyzes the state of a business and diagnoses why certain events or outcomes happened.
Business analytics vs. business intelligence
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. BI uses descriptive analytics.
In contrast, business analytics focuses on predictive analytics, generating actionable insights for decision-makers. Instead of summarizing past data points, BA 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.
Business analytics vs. data analytics
Data analytics is the analysis of data sets to draw conclusions about the information they contain. Data analytics does not have to be used in pursuit of business goals or insights. It is a broader practice that includes business analytics.
BA involves using data analytics tools in pursuit of business insights. However, because it's a general term, data analytics is sometimes used interchangeably with business analytics.
Business analytics vs. data science
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 is 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. That's because true data science uses custom coding and explores answers to open-ended questions. In contrast, business analytics aims to solve a specific question or problem.
Common challenges of business analytics
Businesses might encounter both business analytics and business intelligence challenges when trying to implement a business analytics strategy:
- Too many data sources. There is an increasingly large spectrum of internet-connected devices generating business data. In many cases, they are generating different types of data that must be integrated into an analytics strategy. However, the more complex a data set becomes, the harder it is to use it as part of an analytics framework.
- Lack of skills. The demand for employees with the data analytic skills necessary to process BA data has grown. Some businesses, particularly small and medium-sized businesses (SMBs), may have a hard time hiring people with the BA expertise and skills they need.
- Data storage limitations. Before a business can begin to decide how it will process data, it must decide where to store it. For instance, a data lake can be used to capture large volumes of unstructured data.
Business analytics examples and tools
There are several BA and BI tools that can automate advanced data analytics functions and 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. These applications may be part of supply chain management, enterprise resource planning and customer relationship management applications.
Below are some business analytics tools on the market:
- Dundas BI, with automated trend forecasting and a user-friendly interface;
- Knime Analytics Platform, which has high-performance data pipelining and machine learning;
- Qlik's QlikView with data visualization and automated data association features;
- Sisense, known for its dynamic text-analysis features and data warehousing;
- Splunk, which has intuitive user interface and data visualization features;
- Tableau, which has advanced unstructured text analysis and natural language processing capabilities; and
- Tibco Spotfire, which offers powerful, automated statistical and unstructured text analysis.
BA 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 use aggregate data from enterprise applications, using a DataOps analytics platform like DataKitchen. Then it would use a BA tool to present that data to employees. The BA tool would help employees identify customers at risk of canceling and let them take steps to keep those customers.
When choosing a business analytics tool, organizations should consider the following:
- the sources which their data comes from;
- the type of the data to be analyzed; and
- the usability of the tool.
A good business analytics tool is intuitive and user-friendly. It also provides a full suite of features for more advanced analytics.
Roles and responsibilities in business analytics
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:
- identifying strategic opportunities from data patterns;
- identifying potential problems facing the business and solutions;
- creating a budget and business forecast;
- monitoring progress with business initiatives;
- reporting progress on business objectives back to stakeholders;
- understanding KPIs; and
- understanding regulatory and reporting requirements.
Business analysts must have a mixture of hard and soft skills. A business analyst does not need a deep understanding of IT but does need to understand how systems work together. Some business analysts choose to move from an IT-centric role into a BA role.
When recruiting for these jobs, employers typically look for the following capabilities:
- ability to perform cost-benefit analysis;
- familiarity with process modeling;
- understanding stakeholder analysis;
- analytical problem-solving;
- oral and written communications skills;
- a basic understanding of IT systems, including databases;
- experience with BA tools and software; and
- ability to create visual representations of data.
Certifications and courses for business analytics
There are several popular certifications for business analysts and related career courses:
- Axelos ITIL certification
- International Institute of Business Analysis (IIBA) Entry Certificate in Business Analysis
- IIBA Certification of Competency in Business Analysis
- IIBA Certified Business Analysis Professional
- IIBA Agile Analysis Certification
- International Qualification Board for Business Analysts Certified Foundation Level Business Analyst
- International Requirements Engineering Board Certified Professional for Requirements Engineering
- Project Management Institute (PMI) Professional in Business Analysis
- PMI Disciplined Agile Scrum Master
There are several ways to prepare for BA 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 a master's in business analytics. This path is more expensive and time-intensive, taking a year or two to complete.
Career and salary trends in business analytics
There are several career paths for a person with a BA background. Some common job titles and annual salaries as of 2021, according to PayScale, include the following:
- senior business analyst -- $86,050
- business systems analyst -- $70,155
- business analyst -- $69,785
- business intelligence analyst -- $69,639
- junior business analyst -- $51,009
Learn more about the data analytics skills that are in demand and how you can add them to your skillset.