What is Business Analytics?

To understand how and why the business analytics field is growing, we can answer the question, “what does business analytics do?”

Let’s look at the goals and tasks handled by a business analyst. Business analytics is a field that drives practical, data-driven changes in a business. It is a practical application of statistical analysis that focuses on providing actionable recommendations. Analysts in this field focus on how to apply the insights they derive from data. Their goal is to draw concrete conclusions about a business by answering specific questions about why things happened, what will happen and what should be done.

Business analytics combines the fields of management, business and computer science. The business aspect entails both a high-level understanding of the business as well as the practical limitations that exist. The analytical part involves an understanding of data, statistics and computer science. This combination of fields allows business analysts to bridge the gap between management and technology. Effective communication and problem-solving are also elements of business analytics to translate insights from data to information that is easily communicated to executives.

Business intelligence is a related field that also uses data to help understand and inform a business. What is the difference in goals of business analytics compared to business intelligence? Though both fields use data to answer questions business intelligence aims to understand what has happened in an organization to get to where you are. This includes measuring and tracking key performance indicators (KPIs.) Business analytics, on the other hand, aims to inform changes to a business through utilization of predictive models that provide insight into the outcome of proposed changes.

Business analytics utilizes big data, statistical analysis, and data visualization to implement organization changes. Predictive analytics is an important aspect of this work as it involves available data to create statistical models. These models can be used to predict outcomes and inform decision making. By learning from existing data, business analytics can make concrete recommendations to solve problems and improve businesses.

Business analytics has applications in a wide array of different businesses. Some companies are developing innovative ways to use big data in order to improve their customer’s experience and maximize profits. Here is a real life example of business analytics:

Fast-food companies have begun to implement business analytics to streamline their restaurants. Who wants to have a slow experience in a fast-food drive-thru? By monitoring how busy the drive-thru is these businesses can increase efficiency during peak hours. When the line gets long, the digital order boards change. They begin to highlight items that can be prepared quickly. This leads to more simple orders that can be completed quickly. When the lines are short, slower items with higher margins are featured. In this way, the store can respond to real-time needs to improve efficiency.

Other types of business analytics applications do more than just respond to the current situation. These techniques help businesses predict which customers are less likely to return. They can then target advertising and promotions to these customers to improve retention. Here are some examples of predictive analytics in business:

Casinos use business analytics to improve their profits and keep customers coming back. Though the house wins most of the time, players typically need to win enough to enjoy themselves and keep playing. Otherwise, players may lose interest and stop coming back. By tracking players spending, casinos can learn which customers they make the most money from. They can offer greater incentives to these big spenders to keep them coming back. The collected data also helps these resorts understand which amenities are most popular.

There are data analytics tools that can be used in business analytics to streamline the big data pipeline.

Tools for use in business analytics range substantially in complexity. Self-service analytics tools provide a simplified interface, often are paid services that can do basic data analytics tasks in a user-friendly way. Alternatively, advanced statistical analysis tools require programming and software engineering skills to use effectively. Many of these tools are open-source and available for free to users.

The most well-known tools in both data analytics and business analytics are open source programming languages that provide statistical tools. Two widely used options are R and Python (with the pandas library). Any data processing or analysis task can be automated using these languages. R and Python both have large communities that provide support and many packages and libraries which provide added functionality and statistical methods. These include data visualization tools, advanced statistical algorithms, data scraping tools and much more.

There are also paid statistical programming languages. These include SAS, SPSS and MATLAB. These languages have the advantage of paid support and professional development. However, they are typically used less often than open source solutions.

Not all statistical analysis tools require programming. There are many options for statistical analysis with a graphical user interface (GUI). These tools are generally paid and include Tableau, Qlik, Sisense and SAP. These are self-service analytics tools that can take raw data and turn it into user-friendly charts with the click of a button. This user-friendly workflow allows the most useful insights to quickly be visualized.

Selecting the right tool involves balancing financial costs, time costs, the complexity of the data and the ease of use.

Business analytics can help provide a wide array of benefits

Business analytics provides a way for businesses to plan for the future. By modeling the trends in a businesses’ sales, profits and other key metrics, these indicators can be projected into the future. Understanding the changes that are likely to occur seasonally, annually or on any scale allow businesses to better prepare. This may mean decreasing spending in preparation for a slow season or investing in new marketing campaigns to compensate. Large suppliers can use this data to predict order volume and minimize waste in their warehouses. Planning for future events provides a huge advantage to all businesses.

Business analytics can also enable new types of marketing campaigns. The data collected by businesses give insights into customer behavior which helps businesses understand the effectiveness of advertising campaigns with different audiences. Targeting audiences that are more likely to respond to specific campaigns or products increases efficiency overall. In addition, understanding consumer habits can help businesses improve customer retention. By identifying customers who are less likely to return, businesses can offer targeted promotions. This provides a cost-effective way to gain customer loyalty.

The applications of business analytics are wide-ranging and the benefits of business analytics are clear – data-driven businesses have a competitive edge in almost every industry.

Last updated: April 2022