Business analytics utilizes data and quantitative methods to derive meaning, driving more informed business decision-making and change. This process is iterative, and the goal of the tools, analytics, and practice is to anticipate and predict future business outcomes.
Business analytics isn’t exactly new. The field first originated in the early 1900s when Frederick Winslow Taylor developed the concept of business management as a scientific discipline.
From there, innovators like Henry Ford practiced what is now known as modern business analytics when measuring the time of his assembly lines. Fast forward to the rise of the computer, rapid expansion of software engineering, and big data and analytics have become a key focus of organizations, both big and small.
Why is Business Analytics Important?
By 2025, according to The World Economic Forum, it’s estimated that 463 exabytes of data will be created daily. With so much data being collected throughout an organization through the tools and technology being used, the challenge is often less about finding the data and more about how to interpret, learn, and iterate as a result of the findings. That’s where business analytics comes in.
Regardless of the organization’s size, business analytics can maximize efficiency, save costs, increase productivity, and improve decision-making.
An example of business analytics in use at a large scale is Amazon’s anticipatory shipping, a concept that emerged as far back as 2014. By combining a massive amount of past purchasing data, consumer behavior, and predictive analytics, Amazon aims to reduce the delivery time between ordering and receiving the items you buy from them. It seeks to do so by utilizing these predictions to then re-distribute products to local distribution centers. Once customers purchase, the products are closer to their end location, reducing overall shipping costs and improving customer satisfaction.
While the benefits of business analytics are often visible across many large corporations we interact with daily, they can be just as if not more impactful on a smaller scale. For example, a local brewery that launched its product online may have disparate reports across multiple platforms, preventing them from having a unified, clear picture of their operations and efforts.
Business analysts can identify this pain point and help transform their workflows and processes to integrate this data into a single source. That will enable the brewery to take action and implement changes to how they’re marketing or distributing their products based on current and predicted future metrics.
Comparing Business Analytics to Business Intelligence or Data Analytics
Business analytics, business intelligence, and data analytics are often used interchangeably, but you may be wondering, is there a difference? While there is a great deal of overlap across these terms, slight differentiating factors define each individually.
A useful comparison of business analytics and business intelligence was outlined on SelectHub through a side-by-side comparison of business analytics and business intelligence functionalities.
Both utilize historical data. However, the main driver that often differentiates the two is how the data gets to actionable outcomes.
Business analytics often uses statistical analysis, data mining, and quantitative analysis to predict future outcomes. In contrast, business intelligence provides reporting and insights into the present circumstances and what is happening now. Predictive analytics is often a key focus of business analytics, while descriptive analytics is primarily business intelligence. Business intelligence allows businesses to answer what or how, while business analytics often answer why.
Another helpful explanation of the differentiation between business intelligence, business analytics, and data analytics was outlined by Tableau, a leading analytics platform. What differentiates data analytics from business analytics is the level of depth and the type of insights that are derived from the process.
Data analytics is the term used as the overarching umbrella that business analytics falls under. Data analytics doesn’t only apply to business and can be used for other applications beyond operational analytics that often arise from business analytics practices.
Real-World Applications of Business Analytics
Organizations big and small can employ business analytics in various ways. Below are just a few examples of what types of organizations and which outcomes can be achieved by utilizing business analytics.
Amazon, a company using big data and analytics at some of the largest scales, is utilizing business analytics to identify buyer fraud on its platform. By collecting more than 2,000 data points on every order, they use machine learning algorithms to predict and identify transactions that have a high probability of being fraudulent, saving millions of dollars per year.
Blue Apron forecasts customer demand by deploying a predictive data-driven model to estimate future revenue accurately, waste less food and optimize shipping costs. Over time, the meal kit delivery service has accumulated substantial purchasing activity and consumer behavior data. They’ve transformed and cleaned this data, built models (linear regression and random forest), and now actively use these models to predict recipe and order level demand.
Nissan gained a deeper understanding of product preferences and transformed these insights into actionable marketing decisions through Google Analytics. Through the Google Analytics platform and tracking tags, the company could re-distribute access to these insights for its global marketing teams and enable real-time decision-making for local marketing campaigns based on consumer preferences.
Force Therapeutics, a patient engagement platform and research network, used business analytics via the business intelligence software Looker to help reduce the total cost of patient care by an average of 20 to 30 percent. By incorporating various process data into a single platform, the organization could standardize its clinical processes and continue to optimize them over time through continued tracking. When able to access data in real-time and utilize previous data to preemptively identify potential missteps in a patient’s pre and post-op care, the organization can reduce unplanned care costs while improving and delivering high-quality care to their patients.
Benefits of Utilizing Business Analytics
Through a variety of business analytic tools, including data mining, statistical analytics, and predictive modeling, to name a few, a variety of benefits can be derived from organizations investing in these tools.
HBS highlights that some of the main benefits of utilizing business analytics can be improved financial performance, increased efficiency, and better-informed decision making. Accessing data and identifying historical trends is only part of the process. The actual value is derived when transforming it and deriving key insights that can then be applied to drive change.
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