Understanding Business Analytics for the Enterprise
Business analytics encompasses the tools, processes, and knowledge used to derive insights and recommendations from raw data. Applying business analytics helps peel back layers of information to reveal the trends, opportunities, and risks facing an organization. Many enterprise businesses collect large amounts of data from first-, second-, and third-party sources; business analytics puts it to work.
Business analysts and statisticians have found many uses for business analytics programs, but most functions fall into these four categories:
- Anticipatory analytics: Building on the foundation of predictive analytics, anticipatory can identify and adjust predictions based on inflection points such as the acceleration and deceleration of certain business behaviors or sudden change in business direction. Anticipatory analytics helps to anticipate the future needs of a business before they show obvious signs in their respective opportunity/risk profile.
- Predictive analytics: Algorithms use real-world data to help businesses understand the most probable outcome of a given action.
- Descriptive analytics: Computing power utilizes current performance metrics to analyze why a specific outcome occurred.
- Prescriptive analytics: Analytics applications simulate how variables may affect outcomes, providing guidance on the best course of action.
Before we take a deeper look at use-cases for each type of analysis, it’s important to consider the wider data ecosystem.
Business Intelligence vs. Business Analytics
Business intelligence is concerned with data management, including which events to track, where to find information, how frequently to refresh databases, and so on. It’s the job of the business intelligence analyst to approach data collection in a logical manner that provides end-users with the insights they need.
Business analytics relies upon this data to reach informed conclusions. In this way, the two disciplines work hand-in-hand to help companies better understand the data they collect.
Business Analytics in Action
Examples are helpful in differentiating among the three main types of business analytics. The following hypothetical situations illustrate how each type of analysis can be used:
Predictive Analytics Use-case: Increasing Sales
Salespeople want to reach prospects predisposed to purchasing their products. Predictive analytics can help identify patterns that suggest a particular person is primed to make a purchase.
Here’s a simple example. An apparel wholesaler wants to improve sales of winter gloves. A predictive analytics program could compare a list of existing customers and leads against third-party data, augmenting records with details about the businesses. Certain events, such as an expansion in the number of retail locations, may indicate that a particular prospect is interested in making a purchase.
Rather than working their way down a long list of prospects, salespeople can prioritize outreach efforts.
Descriptive Analytics Use-case: Understanding Online Engagement
Many analysts and marketers spend their days trying to understand why sales numbers improved or took a hit. Descriptive analytics put the most recent data into context.
An e-commerce website may report information on daily sales totals to managers. The rise-and-fall in earnings is in itself an example of descriptive analytics, but they can also be used to understand a trend.
A sharp-eyed employee may notice that no one has purchased a certain product for several days. Digging deeper, they discover that visitors have been abandoning the product page after only two seconds. The reason? It’s displaying an error message instead of product information. In this case, descriptive analytics identified a challenge and provided the evidence needed to overcome it.
Prescriptive Analytics Use-case: Analyzing Staffing Levels
Employers need to strike a balance when hiring. There must be enough staff to keep the business running smoothly, but too many employees can be a drain on resources. Prescriptive analytics use algorithms and machine learning to “war-game” various scenarios.
A call center may handle thousands of customer inquiries every day. However, call volume is inconsistent. It varies by time of day, day of the week, and season. When this data is run against worker productivity figures, a prescriptive analytics program could show the likely outcomes of different staffing levels.
Prescriptive and predictive analytics overlap somewhat, but there is a distinction: prescriptive analytics programs provide a recommendation based upon multiple scenarios.
Choosing a Business Analytics Solution
Due to the broad range of useful applications, there is no one-size-fits-all business analytics tool. However, there are several important factors to consider when searching for a business analytics solution:
- What are you trying to analyze? A marketer interested in tracking the performance of email newsletters has different needs than a procurement professional trying to mitigate the risk of supply chain disruption. Start by defining your needs, and then search for products designed to handle those concerns.
- How user-friendly is the business analytics tool? The most valuable resource is the one employees utilize. If you have a dedicated staff of analysts who understand the finer points of data science, this may not be a concern. However, your average account manager may need a more intuitive program in order to make use of data.
- What data informs the assumptions made by the program? Predictive and prescriptive business analytics tools rely on inputs when modeling outcomes. Make sure that this data comes from a reliable source and is refreshed on a regular basis. There’s no use basing decisions on outdated figures.
Business analytics is already having a profound effect on how people make decisions. Understanding these processes is the first step to integrating them into your daily workflow.
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