7 Aspects of Business Analytics All Managers Should Understand
Last Updated May 29, 2019
Business analytics is no longer the future of business. It is the present.
The use of data has spread to almost every industry. And data is no longer relegated to just the statistical analysis department. Uses of data range from the marketing department trying to target the right consumer demographic to manufacturers streamlining operations on the factory floor.
Business analytics has also become an important tool for project managers in developing detailed project plans and is key for financial departments in planning how to spend and invest money.
Certain aspects of analytics should be part of every manager’s education. Even for those who don’t work directly every day with analytics, the strategy behind crucial techniques should be thoroughly understood.
They include the seven following data analysis concepts.
Correlation Analysis
This technique allows for the comparison of two separate variables to determine if there is a relationship between them. Analysts typically use this approach when business leaders suspect that two variables work together in some way. Correlation analysis offers a way to test the assumption. An example might be testing whether the page through which people enter a website leads to more actual purchases of a product or service.
Data Mining
This term covers much of what analysts do with large data sets, often referred to as Big Data. Using sophisticated statistical analysis, they research large amounts of data looking for patterns and trends that can prove useful to a business. They also look for correlations between two variables. The key here is providing actionable recommendations based on large amounts of information. An example includes searching health records from a certain large patient demographics to determine steps that can be taken to improve health outcomes.
Experimental Design
In this area, analytics are used to test the validity of a strategic business plan. That can include a hypothesis, new packaging for a product or a strategic marketing plan. This typically involves making changes in one department within an organization and comparing it to a “control” – a department where the change wasn’t made. This approach can determine whether the change improved productivity, saved money or enhanced the quality of a product or service.
Factor Analysis
This technique is typically put into play when comparing a large number of variables, rather than just two. The statistical approaches used within factor analysis can help reduce the number of those variables, resulting in a reduction in the amount of data needed. An example comes from baseball, a statistically rich sport that increasingly uses data analytics to develop an in-game strategy. For example, a baseball team might take a large dataset of thousands of individual plays and determine that just a handful of factors typically determine the outcome.
Linear Optimization
This technique aids organizations in reducing the information from large data sets into an actionable series of steps. Using a linear mathematical model, analysts determine the best outcome possible based on a set of constraints. In business, those constraints can include manpower, time, money and materials. Linear optimizations help determine the best combination of those factors to reach the best possible outcome – typically increased profit at a reduced cost.
Meta-Analysis
Sometimes in business analytics, it’s best not to reinvent the wheel. Meta-analysis involves the research of studies previously done on an issue. Researchers look to the earlier work, often conducted over decades, to determine trends, find patterns and discover relationships between variables. This approach can save both time and money involved with conducting original statistical research.
Regression Analysis
This technique involves research into whether one variable has a large effect on another. For example, what impact does demand have on price structure? This analysis is the type airlines do on a daily basis to set ticket prices. Using regression analysis to check past data on the effect of one variable on another can help business leaders make accurate decisions in areas such as pricing before the trend becomes apparent to competitors.
These represent some of the key statistical techniques used in data analysis. Managers across an organization should make them a part of their overall education in running their section of a business. It can help not only in understanding data analytics but also in knowing what to ask for when developing a new strategy.
