In an era where technology can enhance or infringe upon your daily life, big data is a hot topic. Whatever side you agree with personally, businesses use big data sources, statistical methods and business analytics to facilitate more successful outcomes for their companies, and to keep up with the global marketplace.
Business Analytics vs. Business Intelligence
- BA uses optimization techniques and predictive modeling to identify and study trends or predictive patterns in historical data. The results are then used by the business to develop strategies and for better decision making.
- BI uses industry information and big data to answer the question of what happened while BA focuses on the 'why' and uses trends to gain insight into expectations for the future.
BA and BI work together to create more robust reporting and data management. Both are used for strategic planning purposes.
Business analytics utilizes statistical analysis and data mining techniques to manage data warehouses. Examples of BA techniques include regression analysis, time series forecasting, conjoint and cluster analysis.
Regression analysis is a statistical method of estimating the relationship among variables. It can use various techniques and determine relationships between a dependent variable and one or more independent variables.
Consider the example of a bank trying to increase its customer base for credit cards. They need to be able to attract new clientele and minimize their risk of default, so they'd like to predict a client credit rating. Data on existing customers' income, education, age, existing debt, and more, would be collected and used to build several models.
Those models help answer questions on how the variables impact each other, which are most important, and which are insignificant. They establish a relationship between the dependent variable (credit rating) and independent variables (the data) that would predict a customer's credit rating to a certain degree of accuracy. The acceptable percentage accuracy varies per industry and project, and it is important to ensure the statistical model used is a reliable one.
Time Series Forecasting
Time series forecasting extrapolates past behavior into the future by looking at data over a specific period. The focus is to utilize what happened in the past to attempt to predict what will occur in the future.
Take stock prices history. By looking at daily closing values for a stock price over a year, a closing price time series could be created. A data scientist might be interested in whether there was any seasonality to the data set to predict peaks and valleys according to retail seasons, time of year, and even time of day.
Those patterns, and other forecasts, help businesses gain insight. They are used to address concerns on risk, develop investment philosophies, and predict future behavior with predictive analytics.
Cluster Analysis is exactly as it sounds. Data points are grouped into clusters based on properties that make them more similar to each other than to the other groups.
An everyday example of this is analyzing data on current customers, grouping them into clusters by income, demographic, or habits, and using that analysis to create marketing campaigns geared towards each target market. Say you discover a large cluster is athletes, and another is mothers. Business are then able to delve deeper into how to address each cluster's specific needs, why the products may be a good fit, and make strategic decisions on how and when to engage.
Who Benefits From BA?
In the era of big data, companies of any size can benefit from using modern business analytics to help make important business decisions.
Large corporations benefit from analyzing big data to strategize, plan, and implement change. It behooves them to have intimate knowledge of customer behaviors, industry and sales trends, and to use predictive modeling capabilities to help optimize customer experience, supply chains and business performance moving forward. Large businesses typically have resources allocated to undertake this effort and maximize profitability.
Smaller businesses can also benefit from using data statistical analysis to gain knowledge that enables them to work 'on' their business versus working 'in' their business. Developing a strategy on how to do so can be as simple or complicated as your resources permit, and data for comparable businesses scenarios may help supplement in instances where data is not substantial enough to draw sound conclusions.
Using BA is not without its challenges, such as determining which data to use, selecting the best way to handle analytics, business ethics with data collection, and how to best apply the insights you've gained, but is a worthwhile venture. There are resources designed to help, including machine learning tools. Regardless of company size, it is never too late to start the process of implementing the impactful analytics strategies that directly result from using business analytics.