A Definition of Business Analytics
Business Analytics is “the study of data through statistical and operations analysis, the formation of predictive models, application of optimization techniques, and the communication of these results to customers, business partners, and college executives.” Business Analytics requires quantitative methods and evidence-based data for business modeling and decision making; as such, Business Analytics requires the use of Big Data.
Big Data: An Overview
SAS describes Big Data as “a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis.” What’s important to keep in mind about Big Data is that the amount of data is not as important to an organization as the analytics that accompany it. When companies analyze Big Data, they are using Business Analytics to get the insights required for making better business decisions and strategic moves.
Benefits of Data-Driven Decision Making with Business Analytics
Companies use Business Analytics (BA) to make data-driven decisions. The insight gained by BA enables these companies to automate and optimize their business processes. In fact, data-driven companies that utilize Business Analytics achieve a competitive advantage because they are able to use the insights to:
- Conduct data mining (explore data to find new patterns and relationships)
- Complete statistical analysis and quantitative analysis to explain why certain results occur
- Test previous decisions using A/B testing and multivariate testing
- Make use of predictive modeling and predictive analytics to forecast future results
Business Analytics also provides support for companies in the process of making proactive tactical decisions, and BA makes it possible for those companies to automate decision making in order to support real-time responses.
The Differences Between Business Intelligence and Business Analytics
Business Intelligence (BI) and Business Analytics are similar, though they are not exactly the same. Business Intelligence involves the process of collecting data from all sources and preparing it for Business Analytics. Business Intelligence is more of a first step for companies to take when they need the ability to make data-driven decisions. Business Analytics, on the other hand, is the analysis of the answers provided by Business Intelligence. While Business Intelligence answers what happened, Business Analytics answers why it happened and whether it will happen again. Business Intelligence includes reporting, automated monitoring and alerting, dashboards, scorecards, and ad hoc query; Business Analytics, in contrast, includes statistical and quantitative analysis, data mining, predictive modeling, and multivariate testing.
Challenges with Business Analytics
Penn State University’s John Jordan described the challenges with Business Analytics: there is “a greater potential for privacy invasion, greater financial exposure in fast-moving markets, greater potential for mistaking noise for true insight, and a greater risk of spending lots of money and time chasing poorly defined problems or opportunities.” Other challenges with developing and implementing Business Analytics include…
- Executive Ownership – Business Analytics requires buy-in from senior leadership and a clear corporate strategy for integrating predictive models
- IT Involvement – Technology infrastructure and tools must be able to handle the data and Business Analytics processes
- Available Production Data vs. Cleansed Modeling Data – Watch for technology infrastructure that restrict available data for historical modeling, and know the difference between historical data for model development and real-time data in production
- Project Management Office (PMO) – The correct project management structure must be in place in order to implement predictive models and adopt an agile approach
- End user Involvement and Buy-In – End users should be involved in adopting Business Analytics and have a stake in the predictive model
- Change Management – Organizations should be prepared for the changes that Business Analytics bring to current business and technology operations
- Explainability vs. the “Perfect Lift” – Balance building precise statistical models with being able to explain the model and how it will produce results
Business Analytics Best Practices
Adopting and implementing Business Analytics is not something a company can do overnight. But, if a company follows some best practices for Business Analytics, they will get the levels of insight they seek and become more competitive and successful. We list some of the most important best practices for Business Analytics here, though your organization will need to determine which best practices are most fitting for your needs.
- Know the objective for using Business Analytics. Define your business use case and the goal ahead of time.
- Define your criteria for success and failure.
- Select your methodology and be sure you know the data and relevant internal and external factors
- Validate models using your predefined success and failure criteria
Business Analytics is critical for remaining competitive and achieving success. When you get BA best practices in place and get buy-in from all stakeholders, your organization will benefit from data-driven decision making.
For further information on Business Analytics, check out the posts below: