In simple terms, Business Analytics is the learning 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 is nothing but the humongous volume of structured and unstructured data that gets accumulated in a business on a daily basis. Rather than giving importance to the chunk of data, the firms should focus on the analytics that accompanies it. In order make better business decisions and strategic moves, the firms, while analyzing Big Data, use Business Analytics to get better insights.
Basically, the business houses use BA in order to make data-driven decisions and enabling these firms to automate and optimize their business processes. The advantages of the data-driven firms that utilize BA, achieve a competitive advantage because these companies 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 modelling and predictive analytics to forecast future results. Business Analytics also provides support for firms 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.
According to John Jordan of Penn State University, 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.”
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 versus Cleansed Modelling Data – Watch for technology infrastructure that restricts available data for historical modeling and knows 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 versus the “Perfect Lift”: Balance building precise statistical models with being able to explain the model and how it will produce results
Adopting and implementing Business Analytics is not something an entity can do overnight. But, if a firm follows some best practices for Business Analytics, they will get the levels of insight they seek and become more competitive and successful.
Knowing the objective for using Business Analytics
Define your business use case and the goal ahead of time, while defining your criteria for success and failure. Select your methodology and be sure you know the data and relevant internal and external factors, care needs to be taken to validate models using your predefined success and failure criteria. Business Analytics is crucial for remaining competitive and achieving success. When you get BA best practices in place and get buy-in from all stakeholders, your company will benefit from data-driven decision making.
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