This chapter is from the book
This chapter is from the book
This chapter is from the book
Implementing Business Analytics
As organizations are becoming more competitive by using business analytics, there is no doubt that practitioners, from both large and small companies, are eager to learn more about data analytics and how to implement it in their everyday decision making. But how do managers implement business analytics in the workplace? Is there a methodology or a recommended set of steps that can be followed by practitioners? The examples of Target, LinkedIn, First Tennessee, and many other companies that have implemented data analytics can be used to derive practical steps for implementing analytics in organizational settings. Business analytics initiatives in the era of Big Data usually follow an eight-step cycle:
- Understand the company’s products in depth.
- Establish tracking mechanisms to retrieve the data about the products.
- Deploy good-quality data throughout the enterprise.
- Apply real-time analysis to the data.
- Use business intelligence to standardize reporting.
- Use more advanced analytics functions to discover important patterns.
- Obtain insights to extract relevant knowledge from the patterns.
- Make decisions to derive value using the knowledge discovered.
These eight steps illustrate how organizations utilize all aspects of data analytics. LinkedIn, for example, uses information about its members, which is housed in operational databases. This information is then organized into a data warehouse and the analysts can use this information to explore the browsing history of LinkedIn members. Furthermore, LinkedIn uses descriptive statistics to generate reports and discover patterns. These patterns let the data analytical team at LinkedIn use predictive analytics to discover that speed is very important in receiving positive responses. Specifically, LinkedIn analysts were able to determine that “adaptation exponentially increases as the response time goes towards sub-seconds.” Finally, prescriptive analytics is used to generate appropriate actions. For example, LinkedIn could use optimization techniques to identify the best mix of companies or individuals, which maximizes the number of prospects or product sales.