This chapter covers what to do after you have collected data. It explains common data preparation methods, such as parsing and aggregation, and discusses how helpful it can be to visualize data.
In this chapter, you have seen that data by itself has little value, but insights derived from high-quality data can be invaluable when it comes to making decisions. In particular, this chapter highlights alarms, configuration drift, and AI/ML techniques.
Finally, this chapter presents real case studies of data automation solutions put in place to improve business outcomes.
Chapter 4 covers Ansible. You have already seen a few examples of what you can accomplish with Ansible, and after you read Chapter 4, you will understand the components of this tool and how you can use it. Be sure to come back and revisit the examples in this chapter after you master the Ansible concepts in Chapters 4 and 5.