In this chapter, we reviewed how to increase data scientist productivity by reducing time spent manipulating data. The concept of LAD was introduced, along with a process to build them.

Concepts to help prevent data lakes from turning into a data swamp was discussed. Changing perspectives from a data lake to a series of active refineries and production chains helps to plan for a useful data storage area.

Strategies for data retention were also reviewed. Consider the twin goals of maximizing value while minimizing costs when designing a retention strategy. In the next chapter, we will discuss the economics of IoT analytics. The chapter covers ways to look for business value in the data along with a detailed predictive maintenance example.