Embedded Analytics

Chapter 9. Data Science, Machine Learning, and Embedded Analytics

In this chapter, we’re going to explore the intersection between embedded analytics, data science, and machine learning (ML). This chapter is aimed particularly at decision-makers considering the application of data science and machine learning to provide additional value to existing business processes and embedded applications. We’ll also make the case that context and “storytelling” around data science and machine learning is arguably just as important as the application of the processes themselves.

This is a complex topic, so for the sake of simplicity, we’re going to intentionally blur the boundaries between the specific fields of data science and machine learning and refer generally instead to DSML. In the real world, many vendor tools, platforms, and methodologies use features from both disciplines, and against the backdrop of general analytics, it’s safe to say these terms (and many others, like artificial intelligence and predictive analytics) are often used interchangeably.

DSML in Practice

So, what does DSML encapsulate beyond the traditional analytics we’ve discussed so far? Typically, a mature organization where data analytics is well practiced may demonstrate capabilities in one or more of the following areas:

• The active use of sophisticated statistical or probability-based methods to enrich or derive insight from data and support decision making

• The use of languages like Python and R for transformation, ...