Chapter 3. Dawn of the Real-Time Dashboard

Before delving further into the systems and techniques that power predictive analytics applications, human consumption of analytics merits further discussion. Although this book focuses largely on applications using machine learning models to make decisions autonomously, we cannot forget that it is ultimately humans designing, building, evaluating, and maintaining these applications. In fact, the emergence of this type of application only increases the need for trained data scientists capable of understanding, interpreting, and communicating how and how well a predictive analytics application works.

Moreover, despite this book’s emphasis on operational applications, more traditional human-centric, report-oriented analytics will not go away. If anything, its value will only increase as data processing technology improves, enabling faster and more sophisticated reporting. Improvements like reduced Extract, Transform, and Load (ETL) latency and faster query execution empowers data scientists and increases the impact they can have in an organization.

Data visualization is arguably the single most powerful method for enabling humans to understand and spot patterns in a dataset. No one can look at a spreadsheet with thousands or millions of rows and make sense of it. Even the results of a database query, meant to summarize characteristics of the dataset through aggregation, can be difficult to parse when it is just lines and lines of numbers. ...

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