Chapter 6. Governance of Data in Flight

Data, especially data used for insights via data analytics, is a “living” medium. As data gets collected from multiple sources, it is reshaped, transformed, and molded into various patterns for different use cases: from a standardized “transactions table” to allow for forecasting the next season’s business demand, to a dashboard presenting the past yield of a new crop, and more.

Data governance should be consistent across these transformations and allow more efficiency and frictionless security. Data governance should not introduce labor by forcing users to register and annotate new data containers as they work to reshape and collect data for their needs.

This chapter will discuss possible techniques and tools to enable seamless data governance through analysis of data “in flight.”

Data Transformations

There are different ways to transform data, all of which impact governance, and we should be aware of these before we dig in deeper. It is common to refer to these processes as extract-transform-load (ETL). This is a generic phrase used to indicate the various stages of moving data between systems.

Extracting data means retrieving it from the source system in which it is stored, e.g., a legacy DB, a file, or the results a web crawler operation. Data extraction is a separate step, as the act of extracting data is a time-consuming retrieval process. It is advantageous to consider the extraction phase as the first step in a pipeline, allowing ...

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