Chapter 9. Data Quality in the Real World: Conversations and Case Studies
Itâs great to talk about data quality in theory, but what does this desired state actually look like in practice?
Over the past several chapters, weâve walked through what it takes to achieve data reliability at scale, from how to design a DataOps workflow to common SQL tests to determine the volume and freshness of your data assets. Weâve sprinkled in a dose of real-world case studies, but as we all know, data quality isnât achieved in a textbook, and getting to âreliable dataâ depends on several other elements of your data analytics and engineering practice. As technologies advance and companies become more data-reliant, we need to consider how other industry-defining processes and technologies affect our ability to increase data reliability.
In this chapter, weâll discuss five topics that are top of mind for many of todayâs data leaders and share how data quality plays a critical part:
The data mesh and where data quality fits in
Data qualityâs role in the cloud-based data stack journey
Knowledge graphs as the key to more accessible data
Data discovery for distributed data architectures
When to get started with data quality
Over the past several years, these five topics, technologies, and trends have become increasingly common, often giving organizations the advantage necessary to tackle data reliability in a more scalable and repeatable way. Letâs dive in.