Eckerson Group offers a framework for categorizing data observability tools according to their requirements and their expectations about the role that data observability plays in their environments. The categories in this framework vary by product focus (i.e., data quality vs. data quality + pipeline on the horizontal axis) and vendor focus (i.e., pure play data observability vs. multi modal offering on the vertical axis.)
Product Framework
Create a shortlist from our product framework.
Eckerson Group places data observability vendors into the following categories, with arrows that illustrate ongoing shifts in enterprise requirements and vendor offerings. While the list is not exhaustive, it underscores some key market trends.
- Pure play data quality. This category comprises startups that command significant venture capital and press attention. They focus on helping enterprises improve data quality in cloud-centric environments. Eckerson Group expects these vendors to move upward on the framework as they extend into data management through partnerships and internal development.
- Multi modal data quality. Vendors in this category tend to have broader, more mature portfolios that include offerings such as a data catalog or data fabric. They include data quality observability features as part of data management.
- Data quality + pipeline. Some of the vendors in this category started with a focus on data pipeline performance and reliability, then added features for data quality observability. Others addressed both data quality and pipeline performance from the start. These vendors continue to move upward as they address data management through partnerships and internal development.
- Multi modal data quality + pipeline. This final category comprises multi modal vendors that focus on DataOps and data pipeline management. They offer data observability to improve data quality and pipeline performance.
