Key Takeaways

Kevin Petrie, Wayne Eckerson, and Laura Sebastian-Coleman summarized their takeaways from the dynamic discussion during the CDO TechVent on Data Observability on August 18, 2022.

Data Observability takeaways


People and process matter.

While data observability products continue to add depth and breadth, they won’t solve all your problems. Data teams must pay equal attention to people and process; for example, to ensure data observability tools inform their data quality standards and policies.


Observe your costs.

FinOps monitoring and optimization are a top use case for data observability. Enterprises should prioritize tools that can help them predict and control operating expenses related to the consumption of cloud compute.


An ounce of prevention is worth a pound of cure.

Ben Franklin’s maxim holds true here as well. The more data teams can spot risk, then predict and prevent issues, the better they can avoid angry customers and costly disruptions. They should observe data as close to the source as possible.


Plan. Then plan some more.

Enterprises must carefully plan out their data observability program. A well-designed program is ambitious in its scope, but incremental in its deployment to ensure progress. It also maintains a ruthless focus on the business problem it needs to solve.


Don’t oversimplify your implementation.

Modern enterprises have complex environments and business requirements. Data teams should keep an open mind about where data observability fits—e.g., as part of a pure play tool or multi modal tool—and ensure their various elements are as interoperable as Lego pieces.