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Top 12 data observability use cases

Experts identify 12 top data observability use cases and examine how they influence all aspects of data management and governance operations.

Data observability is an investment worth considering for any organization looking to improve data quality, management and governance.

The construction and improvement of data infrastructure is a top priority for enterprise executives. In the 2023 Data and Analytics Leadership Executive Survey from NewVantage Partners, 83.9% of responding executives said their organizations plan to increase investment in data and analytics this year .

As organizations allocate more resources for data and analytics operations, executives should consider funding the implementation of data observability. Data observability is a growing element of data infrastructure gaining popularity among enterprises, according to industry experts. Data teams can apply data observability tools in a variety of use cases across their data management program to increase data quality, accuracy and efficiency.

What is data observability?

Data observability is a process and set of practices data teams use to understand the health of their organization's data and data environment.

"It's about gaining insights, gaining visibility into quality, behavior and performance," said Chris Dyck, former research lead for data and analytics and enterprise architecture at Info-Tech Research Group. "What you're aiming for is reliability, accuracy and consistency, as well as enabling an organization to make informed decisions on trustworthy data."

Data observability helps ensure a strong data ecosystem. Data teams use it to monitor the quality, reliability and delivery of data, and to identify issues.

Data observability use cases

Extracting the most value from data observability depends on the situation. Data teams have the most knowledge of their data operations and should manage how to apply data observability for the best results.

1. Improve stability

As organizations grow their IT infrastructure and cloud architecture, their data pipelines expand. As pipelines expand, they can become unstable, said Jason Medd, director analyst with the data management team at research firm Gartner.

IT architecture is often based on assumptions -- which aren't always accurate -- of how users work with the data and how the data will behave in those interactions. A data observability practice can help organizations "see where the wheels wobble, and if you see the wheels that wobble, you can see what's going on and fix it. That can really minimize downtime," Medd said.

2. Build effective data pipelines

Teams can use data observability as they design, build and expand their data infrastructure to create more effective and stable pipelines, said Sripathi Jagannathan, head of data engineering at UST, a digital transformation services company.

For example, teams can put in controls to make sure data meets required criteria as they're building a new data pipeline. Granted, that's something they can do even if they're not using data observability, but they're more likely to know and implement the criteria if it's expected as part of an established framework within their data management and governance program.

3. Run simulations to plan capacity

Data observability helps data teams, architects and engineers more accurately predict the capacity an organization needs as it expands its use of data. They can simulate environments to see the effects of a particular change.

"Sometimes you want to see what that change will do to the rest of the pipeline before you actually implement it," Dyck said.

4. Ensure data quality

Data observability helps teams ensure data pipeline performance, as well as the quality of the data within the pipeline. Data observability supports data teams in their work to evaluate the accuracy, completeness and reliability of data, said Diane Gutiw, vice president of data analytics and AI at CGI, an IT and business consulting services firm.

Data observability can also help teams identify gaps that could affect insights gleaned from the data, which allows data teams to deliver better quality data programs, she said.

5. Address data drift

Data observability can help identify and catch data drift before it affects the business, said Mano Mannoochahr, senior vice president and chief data and analytics officer at Travelers Insurance.

Drift refers to incremental changes in the data used to train algorithms and machine learning models over time. Undetected drift can lead to faulty performance of the intelligent systems trained on the data.

6. Tune for performance

Monitoring tools can give data teams insight into when and where data is not flowing as it should through the pipelines. Data observability can readily deliver information on performance issues such as bottlenecks and slowdowns. Teams can use the information to tune their data pipelines and improve performance.

[Data observability] helps us better understand the gaps, identify opportunities that we need to evolve and identify the solutions we need.
Mano MannoochahrSenior vice president and chief data and analytics officer, Travelers Insurance

7. Trace data lineage

A key part of good data management and data quality controls is knowing the data's lineage -- its origin, history and movement in the enterprise over time -- from creation through storage and use. Data teams can use data observability to help understand and document data lineage.

They can use it to identify schema changes and other factors within the data's provenance that could negatively impact data quality or the organization's use of the data to inform decisions.

8. Identify problems

Data observability helps data teams quickly undertake a root cause analysis when they do encounter problems, Medd said. It gives teams insight into the myriad variables that may be creating problems, and it provides information about the variables. Teams can quickly identify any anomalies causing issues.

"It can help identify something that might be broken or something that needs to be addressed; for example, something that's out of bounds of the parameters that have been set," Mannoochahr said.

9. Enable continuous improvement

As data observability delivers more insight into the health of the data and data pipelines, it also gives data teams the ability to track and measure improvements over time.

"It helps us better understand the gaps, identify opportunities that we need to evolve and identify the solutions we need," Mannoochahr said.

10. Build data trust

The visibility that data observability provides can help prove the data accurate, fresh and complete for the context it's used for, Gutiw said.

Decision-makers need to trust the data used in the decision-making process. Ensuring accurate, fresh and complete data builds the trust decision-makers have in the data and empowers them to make decisions with it.

11. Support regulatory compliance

In a related use case, organizations can use data observability to support their regulatory compliance efforts, Jagannathan said. Data observability helps organizations prove to regulators their submitted data is accurate and complete.

"When organizations submit [data to regulators], they have to show rules for handling the data, what transformations have happened, how it has moved through the pipelines, etc. Data observability captures all that," he said.

12. Create efficiencies

Data observability drives efficiency and lowers costs. The insights some data observability tools offer allow organizations to more thoroughly understand their technology use and related spend, Medd said.

Data teams implement data observability to help improve data pipeline management. For data leaders, data observability is ultimately about improving business performance.

Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.

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