Implement data observability and restore trust in data
Many organisations fail their data-driven journey because of data downtime: the period when data is missing, incomplete, inaccurate or incorrect. Data observability is the practice that assures data quality and brings stakeholders’ trust back to data.
Data downtime breeds distrust in data
Broken dashboards, wrong KPIs, and angry stakeholders: these are all-too-familiar nightmares if you ever worked as a data engineer/analyst. What makes it worse is that you are not even the first to know. When such blunders occur too frequently, stakeholders start losing trust in data.
Resolving data issues has never been harder
- Data pipelines are growing in scale. Identifying inter-dependencies soon becomes mission-impossible as the number of data models grows.
- The modern data stack is growing in complexity. Diagnosing silent data issues means navigating among diverse technologies and tools.
- Data issues like schema updates and data drift are usually silent. They are like landmines in your data pipeline, and can spread a fire if no action is taken in time.
Data observability resolves data issues before they start a fire
“The biggest bottleneck used to be data availability. Now it’s the quality
and useability of data.”
Components of data observability
- Cataloging: a data catalog allows users to search for datasets that match their business needs. Data lineages should be documented for quick root cause analysis.
- Testing: testing checks whether the data that enters or leaves the data pipeline meet your expectations. It avoids unexpected changes to your datasets during development.
- Monitoring: monitoring means regularly checking the quality of your datasets. It is performed by continually collecting metadata and calculating quality-related metrics, in order to alert users on data quality issues.
- Profiling: data profiling is the process of examining, analysing and visualising your data to create useful summaries. It can help users better utilise the data.
Data observability explained
- Data observability ensures users can find, understand and know the quality of data.
- It improves data quality, and ensures the health of the whole data platform.
- Just like DevOps leverages system outputs (logs, metrics and traces) to prevent software downtime, data professionals can apply the same principle to fight data downtime.
- It tells you how and why a problem occurs, and where the origin is. It provides detailed diagnosis to help you resolve data issues.
- It is proactive. Data scans alert you about possible data issues (anomaly detection).
Start your data observability journey now
How can we help?
Don't let data quality become a barrier in creating value from your data assets. GoDataDriven can help implementing data observability depending on your organisation's data maturity.
Our Analytics Engineers have experience in implementing data observability solutions to organisations of all sizes.
GoDataDriven consultants are savvy in modern data stack and actively contribute to open-source initiatives.
“What I like about working with GoDataDriven, is that I don’t have to worry about evaluating people’s skillset. I know you have already done that. They went through the process, and you made sure that everything is of high quality. Knowing you can depend on that is very reassuring to me.”
“Our customers are, in general, technologically advanced companies that recognize the advantages of having a cloud platform. They understand it doesn’t compromise security but, on the contrary, increases it and allows a solution to be accessible everywhere without complex and expensive on-premises infrastructures.”
“I’m curious to see what our data-driven future will look like. By using data, we now know how to stay relevant for individual visitors. I’d like to go beyond predicting theater occupancy and introduce smart applications to optimize the number of visitors. But that’s not all. Data also helps us better understand the member life cycle and retain members.” – Rick Stammes, Business Analyst at Pathé.
Are you interested in Data Observability?
Contact Bram Ochsendorf (lead data scientist at GoDataDriven) to learn more about fighting data downtime and create value from data.