#253 Data Mesh Implementation Success Metrics - Data Quality - Mesh Musings 53
Sep 22, 2023
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The podcast discusses the importance of measuring data quality and setting up implementation metrics in Data Mesh. It explores the concept of data quality, measuring trust in data quality, and measuring data quality at the implementation level. It emphasizes the significance of trust and the need for data to be both trustworthy and useful for decision-making. The podcast provides insights on metrics such as time to detect and fix issues, compliance with quality SLAs, incident detection and resolution, and the impact of trust on implementation success.
One important aspect of measuring data quality at the implementation level is tracking compliance with service level agreements (SLAs) and the ability to detect and recover from incidents.
Measuring trust in the data and its impact on decision-making is a crucial factor in assessing data quality at the implementation level.
Deep dives
Measuring Data Quality at the Implementation Level
To measure data quality at the implementation level, it is important to track compliance with quality service level agreements (SLAs) and the ability to detect and recover from incidents. This includes measuring how often data products meet their SLAs and the severity of incidents. Trust is another key factor to consider, measuring how much trust exists in the data and its impact on decision-making. Additionally, the quality of metadata should be assessed, although measuring this can be challenging. Overall, setting up a data quality measurement framework early on and focusing on the key metrics of SLA compliance, incident detection, trust, and metadata quality can help ensure data quality in the implementation.
The Importance of SLA Compliance and Incident Detection
One of the key metrics for measuring data quality at the implementation level is compliance with service level agreements (SLAs). Tracking how often data products meet their agreed-upon SLAs provides insight into their overall quality. Additionally, measuring incident detection and recovery times is crucial. Quick detection of incidents and timely resolution helps maintain data quality and rebuild trust. Monitoring the severity of incidents and analyzing the causes can also provide valuable insights for improvement.
Building Trust and Assessing its Impact on Data Usage
Measuring trust in the data and its impact on decision-making is a vital aspect of data quality assessment. Surveying key stakeholders and business users to understand their level of trust can provide valuable insights. Assessing whether people rely on the data in their day-to-day operations is equally important. By measuring data usage and reliance, organizations can gauge the effectiveness of their data quality efforts and identify areas for improvement.
Considering Metadata Quality
Assessing the quality of metadata is an integral part of measuring data quality at the implementation level. While metadata quality is often more qualitative than quantitative, there are ways to gauge its effectiveness. Ensuring consistent definitions and measurements of metadata elements can help establish a standardized approach. Additionally, providing easy access to metadata information for consumers, regardless of the platform or interface they use, is crucial for understanding the data and building trust. Measuring the effectiveness of metadata delivery can provide insights into the overall quality of the data.
As mentioned the last two times, at the start, it's more important to start measuring something than it is to measure the right things. Do NOT let analysis paralysis hold you back. Start measuring early to figure out what actually matters and that will also change over time.
Similarly, your success metric measurement framework will probably suck to start. Oh well, get to measuring.
Use fitness functions. Episode #95 with Dave Colls covers a lot on this.
Data mesh really is a journey and your success measurement will be too. You will need to find small and simple ways to measure. Don't get bogged down. Your measurements will be rough and kinda depressing with the amount of challenges to tackle at the start. Just understand this is about how well you are doing, not how complete you are - there is always more to do!
Reflect back on how far you've come, we often forget to do that!
When it comes to data quality measurement at the implementation level, you need to think about what are you trying to accomplish. Many people go down the wrong path of trying to measure quality in a vacuum. It's about what are the expectations and why do we care about quality - to improve our decision making around data and to improve trust so more people feel they can rely on data. It's that simple. Now, measuring how well you are achieving those gets a bit harder… :D
So, what to measure or consider how to measure regarding data quality at the implementation level: how often are people in compliance with their quality SLAs, whatever those SLAs may be? How quickly are you detecting and resolving/recovering from incidents? How many incidents are you having and what is their severity? Who is actually discovering the issues - are there automated detections and is it the producer or consumers discovering them? How do you actually think about trust and the impact of trust on the success of your implementation? How do you measure and increase trust levels? How does that impact value creation? And finally, what is the quality of your metadata?
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