Data Observability vs Data Quality: What's the Difference?

Data quality is a popular topic, but more people talk about it than actually implement data quality management. Monitoring data quality is simple. You can verify a few rules in the data pipeline or let a data observability tool monitor your data. If you want to learn more about how to start your journey with data quality, I have compiled a list of basic topics to explore. #dataquality #datagovernance #dataengineering

When I work with people to help improve their data management habits, there are 3 things I see people adopt most often that are fairly simple to implement but have huge impacts on how they (and their collaborators) work with data. 1. Naming files systematically so that they are consistently named and versioned and contain any relevant information that a user may need to know about that file (e.g., what is this data, who was it collected on, is it raw or altered data, when was the file created). (https://lnkd.in/gGyBxmVa) 2. Similarly, naming variables so that they follow a standard pattern that allows people to more easily interpret and use your variables (i.e., a standard naming convention, standard abbreviations, consistent order of information). (https://lnkd.in/gRDpSK_V) 3. Creating data dictionaries for datasets (e.g., what variables are in the data, how are they named, what do they represent, what are the variable types, what are the allowable values, and how were these variables calculated/transformed if that is necessary information). (https://lnkd.in/gyEXPnpS) You can learn more about each in the provided links. 🌟

Data timeliness issues are the best indicator of data pipeline reliability. If data ingestion or transformation stops, the data becomes outdated. What if you could detect data pipeline failures without reviewing the logs? How about detecting data pipeline failures in the source system when you don't have access to the logs managed by a different team? Data timeliness monitoring is the answer. Watch the table in regular intervals, such as daily. Find the most recent timestamp and measure how old it is. You must use a timestamp column that stores a transaction or event timestamp. The age of the most recent record will tell you if the data is fresh. Beyond data freshness, you can also monitor other data timeliness metrics, such as data staleness - the time since the data was refreshed. You will need to have another column in the table, such as "inserted_at", which must be populated by the data ingestion pipeline. Additionally, you can compare two timestamp columns, the event timestamp (a business-generated timestamp), and the ingestion timestamp (assigned by the data pipeline) to measure the data processing delay. Check the article in the comment for more details. #dataquality #datagovernance #dataengineering

In my last post, I spoke about the benefits of performing a data assessment and how it uncovers inconsistencies that impact business performance. But here’s the next layer: data context is just as important as data quality. When we review data, it’s not only about spotting invalid fields or duplicates. The real value comes from asking why the data looks the way it does: - A blank field might point to a process gap. - Duplicate vendors often show teams working in silos. - Incorrect country codes can highlight training or system setup issues. By looking at data in context, we move beyond surface-level fixes. This leads to process improvement, stronger ownership, and solutions that last well past a single migration project. Key Takeaway: Without context, you’re fixing symptoms. With context, you’re fixing root causes. When you last looked at your data - did you just check the values, or did you look at the story behind them? #IxiaConsulting #Data #DataQuality #DataMigration

🚨 Unlocking Peak Performance: The Critical Role of Data Pipeline Monitoring 🚨 In the fast-paced world of data-driven insights, a single bottleneck in a data pipeline can lead to delayed analytics and missed opportunities. Data pipelines are complex systems that require continuous monitoring to ensure they deliver accurate and timely data. I once encountered a data pipeline that seemed to be performing well, but upon closer inspection, it was causing significant delays due to high latency. This led to outdated analytics and missed insights. To address this, I implemented real-time monitoring tools to track throughput, latency, error rate, and freshness. By setting up alerts and automating recovery processes, we reduced bottlenecks and ensured timely data delivery. How do you approach data pipeline optimization in your workflow? 🤔 #DataPipelineMonitoring #PerformanceOptimization #DataIntegrity #AnalyticsHealth #RealTimeMonitoring ----------------------------------------

What’s the point of “governing” data you can’t use, or “preparing” data you can’t trust? Data Readiness and Data Governance are two terms often used interchangeably, but they play very different roles in shaping strong data strategies. Data Readiness ensures your data is fit for purpose while Data Governance ensures your data is reliable. But the real magic happens where they meet: when your data is both usable and trustworthy. Dive deeper into how readiness and governance differ yet complement each other, and why you need both to unleash your data’s true power. Read further: https://lnkd.in/dSNxCran

Developing a process or program focused on #dataquality, including continuous auditing and cleaning, is critical to ensure trusted #data. Learn how to achieve excellence in data quality with lessons from Target. Check out our whitepaper. https://bit.ly/3Kjhesc

Data Alignment - Be Transparent Data alignment happens when you join two tables that represent the same point in time. It forms a baseline for any data quality discussion - if you have good quality in Table A and good quality in Table B, but one table is 1/1/2020 and one table is 1/1/2024 then you are degrading the quality in your joined dataset. A good example is a sales transaction from 1/1/2020 and a customer record from 1/2/2024 - you are not the same person that you were in 2020 - there are many factors that can have changed your customer data. Of course, you may get lucky and there were no changes, but luck is not normally part of a data quality discussion. The two most common reasons for data alignment issues are: 1) Complexity Of The Join. The join becomes more complex when you align the correct version of the customer with the transaction. 2) Good enough. The simplest join is to the current customer record. It is also much faster than a complex join. We ignore the risk for the speed to return a result. 3) Not Even Possible. The tables were not even modeled to support data alignment - no correct join is possible. It is hard to identify this as a material issue - as we can't provide data to measure it. Definitely a grey area. There will be situations where data misalignment will occur. You need to be transparent if you believe it can cause data quality issues. #data #dataanalysis

When you plan a new campaign to fire up a new product release, make sure your data is up to the plan as well. Figure what happens if it is not - you loose money and opportunities. For your next campaign, for an upcoming migration to a new system or as your foundation for a good start of your data governance. There is no task too small to take - I care for all your data.

Data cleaning levels Level 1- least deep data processing steps. The data has the following characteristics; It is in a standard and preferred data structure. It is codable and intuitive column title. Each row has a unique identification. Level 2- Unpacking, Restructuring and reformulating the table. In this level, we have a dataset 11that is reasonably clean and is in standard data structure, but the analysis we have in mind cannot be done because the data needs to be in a specific structure due to the analysis itself, or the tool we plan to use for the analysis. Level 3- Missing values, outliers and errors