“Data observability” can be described as the practice of monitoring the “health and state” of data pipelines in your system. This practice encompasses some technologies and activities that enable business operators to identify, examine, and solve data-related problems in near real time. Though organizations rely heavily on accurate and reliable data to make informed decisions, […]
Understanding Data Observability Tools
Data observability tools have become increasingly important as businesses rely more and more on data-driven decision-making. These tools are used to support the data’s reliability, consistency, and accuracy throughout the business. Data observability has become necessary for developing trustworthy data and diagnosing data flow problems that interfere with the business’s objectives. Data observability tools normally […]
What Is Data Quality? Dimensions, Benefits, Uses
Data Quality (DQ) describes the degree of business and consumer confidence in data’s usefulness based on agreed-upon business requirements. These expectations evolve based on changing contexts in the marketplace. As people get new information and experience different interactions, business requirements face updating, redefining Data Quality needs within the data’s lifespan. Since DQ represents a moving […]
Observability Maturity Model: A Framework to Enhance Monitoring and Observability Practices
Imagine this heartfelt conversation between a cloud architect and her customer who is a DevOps engineer: Cloud architect: “How satisfied are you with the monitoring in place?” DevOps engineer: “It is all right. We just monitor our servers and their health status – nothing more.” Cloud architect: “Is that the desired state of monitoring you […]
Testing and Monitoring Data Pipelines: Part Two
In part one of this article, we discussed how data testing can specifically test a data object (e.g., table, column, metadata) at one particular point in the data pipeline. While this technique is practical for in-database verifications – as tests are embedded directly in their data modeling efforts – it is tedious and time-consuming when end-to-end data […]
Testing and Monitoring Data Pipelines: Part One
Suppose you’re in charge of maintaining a large set of data pipelines from cloud storage or streaming data into a data warehouse. How can you ensure that your data meets expectations after every transformation? That’s where data quality testing comes in. Data testing uses a set of rules to check if the data conforms to […]
Data Observability vs. Monitoring vs. Testing
Companies are spending a lot of money on data and analytics capabilities, creating more and more data products for people inside and outside the company. These products rely on a tangle of data pipelines, each a choreography of software executions transporting data from one place to another. As these pipelines become more complex, it’s important […]
What Is Data Observability?
Data observability is the practice of monitoring and analyzing the health of an organization’s data and data systems. Essentially, it gives you a 360o overview of what’s happening with your data at any given point in time. This practice is beneficial, as it provides all stakeholders with an in-depth insight into how their data is collected, […]
Data Quality Management 101
Data Quality Management is necessary for dealing with the real challenge of low-quality data. Data Quality Management can stop the waste of time and energy required to deal with inaccurate data by manually reprocessing it. Low-quality data can hide problems in operations and make regulatory compliance a challenge. Good Data Quality Management is essential for […]
4 Key Takeaways for Your Data Quality Journey
The road to better Data Quality is a path most data-driven organizations are already on. The path becomes bumpy for organizations when stakeholders are constantly dealing with data that is either incomplete or inaccurate. That scenario is far too familiar for most organizations and creates a lack of trust in Data Quality. While most organizations […]