The word that comes out to me immediately is “commitment,” or, as I like to think, a “promise.” AlS is a promise to your stakeholders that you will provide a quality and predictable service they can count on. It`s also a promise to communicate if your level of service is compromised. reliability. Reliability is closely linked to accuracy, but rather a relative measure of the confidence that can be put in data values. Reliability is often used for data provided by external suppliers. For example, a bank that obtains credit ratings from a credit company estimates that the credit ratings it obtains are correct for 99.9% of its outlook. With an example rate of 100,000 customers, credit ratings for 100 customers are wrong. Before we look at the ALS categories of Data Warehousing, let`s check out some universal categories: the data team promises to provide services with the expected services. If there are problems, you may have reporting problems and we promise to resolve the issue in times of reaction. However, we have internal monitoring processes in place so that if there is a problem, we want to find it first and publish the communication. One of the main metrics for network services is when a customer needs to be contacted when a connection outage has been detected. The next metric is the period before the outage needs to be corrected. How do you check your service level agreements? Do you have any suggestions for ALS checklists that could help you optimize your business relationships? Let us know by writing a comment, your findings are important to us.
💡 data quality metrics A key element of ALS4D is measurable data quality metrics. If the parameter cannot be measured, it cannot be controlled or controlled and is therefore of little use as a condition or term in an SLA4D. In a previous article, The Partnership of Six Sigma and Data Certification, several data quality metrics were presented, including: data reliability can also be associated with the reliability of the data source.