Data Cleansing and Governance Metrics: Ensuring Data Quality
Data quality is a hot topic. Everyone is talking about it, but few seem to be doing anything about it. The reason for this is that data quality has many dimensions, and each dimension requires a different approach to ensure success. Data cleansing and governance metrics are the means by which you can measure how well your data meets quality requirements. In this article, we'll discuss some of the most important metrics every organisation should be using to monitor their data cleansing initiatives.
Introduction
Data cleansing and governance is a complex task. Before you start, you need to set your goals. This can be tough, but you have to do it before you begin.
It’s important that your goals are defined, specific, and measurable – otherwise they won't be useful in helping identify problems within your data or how to fix them. For example: “I want my sales team to make more calls each week” doesn't give much information around what specifically needs fixing (e.g., the number of calls being made isn't enough). Instead try something like "double the average call rate from 12% per salesperson per day."
You might be wondering what other people's goals look like? Don't worry about them – focus on yours! The goal should also be ambitious but realistic, which might take some trial-and-error before landing on something that works for both employees and employers alike
Goal
The goal of this article is to explore the concept of data quality metrics and how you can apply them to your organisation. By the end, you'll be able to understand what different types of data quality metrics exist, as well as the benefits they provide when used properly.
Metrics
Data quality metrics are the means to measure how well the data meets quality requirements. They can be used to measure and monitor data quality, as well as provide a basis for improving it.
Data Quality metrics include:
Data completeness - The percentage of records with all required fields populated
Data consistency - The degree to which multiple records in related tables have identical values for certain attributes or entities that are subject to such relationships (e.g., customer ID), or whether values of an attribute within a single record match across different tables (e.g., shipping address).
Data correctness - Whether values in a given record are consistent with business rules established by organisation policies, regulatory compliance needs, and industry standards; these include formatting issues like date formats that prevent calculations from being performed correctly on them (e.g., 2/20/2014 vs 02/20/2014)
Monitoring
Data cleansing is a continuous process, which means that monitoring data quality must also be a continuous process. Monitoring can be done manually or automatically; however, it's important to consider the needs and resources of your organisation when deciding how to monitor data quality. In addition, you should have a strong understanding of your organisation's data governance framework and its ongoing activities related to data governance before implementing any monitoring strategy.
Data Quality Management Systems (DQMS) are designed specifically for monitoring purposes—they're designed with robust reporting features that allow for easy analysis of critical metrics about your business's cleanliness over time. DQMSs also contain built-in dashboards that provide information regarding the status of your company's overall health based on metrics from previous periods of time (e.g., 1 month ago vs 5 months ago). With these tools at hand, you'll know exactly where you stand at all times!
Reporting
In addition to providing key metrics for evaluating data quality and governance, reporting is an essential component of any data governance framework. A report should be automated and provide a way to track performance over time. In its simplest form, reporting can consist of a dashboard or table showing the current state of the data warehouse (or whatever repository you're using). It could also include information about where errors occurred, along with their criticality level. Another option is to create reports that track trends over time—for example, how many errors are being introduced into production every month?
Data quality metrics are the means to measure how well the data meets quality requirements.
Data quality metrics are the means to measure how well the data meets quality requirements. There are many different types of data quality metrics, which can be used to measure the quality of a dataset or process that generates the dataset.
Examples of common data quality metrics include:
Number of bad records (e.g., missing values)
Number of duplicate records
Fraction of blank fields in a record (which may indicate incomplete or incorrect information)
In conclusion, data cleansing and governance metrics are the means to measure how well the data meets quality requirements. They can also help to identify gaps in compliance and monitor progress towards meeting business objectives. The key takeaway here is that all of these different types of metrics should be used together as part of an overall strategy for managing data quality across your organisation.