Continuous Data Quality Monitoring with DQLabsContinuous Data Quality Monitoring with DQLabs https://www.dqlabs.ai/wp-content/uploads/2021/10/Continuous-data-quality-monitoring-using-AIML-1-1.webp 540 300 DQLabs DQLabs https://www.dqlabs.ai/wp-content/uploads/2021/10/Continuous-data-quality-monitoring-using-AIML-1-1.webp
Continuous data quality monitoring using AI/ML with DQLabs
In today’s world, we have been doing more of the traditional data management practices. This is a process of connecting people, processes, and technologies by creating governance foundations, going into data stewardship, standardizing and setting policies, executing master data management, data quality, and a feedback loop. The problem is that it takes a lot of time and cost and most of the time the value is not generated.
DQLabs, however, takes a paradigm shift from this traditional approach and focuses on,
- Self-service automation
- Support all types of users
- Automate first as much as one could
DQLabs.ai can be described as an augmented data quality platform that manages an entire data quality life cycle. With the use of ML and self-learning capabilities, DQLabs helps organizations measure, monitor, remediate and improve data quality across any type of data.
This article helps you understand how DQLabs performs data quality monitoring continuously, not just once. There are three different metrics that we capture by using a lot of other processing procedures automatically. There are three levels of measurement we do;
- Data quality scores are the standard data quality indicators used to record the quality attributes of the data. Usually, most products are validated by these data quality rules or by tying different rules to different sizes and then bringing a score. DQLabs does that. However, the main difference is we don’t expect the users to manage or create any of these rules. DQLabs platform does it all automatically by a semantic classification and discovery of the data within your data sets. For example, if you have a number data, is it a phone number, social security number, or license number? Those are the questions we ask, and we use different types of technologies around that to identify that. Once we recognize, we automatically create all these checks we need to do across these dimensions and calculate this. We also do subjective measurements. Subjective dimensions are usually collected by customer satisfaction service or input from different users, functional stakeholders, etc., usually in a traditional world. At DQLabs, however, we have a collaborative portal that users within your organization use. We track every type of usage that happens within that portal in terms of viewing, adding favorites, a conversation that goes across, or any remediation of data quality issues that occurs within that particular data set. That allows this subjective way of measuring data quality metrics, so that’s a measurement at one snapshot, but this is also done continuously.
- Impact Score; we not only measure and give how many records are bad based on those checks, but we also take it to the next level of how much we can convert automatically. This is important because we no longer find insufficient data and provide tools to remedy it. We then take it to the next level of how much difference we can make automatically. This is critical in the world of data preparation, data science, engineering, or data engineering because you’re not doing it manually. It is a seamless process and measures how much of an impact we are making. This ensures you understand the bad records using a quality data score, and you can measure what percentage of those bad records can be turned into good records. For example, if the data quality score depicts the accuracy score to be 60%, the impact score can automatically determine how much of the 40% bad score can be converted into a good score. This will happen for all the data quality checks such as completeness, consistency and accessibility in a continuous way.
- The third level of scoring is called a drift level. This is primarily identifying the volatility of the data. An example of a drift level is a stock market price for a stock ticker. The cost can go up and down, and sometimes based on the data collection, it could be a system outage that may be causing a bad record or macro factors such as economic factors, which may be beyond your organization’s control. We have created another set of scores to measure the volatility of the data, and based on the strip level, which can go from none to low to medium to high. All this is done automatically out of DQLabs this is done using the statistical trending benchmarking and then using different AI/ML based algorithms etc.
Watch our on-demand webinar to learn more about the use of advanced algorithms to identify data quality issues not just once but continuously.
In conclusion, the idea of continuous data quality monitoring is to prioritize data quality first, and then move on to the process of discovering all of these metrics right away. This enables greater automation, increased ROI for organizations, and enhanced customer experiences by providing them with trustworthy data and business insights in minutes.
Interested in trying DQLabs free? Request a demo.