In today’s world, we are doing more of the traditional data management practices. The process of connecting people, processes, and technologies by creating governance foundations, going into data stewardship, standardizing and setting policies, and with a feedback loop. The problem is that takes a lot of time and cost.
DQLabs takes a paradigm shift from this traditional approach and focuses on,
1. Self-service automation
2. Support all types of users
3. Automate first as much as one could
DQLabs.ai can be described as an augmented data quality platform that manages an entire data quality lifecycle. The use of ML and self-learning capabilities, helps organizations to measure, monitor, remediate and improve data quality across any type of data.
Learn how DQLabs perform 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;
1. Data quality scores: The standard data quality indicators used to record quality attributes of the data. Most products are validated by these data-quality rules tying different rules to different sizes and then bringing a score. DQLabs does that. The main difference is it doesnt 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.
We have a collaborative portal that users within your organizations 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 thats a measurement at one snapshot, but this is also done continuously.
2. 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 youre 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.
3. 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 organizations 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.
To learn more about the use of advanced algorithms to identify data quality issues not just once but continuously Watch our on-demand webinar.
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.