Data Quality Approach

Approach

Traditional Data Management practices have failed.

Organizations have spent extensive time and money on disciplines in the realm of Data Governance, Data Cataloging, and Metadata yet these efforts have not scaled or met expectations without a focused approach towards Data Quality.

Current trends show that data is generated faster than data can be consumed and, in this climate, as a business you need a change in thinking from traditional data management practices towards a “Data Quality First” approach.

What is “Data Quality First” approach?

As organizations grow, you must be aware of where your internal practices land within the data maturity life cycle as this can drastically enhance or hamper the overall growth of your organization. DQLabs defines this data maturity life cycle in four stages:

Data Quality First Approach
Beginner

No data management practices implemented and more people-dependent processes using offline tools and methodologies

Low

Have used or tried individual or departmental efforts either using custom solutions or off the shelf solutions with no centralized focus

Medium

Have implemented a Data Quality, Data Catalog or Data Governance solution and are engaged in an effort towards centralized management practices, however, they are still struggling to streamline processes and have not seen real ROI.

High

Have a centralized data governance team and implemented technology and processes for Data Governance, Data Catalog, Data Lineage, Data Quality, DataOps, and Data Science but many are still struggling to have a modern collaborative data workspace with a focus on Data Quality.

Here’s the bad news - irrespective of which category you belong to, many organizations have no understanding of what percentage of bad data exists. This is because most of the efforts taken towards Data Quality are in conjunction with other disciplines of Data Governance, Data Catalog, or Data Lineage but there is not a primary focus or an actionable effort towards Data Quality.

That is why we preach a Data Quality first approach – an approach that focuses on understanding data from a business context via automated processes like semantic discovery and classification. This is further fueled by automation using proven machine learning technology that helps organizations measure, monitor, and remediate data quality issues in a more practical way and derive immediate value. If you are in the high maturity cycle, the good news is that a Data Quality First approach still takes into consideration your current learnings of business glossaries, governance practices and seamlessly integrates to automate an actionable data quality process.

Using this approach, you can benefit from trustable, actionable data and answer

  • What data is important and what is not?
  • What data needs to be improved or remediated?
  • What data can be used in reporting and analytics?
  • What data pipeline changes are needed to address schema/data deviation or drift?
  • What models can be built to enrich customer experiences?
  • Can I trust this report I am signing off on and am I as compliant as I think I am?

Recently published

Continuous Data Quality Monitoring

BLOGS

Continuous Data Quality Monitoring with DQLabs

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, execution of master data management, data quality, and with 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,

  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 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;

  1. 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 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 that’s 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 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.
  3. 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.

View More Arrow image

Best Practices

Continuous Data Quality Monitoring

BLOGS

Continuous Data Quality Monitoring with DQLabs

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, execution of master data management, data quality, and with 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,

  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 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;

  1. 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 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 that’s 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 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.
  3. 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.

View More Arrow image