The transformation of data quality to its augmented version has been quite an impressive journey

Augmented data management uses artificial intelligence (AI) and machine learning to improve or automate data management. It can assist data experts, such as data scientists, in time-consuming and data-intensive operations that would otherwise be completed manually.

Need for Augmented Data Quality

We're witnessing an increase in demand for automated data quality management systems as data sources & quantities expand dramatically year after year. Since so much data is now being created and processed at such a rapid rate, traditional data quality management procedures are no longer effective.

Data quality has traditionally been a manual duty that has fallen on data administrators who are unable to keep pace with the increasing number of problems. There are currently too many data sources, and the information is inconsistent and unorganized. As a consequence of persistent data quality issues, projects take a long time to finish, decision-making gets delayed, and resources are just squandered. Clearly, this is not a viable option.

Data quality is important since it is utilised to make decisions and fuel AI algorithms. Models and judgments are only as effective as the data they're based on, thus any lack of trust in the data makes them less valuable for forecasting and delivering insights, slowing down, and undercutting the process of quick decision-making. Because trust in data is difficult to earn and simple to lose, data quality should be maintained at all times for models & dashboards to be meaningful. The volume, diversity, and velocity of corporate data have expanded considerably in the previous ten years, making static testing impractical to ensure data quality. As a result, data-driven companies must be able to maintain trust in analysis and simulation models at all times.

Augmented data quality in action

By 2022, 60% of enterprises will use AI-enabled data quality technologies for ideas to minimize manual chores for data quality improvement, according to Gartner.

Augmented Data quality may be assessed using either categorical or numerical data. Master data, for example, is categorical data, which is a collection of different values. Data quality refers to determining if a value matches one on the table, is a new value, or is just a data quality issue that should be mapped to a legitimate value. For numerical data, such as fact data, ML employs statistical process control, such as identifying a range of values, trends, and data feed limits.

Machine learning-based data quality solutions basically train models to examine what has been done previously to rectify incorrect data and how data controllers have allowed such adjustments.

With these insights, you might expect a machine learning model to achieve a high level of confidence in 80% of situations to accurately determine a data quality issue and make the appropriate change to fix it, or at the very least to specify the change so that a database administrator can review and approve it.

The typical business user can draw in with innovative analytics tools that take into account Automated Machine Learning (AutoML) and utilize sophisticated analytical techniques and algorithms in a steered environment that uses auto-proposals and suggestions to guide users through the unforeseen universe of data science with ease and intuitive tools using Augmented Data Science.

Data quality will be impacted by enhanced data management. It will also assist in making faster, more scalable, and improved business decisions. Even better, firms may benefit from more precise anomaly detection and correction with the help of automation.

Applications providing Augmented data quality:

1. Machine learning is used in technology like Wavicle's AugmentTM to continually assess and monitor the quality of data. You can quickly uncover data quality patterns and assess the effect of data quality on your business models with Augment.

2. DQLabs' Augmented Data Quality Platform