AI-enhanced Data Quality​

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Problem

According to Gartner, $14.2M is lost to poor data quality each year. Data quality refers to how suitable the data is for its intended purpose. High quality data is extremely important as it impacts the performance of business operations.

For financial services, in particular trading teams, data must be of extremely high standard, as any outdates, duplicates or omissions in the data can directly impact the trades. For instance, any obsolete data can lead to traders making the wrong decisions and put them at loss. In addition, it can also lead to regulatory issues.

Currently, data quality is a rule-based manual process, which is rigid, time-consuming, expensive and difficult to scale. This process can only be applied to random checks on sample data and to problems known to the current team, failing to detect unknown data issues. Furthermore, as the volume and types of data increase exponentially, it can quickly become unfit for purpose.

“Failure rates in fixed income are incredibly high, and that is mostly due to data quality issues”.
--Virginie O’Shea, research director at consultancy Aite Group

Solution

 EvoML enables data quality teams to automatically build ML models to identify anomalies in the data and provide suggestions on how to correct it. Models are also self-adjusting to changes in trading environments. This will greatly reduce the time, cost and effort it takes to manually identify errors. With trustworthy data, traders can then make better decisions, faster and with confidence.

Furthermore, data professionals can use EvoML to create ML models to automatically capture and add incoming data to existing sources, so that it is always up do date in real time and ready to use on demand. This allows traders to move with the fast-changing market and stay ahead of the competition, avoid risk and reduce loss.

Benefits

  • • Accelerated data quality at scale:

Speed up data quality process by building ML models to automatically detect and correct anomalies in the data. Models continuously learn from incoming data and easily scale.

  • • Reduce cost and effort:

By automating the process of data investigation and reducing the time it takes to manually fix each issue, financial institutions can exponentially lower the cost and efforts needed to improve data quality.

EvoML can be used by:

  • • Data Quality Team
  • • Data Quality Platform Vendors

Find out more use cases in data management

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