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Already hailed as a valuable new addition by many in the industry, DataValidator was introduced by Willis Towers Watson to make sure that data which is used in financial modelling and analytics is of a high quality, ensuring greater accuracy and saving time. Moreover, it also produces full audit trails needed for regulatory controls and current governance of financial modelling and reporting processes.

The InsuranceERM judging panel comprises leading industry professionals with deep experience of risk and capital management, and they praised DataValidator’s relevance to one of the industry’s main challenges. They additionally praised the solution’s clarity and ease of use, as well as its ability to bring a new consistency to auditing across the business.

What makes DataValidator particularly innovative is unlike other software in the same category, it does away with database and script-based solutions. Instead, it guides users through each stage of a comprehensive audit process, providing detailed information about each step. This makes it possible to quickly implement validation, cleansing and transformation rules while retaining total transparency for later oversight and analysis.

“That the judges recognised our determination to help companies needing to be able to account for the quality of their data and take steps to audit the process was really gratifying,” explains Mark Brown, the DataValidator product lead at Willis Towers Watson.

“All our software is the result of deep levels of industry insight which allows us to create solutions that serve a genuine need. We had become aware that there were real concerns about data quality and about the usability and transparency of available solutions. DataValidator addresses all of those in a single, powerful way.”

There are many converts to DataValidator. A UK company which looks after a number of closed books and systems covering 900,000 policyholders used it to validate, cleanse and transform their data when they were faced with numerous, separate transformation and validation processes which had to be completed within a tight schedule. They achieved a 70% reduction in the number of processes used to achieve model-ready data, and estimated they reduced user effort by 50%.

Prudential UK also reported a successful implementation of DataValidator, using it to transform raw data into model-ready data. They were able to set up over 1,000 quality checks using the in-built data validation functionality, and reported increased confidence in their data as well as greatly improved working day timetables.

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