https://store-images.s-microsoft.com/image/apps.61239.bdea0f26-12f0-48dd-88b4-8e84c12e3468.6651c788-a523-4d38-a812-a991737f01e1.d3f39236-cdb3-47bb-b18c-dc549647cd91
EY Data Quality and Cleansing
by EY Global
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Supporting analytics programs and business transformation benefits
Solution Overview
The
EY Data Quality and Cleansing Service solution combines the deep industry,
domain and systems knowledge of EY with
the data quality and cleansing service that leverages Microsoft’s advanced
technologies — Microsoft Azure services, Microsoft machine learning (ML),
Microsoft Revolution R for data profiling/cleansing and Microsoft Data Quality
Services (DQS) — to help you in your advanced analytics, internet of things
(IoT) and big data initiatives. The integrated platform reduces the total cost
of ownership, and provides a sustainable and cost-effective way to deliver data
quality.
Solution Benefits
- Improving business performance: Facilitate trusted decision-making support for increased accuracy in forecasting, making better budgeting and operating decisions
- Gaining competitive advantage: Drive valuable business insights from your analytics programs to improve profitability and reduce costs
- Promoting analytics-driven culture: Increase employee engagement and trust, leading to greater adoption and consumption of analytics — creating a culture of analytics
- Managing risk and regulatory pressures: Improve reporting processes through the exploitation of more robust data, while also identifying potential risk areas, such as compliance violations, fraud or reputational damage
- Achieving sustainable results: Help build a broad data cleansing and data quality monitoring solution to clean your data and prevent deterioration of data quality
- Boosting speed to value: Reduce the time and costs associated with traditional data cleansing to deliver speedy analytics with the pre-built data profiling library and data cleansing procedures of EY teams
At a glance
https://store-images.s-microsoft.com/image/apps.35744.bdea0f26-12f0-48dd-88b4-8e84c12e3468.09ea6827-0249-4e21-85a7-c4ae7e37c2ef.dd3af3dd-4d51-4317-a931-b2c474687d30
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