A Guide for CDOs and Data Executives

How to Get Data Quality Right

Don't Let AI Efforts Fall Flat

According to MIT, poor data quality is estimated to cost companies a staggering 20% of revenue. This ebook has best practices for controlling data quality at scale to ensure that data efforts don't put AI ambitions at risk.

Ensure Effective Data Quality Today

87% Time Saved
Preparing Analyses and Deploying Insights

Bankers’ Bank leverages Dataiku to ensure data quality across an array of financial analytics, resulting in an impressive 87% reduction in time spent on preparing analyses and deploying insights. This transformation has streamlined their previously manual transactional reporting process, unlocking newfound efficiency and agility.

Explore this ebook to understand the critical impact of poor data quality on business functions. It offers strategies for controlling data quality, from securing stakeholder buy-in to establishing metrics, and highlights key data quality attributes to monitor and maintain.

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Drive Organizational Success With Strategic
Data Quality Management

Build a Robust Infrastructure, Establish Precise Metrics, and Assign Clear Ownership to Optimize Performance

Secure Leadership and Stakeholder Buy-In

Ensure alignment on metrics and KPIs to drive successful data quality initiatives and better business outcomes.

See p. 6

Invest in Data Quality Infrastructure

Benefit from a robust data quality infrastructure to ensure effective data governance, enabling businesses to promptly address issues and drive better outcomes.

See p. 7

Establish Metrics Around Accuracy

Link data quality metrics to business KPIs for actionable insights and improved performance. 
See p. 8

Define Data Quality for Your Organization

Align on a clear definition of data quality by establishing data policies, standards, and a business glossary to manage consistent expectations across all departments for effective data management.

See p. 9

Assign Ownership

Designate individuals responsible for data accuracy and daily quality monitoring, ensuring clear roles and compliant data usage.
See p. 10

Implement Issues Management

Establish a clear process for identifying and resolving data quality issues promptly at the onboarding stage to ensure data integrity and efficiency.
See p. 10

“A lack of quality data is probably the single biggest reason that organizations fail in their data efforts.”

Jeff McMillan
Chief Data and Analytics Officer, Morgan Stanley Wealth Management

“The most important thing we do every day is ensure the accuracy of the input. If you are not investing in a data quality/data governance infrastructure, you’re going to fail.”

Jeff McMillan
Chief Data and Analytics Officer, Morgan Stanley Wealth Management
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