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How to stop data quality from undermining your customer experience
Nearly one-third of customer and prospect data believed to be inaccurate, delivering personalized product offerings and critical consumer goods can be a hurdle. We’re here to help you with a strategy for quickly addressing growing volumes of inaccurate data so you can meet the pressing real-time needs of your customers.
3 most common data preparation challenges—and how to solve them
Research shows 93 percent of businesses face challenges in preparing data, and more than one-third are unable to see ROI from data management initiatives. Simply put: Bad data is bad for business.
4 best practices for achieving trusted data
In data we trust…or do we? That’s the question—and it’s one that the vast majority of companies continue to agonize over. Just how much? Research shows by increasing the usability of data by just 10 percent, the average Fortune 100 company could expect an increase of $2 billion dollars in revenue.
Highlights from our 2020 Global data management research
The demand for data-driven insights and innovation has never been higher. Maintaining a competitive edge in today’s digital landscape hinges on the ability to leverage accurate and reliable data to make informed and strategic business decisions. Learn more
Applying quality data to the retail supply chain
When it comes to data quality, don’t forget about staffing
The old saying “information is power” could not be truer today. The information we house in our databases is used to do everything: help us better understand customers, provide a better customer experience, improve operational efficiency, and ensure more informed business decisions.
The desire for data and data-driven insight has proliferated every industry—and that drive for data puts data scientists and data managers in a great position. There is far more pressure for information to be accurate, consolidated, and complete. Otherwise, insight will either take too long to obtain or will be inaccurate. The lack of expertise in the market and resources for staffing is creating gaps in data insight.
Three ways to justify a data management initiative at your financial institution
Data is only as good as the quality behind it. The saying ‘garbage in, garbage out’ holds true. If you use inaccurate data for analytics, customer insight, or reporting, your institution is open to poor customer experience, inefficiency, or even non-compliance. While many agree that trusted data is a must, not everyone is making the investments needed to make that a reality. Here are three ways to justify a data management initiative within your institution.
Data quality before data governance
Focused on the needs of government agencies, the DGVision conference empowered federal employees with new insights, best practices, and education around starting and optimizing data governance initiatives. Experian joined business leaders in both private and public sector companies for meaningful conversations about all things data governance.
Email validation: Why you want quality versus quantity
To ensure successful email delivery, reduce the chances of being filtered out by an email service provider (ESP), and avoid becoming blacklisted as a spammer, organizations need to ensure the accuracy of the email data held in their mailing lists. Learn more about Experian’s industry-leading technology which verifies business domains, international domains, and consumer domains like Yahoo!, Gmail, Hotmail, and more.
How to justify and support your data governance initiative
We find a growing number of businesses are either looking to start or optimize data governance programs to better control data, but at Data Governance Winter Conference, we learned that there are often two major roadblocks to implementing or optimizing your data governance program. Explore how you can navigate these challenges.
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