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Monthly Archives: November 2014

Working at a socially responsible company: What makes Experian Data Quality stand out?

This October I participated in my third 60 mile walk for breast cancer. The event raises awareness and funds for breast cancer research and screening. As the daughter of a survivor, this event and the cause are very close to my heart. The walk is a life-changing experience, full of inspiration and remembrance. Each year I am most impressed by the support I receive from my community.

What does this have to do with Experian Data Quality? Participation in the walk is not only a large financial commitment (with fundraising minimum of $2,300), but also a huge time commitment, for both training and the actual walk itself (requiring travel this year to Atlanta, GA). I am thankful to work for a company that positions itself in part of my support community. Experian Data Quality allowed for me to take the time off needed to travel to and participate in the event showing their dedication to having socially responsible employees. Additionally, 43 percent of donors to my fundraising efforts came from Experian Data Quality employees, including senior management and peers both on and off my team.

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All professionals must take responsibility for data quality rules

Rachel Wheeler

November 25, 2014

Archive

Data quality should be a key goal in business.

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Everyone has loyalty campaigns, but few get the data right

It seems that every time I go into a store today, I am offered a loyalty card. From one of my favorite local restaurants to my shoe store VIP program, I feel like I am getting a host of emails and points at every turn. Statistics support my theory: according to a recent Experian Data Quality study, 91 percent of organizations use loyalty programs.

Why did they become so prevalent? Today’s consumer is more empowered than ever before and driving major change within business. In the era of Yelp, digital channels and a 24/7 shopping cycle, organizations have less control.

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6 tools for building a data quality strategy

Stephanie Zatyko

Stephanie Zatyko

November 17, 2014

Data quality

Determining what type of data quality tools your organization needs depends upon how sophisticated your data quality strategy is. For some companies, a simple data quality approach may be fine, but other organizations may need a very advanced level of data accuracy and data management.

Regardless of your organization’s specific needs, there are a few key universal data quality considerations that are important for any effective approach. Here are six tools that can help with your data quality strategy.

1. Data cleansing: Data cleansing tools are needed when an organization’s data must meet specific domain restrictions, integrity constraints or other business rules. These types of tools provide accurate information for business use. Examples include address verification, email verification, phone validation, etc.

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3 killer marketing strategies for the approaching holiday season

Richard Jones

November 14, 2014

Archive

It's always important to maintain close relationships with your customers.

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Targeting the right demographics in direct marketing

Rachel Wheeler

November 13, 2014

Archive

One key takeaway from direct marketing involves already loyal customers.

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Are you getting value from your data quality tools?

Erin Haselkorn

Erin Haselkorn

November 12, 2014

Data quality

Data is an increasingly important part of organizations today. It can be leveraged for traditional operations and efficiency, but now is also being used to gain a better understanding of the consumer, prompt personalized marketing messages and determine a host of new product innovations.

The increasing use of data is putting a greater spotlight on information and how it is used. But many companies have issues with their data. In fact, a recent Experian Data Quality study found that U.S. companies believe on average a quarter of the information in their databases is inaccurate.

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Exploring social media data and its impact in 2014

Paul Newman

November 12, 2014

Archive

As technology has evolved, the definition of "data quality" has changed.

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Using accurate data to understand customer behavior

Richard Jones

November 11, 2014

Archive

One great use for data quality is in understanding customer behavior.

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Examining the pros and cons of retargeting customers in marketing

Richard Jones

November 10, 2014

Archive

Part of the beauty of data quality is you can rely on it again and again.

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