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Experian QAS case studies
Data quality for the decisions that matter
Experian QAS have built up exceptional market coverage assisting over 10,000 customer globally customers with their unique data quality challenges. We provide a comprehensive toolkit for data quality projects combining our market leading software with a vast scope of reference data assets and services.
Our mission is to put our customers in a position to make the right decisions from accurate and reliable data. The size and scope of data management projects varies considerably but the common factor in all initiatives is that they are the key to unlocking operational efficiency and improving customer engagement.
We see the potential of data. Whether it's in enabling ambulances to be sent to the exact location of an emergency or attributing charitable donations to the people who need it the most - data accuracy makes the telling difference to service provision.
All content by Experian QAS
Continuous data management improvement
Company: Experian QAS
just 7% cent of organisations have VIEW SUMMARY
Category: Master Data Management | Published: April 2013| Type: White Paper
Harness the power of data to drive marketing ROI
Company: Experian QAS
What is data driven marketing? Quite simply, Data Driven Marketing tells you what your customers want so you can make sure you give it to them. By harnessing the insights and information locked in your customer data, it removes the guesswork from your marketing campaigns. VIEW SUMMARY
Category: Customer Data Management | Published: November 2012| Type: White Paper
Rating: 
+ 1
Data quality strategy: a guide
Company: Experian QAS
This guide has been devised to help you build a data quality strategy to govern the management of contact information across your organisation. At the rear of this document you will find appendices which will help you build a case for committing to a data quality strategy. VIEW SUMMARY
Category: Data Quality | Published: November 2012| Type: White Paper
Rating: + 6
10 top tips: data management
Company: Experian QAS
Data quality can seem like a daunting task, but it’s really all about having the right people, processes and technology in place. These 10 simple steps will help you focus your efforts and build an action plan of how to approach this massive challenge. VIEW SUMMARY
Category: Data Quality | Published: November 2012| Type: White Paper
Rating: + 4
The data revolution: liberating lost budgets
Company: Experian QAS
With flat or falling budgets, every penny counts! On average, data inaccuracy is wasting between 15 and 18 per cent of all departmental budgets.This paper which leans on research from Dynamic Markets looks into the issues and shows you how your business can do more with the data you have. VIEW SUMMARY
Category: Data Display & Analytics | Published: September 2012| Type: White Paper
Rating: + 4
Data governance - a phased approach
Company: Experian QAS
89% of companies agree that departmental funds have been wasted due to data inaccuracy. This paper discusses how adopting data governance through a phased approach can help you understand these underlying causes and introduce efficient solutions that improve data accuracy and quality. VIEW SUMMARY
Category: Data Governance | Published: September 2012| Type: White Paper
Rating: + 8
Build a successful business case for a single customer view
Company: Experian QAS
This paper gives you an in depth view into building a successful business case for a single customer view and the benefits this can bring to your business. An effective SCV gives you the platform from which you can; detangle information flows, uncover deep insight and produce targeted engagement. VIEW SUMMARY
Category: Data Display & Analytics | Published: September 2012| Type: White Paper
Rating: + 8
Contact data quality in retail banks and reducing data wastage
Company: Experian QAS
This technology white paper analyses a recent Experian QAS survey revealing that nearly two out of three retail banks find that between five and thirty percent of their marketing budget is wasted annually as a result of bad data. VIEW SUMMARY
Category: Data Mining | Published: January 2011| Type: White Paper
Rating: + 4
