How to get Sound Customer Data for CRM
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The document discusses the importance of data quality for customer relationship management systems and identifies common sources of data errors such as input mistakes, external inconsistencies, and data migration issues. It recommends that businesses use a four point program from Data Check Central to assess customer data quality, cleanse and improve inaccurate data, model the data, and integrate it to eliminate errors and ensure data standards are followed. This will help businesses maximize productivity, increase customer satisfaction, and improve marketing return on investment through targeted communications.
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Poor data quality is one of the main reasons why nearly half of all CRM business initiatives fail to achieve their targets.
Today, organizations rely on CRM systems to effectively interact and engage with customers and prospects. Whether that be through target marketing, customer outreach or sales interactions – it is essential to ensure continuously high-levels of data quality within your CRM which does not come without it’s challenges.
View this webinar on-demand to learn about the latest enhancements in Syncsort’s Trillium Quality for Microsoft Dynamics CRM, including the ability to easily import and de-duplicate contacts and leads. Come explore how easy integrated data quality can be for Microsoft Dynamics CRM on premise and Microsoft Dynamics 365 in the Cloud.
System Overload: Getting Control of Your Data and Sanity
The document discusses strategies for managing and maintaining data quality in Salesforce implementations. It provides examples from Genesys Conferencing and Ryder System of how they standardized processes, integrated external data, and used tools like the Excel Connector and Customer Connect to improve data quality over time. It emphasizes that ongoing manual processes are still needed along with automation to continuously monitor data health.
System Overload - Getting Control of Your Data and Sanity
The document discusses strategies for managing and maintaining data quality in Salesforce implementations. It provides examples from Genesys Conferencing and Ryder System of how they standardized processes, integrated external data, and used reports and dashboards to identify issues and ensure data remains clean over time. Both companies required manual effort initially to get data in a clean state before establishing more automated processes and leveraging tools like Excel Connector and Customer Connect.
What Is Data Quality.pdf
Data quality refers to the reliability and accuracy of data. It is important to consider factors like completeness, consistency, currency and standardization. Poor data quality can negatively impact business decisions and performance by increasing costs, lowering customer satisfaction and employee morale. Tools are available to improve data quality by identifying errors, validating values and standardizing formats to enhance consistency and usability of data. Selecting the right tools requires defining data quality goals and roles to ensure high quality data supports business objectives.
A Business-first Approach to Building Data Governance Programs
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. In this presentation, we share a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term.
Developing & Deploying Effective Data Governance Framework
This is the slide deck presented at the Customer Privacy and Data Protection India Summit 2019 held in Mumbai, India. The specific topics touched upon are the guiding principles, Aligning with Data Architecture, Data Quality & Compliance.
How Data.com Drives Data Quality
Dun & Bradstreet collects data from over 30,000 global sources to maintain records on over 233 million companies worldwide. They use a proprietary process called DUNSRight that involves global data collection, entity matching, assigning D-U-N-S numbers, determining corporate linkages, and using predictive indicators to ensure high data quality. Data.com also sources data from its community of over 2 million members who contribute and update contact records, as well as validating data through technological processes. Both Data.com and Dun & Bradstreet have dedicated teams that continuously monitor data quality and make improvements to their methods.
Building a Data Quality Program from Scratch
The document outlines steps for building a data quality program from scratch, including defining data quality, identifying factors that impact quality, best practices, common causes of poor quality data, benefits of high quality data, and who is responsible. It then provides recommendations for getting started with a proof of concept, expanding to full projects, profiling data, analyzing and fixing issues, monitoring, and celebrating wins.
Is Your Data Ready to Drive Your Company's Future?
Before investing the time and money to implement a reporting and analytics solution to guide you out of the current economic crisis, make sure that your data is prepared to lead the way.
Join Edgewater Technology for a step-by-step approach to readying your data to support enterprise reporting and analytics applications.
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