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Are your campaigns missing the mark? Are your KPIs telling different stories? What if the real issue lies in your data? Discover the 9 key dimensions to turn data quality into a true performance driver.

Increasing volume, speed, and types of data: the amount of data is ever multiplying, and with it, pressure for personalisation and compliance. As well, to maintain control of data, businesses are turning to Master Data Management to break open silos and automate governance processes.

Agentforce projects carry a strong promise: to automate processes and improve efficiency within the CRM. But without reliable and well-structured data, even the most advanced agents cannot reach their full potential.

Turning scattered data into real business opportunities is a major challenge. CRM, e-commerce, sales, marketing, data, and IT leaders all stand to gain from properly qualifying their data to maximise its value.

Clienteling is one of the best ways to turn every interaction into satisfaction chance to delight your customers, provided it is based on customer information that is accurate and reliable.

Deploying Data Quality within a company is a “project within the project”. DQE’s client companies have successfully met the challenge. Discover how satisfied they are with the solutions implemented.

Surveys on data quality show multiple problems in companies that have not made efforts to cleanse their customer data. Awareness is growing, but 59% of brands still do not measure the quality of their data. However, qualifying customer data helps avoid many problems.

When companies validate their customers’ contact data, the range of benefits is extensive! Several DQE clients have shared with us the evident benefits of using our contact data qualification and deduplication solutions.

AI must be trained with relevant and reliable data in order to deliver convincing results in its applications. That’s why, when customer data comes into play, the first thing to do is to qualify the elements of its foundation, i.e. customer contact data. What is at stake is the reliability of AI results, user confidence, and successful use cases.

Duplicate and inaccurate data buildup happens fast. Without proper checks and balances, your CRM data could become a pit of quicksand full of messy, wrong contact info. Avoiding this disaster is the key to making your CRM work for you.

Data quality is the basic foundation of a data-driven company, data quality requires real management to be applied at all levels. Here are 3 pitfalls to avoid in order to properly control data quality management.

On Salesforce Ben, discover this insightful article by DQE titled: Why Data Governance Could Be Your Key to Sustainable Data and Maximizing AI Efficiency.