It’s once again time for another Experian Marketing Services Client Summit. This year promises to be a great event and I am getting excited to attend again. Not only do I get to meet so many great marketers, but I also get the opportunity to learn from their experiences over the past year.
Becoming a data-driven company. It is something many of us want to achieve today because it elicits thoughts of ultimate insight. One rarely thinks of data as being sexy, but the concept of being able to use data to accurately predict your company’s next move is pretty cool.
The race is on. Companies are working as quickly as possible to leverage their data assets to drive action and derive value. Terms like big data are common and the c-suite has a desire to pinpoint insight in these mass stores of data.
In the world of data management, there is a lot of terminology that is used interchangeably. For example: validation and verification or fuzzy matching and identity resolution; these are similar terms that are widely used in our ecosystem. Data quality and data governance are two examples of terms that are not synonyms, but are often confused, and with good reason. These two terms are symbiotic, meaning they are interdependent on each other. You don’t really want to do one without the other.
To make best use of your data asset across your organization, it is essential to have a solid data quality framework to ensure that your data is accurate and complete. But all data quality management models are not created equal: different organizations have different levels of data quality maturity, depending on organizational priorities and needs.
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