The Importance of Data Profiling for Data Management
Any data professional will agree that having accurate, clean, and consistent data is critical for meeting business objectives. And yet, you will hear about only
Any data professional will agree that having accurate, clean, and consistent data is critical for meeting business objectives. And yet, you will hear about only
To understand the complete customer story and extract benefit, it is necessary to integrate and merge data such that each record represents a single entity.
Consumers interact with a brand through hundreds of touchpoints across devices, platforms, and channels. During the buyer’s journey, consumers use 3-4 internet-connected devices. And by
Data cleansing and data preparation are not the same. When you are cleaning data, you are removing inaccuracies, invalidities, and junk from it. But when
Data compliance standards (such as GDPR, HIPAA, and CCPA, etc.) are compelling corporations to revisit and revise their data management strategies. Although each standard enforces
A household consists of multiple separate accounts that belong to individuals who are related and are physically living together.
Duplicate data is a serious problem that affects an organization’s insights, eats up expensive storage space, messes up customer information & leads the business into
Research shows that businesses can lose up to $3 of every $10 of revenue due to poor data quality. With incomplete data known to be
Les données du monde réel auront toujours des valeurs incomplètes ou manquantes, surtout si elles sont recueillies auprès de plusieurs sources. Des données incomplètes peuvent
Inaccurate data has real-world implications across industries. In law enforcement, inaccurate data could mean booking the wrong person for a crime. In healthcare, it could
Where there is data, there is regulation. Most of all in the financial industry. Banks, insurance companies and financial institutes must deal with a complex
Address data is semi-structured, making it one of the most challenging components in a data matching activity. For long now, manual data matching methods including
Data Ladder offers an end-to-end data quality and matching engine to enhance the reliability and accuracy of enterprise data ecosystem without friction.