Maximizing the value of data often comes down to ensuring it’s in the right place at the right time and in the right form. While that process may produce magical-seeming results, the road to creating an optimal data quality strategy need not be mystifying. This was the guiding message delivered by Monika Kapoor – director […]
How to Assess Data Quality Readiness for Modern Data Pipelines
For growth-minded organizations, the ability to effectively respond to market conditions, competitive pressures, and customer expectations is dependent on one key asset: data. But having just massive troves of data isn’t enough. The key to being truly data-driven is having access to accurate, complete, and reliable data. In fact, Gartner recently found that organizations believe […]
Data Science Metrics: Purpose and Uses
When one thinks of “metrics” in the context of Data Science, the term might denote raw numbers as in descriptive metrics, qualitative labels as in marketing analytics, or comparative labels as in website analytics. Metrics come in many different forms and structures, but the primary objective of Data Science metrics is to measure and report […]
How to Implement a Data Quality Framework
According to IDC, 30-50% of businesses experience gaps between their data expectations and reality. They have the data they need, but due to the presence of intolerable defects, they cannot use it as needed. These defects – also called Data Quality issues – must be fetched and fixed so that data can be used for successful business […]
Data Quality Management 101
Data Quality Management is necessary for dealing with the real challenge of low-quality data. Data Quality Management can stop the waste of time and energy required to deal with inaccurate data by manually reprocessing it. Low-quality data can hide problems in operations and make regulatory compliance a challenge. Good Data Quality Management is essential for […]
Data Quality Metrics: Importance and Utilization
Data Quality metrics are a measuring system that allows the “quality of data” to be evaluated. Data Quality metrics can be used to determine how useful and relevant data is, and it helps to separate high-quality data from low-quality data. It is much easier—and safer—to make business decisions based on reliable information. Poor information (based […]
Data Governance Program Effectiveness by the Numbers
When it comes to metrics, “Less is more,” said Kelle O’Neal, Founder and CEO of First San Francisco Partners, presenting at DATAVERSITY® Enterprise Data Governance Online 2017. You don’t have to measure everything, she said, you just have to “choose what’s important and meaningful to your stakeholder group and your program.” O’Neal’s presentation showed how […]