New questions on ethical implications, data privacy, or public safety are studied seemingly daily.
Date lineage is increasing important to business users looking to understand and trust data. Yet tradtional solutions provide incomplete and inaccurate results
Poor data quality is the single biggest contributor to the poor performance of customer risk-rating models. Incorrect know-your-customer (KYC) information, missing information on company suppliers, and erroneous business descriptions impair the effectiveness of screening tools and needlessly raise the workload of investigation teams. In many institutions, over half the cases reviewed have been labeled high risk simply due to poor data quality – McKinsey
Digital transformation is not a technology problem, but a business imperative, and insurers need to begin to look at their data as a business asset and not simply the domain of the IT department
Big Data has become a buzzword in recent years as businesses have discovered new ways to collect valuable information about their customers and processes. The advent of mobile tech coupled with the Internet of Things (IoT) has given companies new ways to collect data, while machine learning has given analysts the tools needed to discern…
The data translator bridges the gap between the data scientist and the business
To remain competitive, traditional banks must be able to make intelligent decisions on how best to serve their customers – and the crux of intelligent decision-making is quality data.
For someone that’s been preaching data governance and data quality for more than fifteen years, its been fascinating to see how these two topics have been gaining traction in the last few years. A few week’s back I touched on the difference between data governance and data quality – governance is about “what” and “who”,…