What can you do to become data literate?
How businesses manage the knowledge gap between decision makers and data specialists could be the difference between boom and bust
data quality, hyperscale and Python – the foundation of machine learning
A few week’s back I wrote about the data translator – a role on the data science team that bridges the gap between the data scientist and the business. That got me thinking – what are the characteristics (that may be less obvious) that make for a good data analyst? 1. Intellectual curiosity and focus…
I some times joke to my customers and prospects that I have been doing data since long before it was fashionable. One of the challenges of working in a relatively small market is that one tends to revolve through the same doors – having got a “not now” response i tend to come back six…
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