Welcome to 2020 everyone Kicking off by borrowing a look forward from our partner, Syncsort How to Modernize Your Traditional Database in 2020 If your data center is like many others, you have several important applications that rely on traditional database implementations. For example, it’s not unusual for companies to have business-critical applications that use…
Today I overheard someone say, “Most business users give up trying to find or understand the data they need to do their jobs within 30 minutes of trying.” I gave up after 3 minutes of trying to find its source The digital business is dependent on data Self-service BI is dependent on data. Business goals,…
Cloud computing offers new opportunities and challenges to enterprises grappling with the explosion of data sources, growing data storage needs, and the problem of ensuring data quality.
data quality, hyperscale and Python – the foundation of machine learning
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…
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
