In recent years, banks have benefited from a certain “tolerance” by regulators regarding the quality of risk data and risk reporting. But that period is over. The ECB is clear: regulations on this matter have existed for some time; now is the moment to truly apply them. Imagine a financial institution under stress: markets are […]
In the fascinating world of Data Management, curious stories often circulate that highlight the importance of data quality. Sometimes they even make us smile—until it happens to us or one of our clients, perhaps in the dreaded production environment! As we enjoy the summer, why not have some fun with quirky anecdotes that remind us
According to the Global Data Management Community (DAMA), Data Quality consists of “the planning, implementation and control of the activities that apply quality management techniques to data, in order to assure it is fit for consumption and meets the needs of data consumers.” But why should a company adopt a Data Quality system? According to
Data Quality: what it is, why adopt it and how to apply it Read More »
Augmented Data Quality means applying advanced functions to automate some Data Quality (DQ) processes with the help of “active metadata” and such technologies as Artificial Intelligence and Machine Learning. Many DQ tasks can be automated, for example profiling, data matching, automatic linking between entities, merging, cleansing, monitoring, automatic alignment between business and IT control rules,
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Augmented Data Management uses advanced technologies to optimize and improve data management processes in a company. Data Management (DM) is an essential enabling factor for analytics, Data Science, Regulatory Reporting and digital business transformation. This practice allows collecting and storing, transforming and using the data in compliance with policies and regulations. It helps to quickly
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