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”,…
But to business, having customer data consolidated into a master data management application often leaves a bad taste.
What makes the difference, for any AI project, is the quality and quantity of data that is available to feed the model.
Concerns about the quality of data are inhibiting the adoption of AI in South Africa and world wide – access the report
Learn how DNB is using data to reinvent itself as a customer-centric, digital bank
Most companies would acknowledge that quality customer data is important. In most cases, however, customer data quality is not good enough. Why? Changing business requirements Most businesses have a history. Customer data may have been accumulated over years, or decades. Data that was captured in the past may not have complied with the requirements of…
