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As Banks and Insurance firms have already embraced Data Lakes for their Artificial Intelligence and Machine learning capabilities, it is important to look for continuous Return on Investment on the platform.
If a Data Lake is not well maintained, it can turn into a swamp while finding usable data can confuse the data consumers. Most challenges can be solved by including an active platform governance of the Data Lake.
A data lake as a distributed file system hosts authoritative copies of source data having a variety of data that include assorted formats including structured, semi-structured formats like a JSON, XML and unstructured data like images, audio.
Accumulating technical debt with business use-cases will often lead to increased up-front costs during migration and maintenance costs of existing data.
Lack of data-trust often leads to consumers getting their own copies of data onto the data lake though they might exist already. However, due to lack of self-service discovery capabilities – other consumers might not be able to find the right dataset.
The focus areas of a data lake Technology operating model should be on the below aspects of Data Management –
This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author.
Roman Eloshvili Founder and CEO at XData Group
06 December
Robert Kraal Co-founder and CBDO at Silverflow
Nkiru Uwaje Chief Operating Officer at MANSA
05 December
Ruoyu Xie Marketing Manager at Grand Compliance
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