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Data Lake for AI

The Need for Data Lake

Data in the digital world is increasingly becoming disparate in nature due to use of multiple, heterogeneous types of systems. It is impossible to manually aggregate all these data, analyze them, and learn from them. While the possibilities for utilizing the data in a productive manner is immense, the system disparities within the ALM & DevOps ecosystem hinder the utilization. A huge volume of data trapped in system silos leads to significant delays in identifying opportunities, problems, and trends. Data silos and system incompatibilities also obstruct real-time reporting and analytics. With the creation of a Data Lake, enterprises will be able to leverage cross-domain contextual data in multiple beneficial manners.

Image depicting creation of a Data Lake in an ALM ecosystem

How Data Lake is Beneficial for an Enterprise

Comprehensive Reporting and Analytics

Data Lake stores all data regardless of the source and structure. This means that enterprises can easily apply different types of analytics and create a comprehensive data reporting mechanism by working on a single data source repository.

Compliance Audits

With access to all data from a central place, it is easier for enterprise to adhere to the compliance requirements. End-to-end traceability for all data and related historical information allows enterprise to be compliance-audit-ready always.

Accelerate Functionality with Artificial Intelligence

The teaming-up of Data Lake and Artificial Intelligence can infuse enterprises with accelerated functionality. Data Lake aids in extracting hidden business insights and further provides agility by augmenting Artificial Intelligence to foresee trends and value from raw data as well.





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