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Last Updated Date Mar 26, 2024 |


Following a proven implementation methodology ensures the success of the analytics project and maximizes your available resources. As the build phase begins, start by reviewing the project scope and plan developed during the planning phase. You may implement working with your own IT team or with a team of consultants.

Implementing next generation analytics consists of several key steps: data acquisition and storage, building the enterprise data catalog, data organization and governance, data modification and manipulation, and data operationalizing and publication.

Data may be acquired and ingested from enterprise applications, sensors and other devices, external feeds, and other analytic systems. Data may be acquired via batch, near real-time and real-time streaming. Hadoop is typically used for storage, and it can be implemented in a private data center or in the cloud. An enterprise data catalog automatically classifies and catalogs all data, so you have a complete view of your data so that data can be enriched, searched, and governed. A master data management solution will enable you to link all of your data with a common reference.

Establishing governance policies is critical to that data is used in accordance with corporate and regulatory data privacy mandates. The data science team must also build next-generation analytics models. Finally, the solution can be put into operations, and the IT team typically takes over the primary responsibility.

Testing and tuning is critical. Key stakeholders should be involved in acceptance testing to ensure that analytics models and data lake meet the business objectives. Establish the overall test strategy, including the user acceptance testing plan. Determine the performance benchmarks, and test thoroughly to ensure that these will be reached in a production environment. User training is also critical to facilitate acceptance and adoption.

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