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Last Updated Date Mar 26, 2024 |
optimize stages


Work with stakeholders to identify what is working well from their perspective, and to identify new analytics use cases and applications.

Starting small and demonstrating success to the business is an effective way to reap the full value of the data lake investment. Early projects may focus on a small constituency of data analysts and developers. Later phases may include self-service that address the needs of business users. The business value and ROI of next generation analytics is not complete until the full range of users is enabled.

You may expand analytics for other functions or business units. You may continue to refine the analytics models or create new ones. Data continues to evolve and grow, so you may add new data sets or sources into the data lake, such as human language text from the call sensor or real-time sensor data. Line-of-business managers may reengage – or engage for the first time – to proactively deliver outcomes and drive greater use of analytics across the organization and maximize your analytics investment.

Above all, be diligent about policy-based data governance. A common criticism of a data lake is that it becomes a dumping ground for data of varying quality, and quickly becomes a data swamp. By curating this raw data, data scientists can create useful models for different business contexts.

Always be on the lookout for ways to minimize operational costs. For instance, data storage can be a significant portion of the cost associated with analytics.

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