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Example: For a project to optimize supply chain operations, the scope might include creating dashboards for inventory tracking but exclude advanced predictiveanalytics in the first phase. Key questions to ask: What data sources are required? Are there any data gaps that need to be filled?
How to Scale Your DataQuality Operations with AI and ML: In the fast-paced digital landscape of today, data has become the cornerstone of success for organizations across the globe. Every day, companies generate and collect vast amounts of data, ranging from customer information to market trends.
Follow five essential steps for success in making your data AI ready with data integration. Define clear goals, assess your data landscape, choose the right tools, ensure dataquality and governance, and continuously optimize your integration processes.
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