Top 3 Data Architecture Strategies in the new cloud-led world

Businesses want to leverage data and do so rapidly with scalability and security. At?Supple.AI?we understand that the biggest challenge is to bring data together before it is useful for analytics or any AI/ML application. Here are the factors that we research in detail before starting an implementation.

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1.?Create a single source of data across the business unit. In essence, the principles of traditional enterprise data warehouses still apply. However, the traditional processes and tools are no longer effective and cost-efficient with infrastructure migrating to the cloud at a massive rate.

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2.?Identify varieties of data sources and real-time data updates?to correctly estimate the volume and velocity of data engrossing from transactional systems. Unlike the ERPs of yesteryears, today's enterprises have several data accumulation points. Be these the enterprise applications like CRM, PLM, HTMS, WMS or ERP, or various social presences that businesses maintain.

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3.?Deal with changing data sources. The transaction applications undergo changes in their data schemas and export formats continuously. In the real world, different versions of the schema effectively give rise to multiple ingress points. It's too expensive to discover these nuances once the implementation starts.