11 points to consider for a solid EDW transformation strategy
Making the move from EDW to big data can be daunting. A thorough understanding of requirements, possible scenarios, and processes is crucial to ensure a smooth transition. Organizations must also be equipped to deal with risks such as data loss, and even worse - failed implementation.
But even before enterprises embark on their transformation journey, they must clearly establish the business need and end goal of their EDW transformation. Here are 11 questions you must consider for a solid Enterprise Data Warehouse transformation strategy:
What is your fundamental business reason for an EDW transformation?
COST & CAPACITY
Is your organization looking to free up premium storage capacity and reduce recurring cost of ownership and operations?
How can you avoid the long, complex, and error-prone development, testing, and verification cycles by selecting an automated and validated approach?
How can architectural elasticity and scalability complement business priorities thus reducing time-to-market and boosting business agility?
How can data-driven assessments and insight-driven recommendations be employed to mitigate risks, save time, and reduce effort?
How can you overcome the skill set gap and the risks associated with manual logic transformation by going code-free?
Is it possible to reuse your EDW investments by transforming not just the data but scripts, views, reports, business logic and code, and more?
How can you drive the innovation agenda by improving data availability across the enterprise and staying ahead of the churn?
A PROVEN SOLUTION
Is there a platform that’s proven, reliable, fully automated, and capable of transforming all the required workloads?
How can you optimize IT teams’ productivity by automating, simplifying, and de-risking transformation of EDW, ETL, analytical, and reporting workloads to the big data warehouse?
How can an optimized performance be achieved for cloud, on-premise, and hybrid strategy?
An Automated EDW Assessment and Transformation Solution is the answer.
VP of Modern Data Architecture Practice