Things to Keep in Mind for a Solid EDW Transformation Strategy

by Dynamix Group Writer

In the past few decades, organizations worldwide have invested heavily in an enterprise data warehouse (EDW) as their chosen platform for managing the growing volume of disparate digital data. However, amid escalating resource constraints and financial challenges, many organizations are reevaluating their options and questioning the capability of a traditional enterprise data warehouse in meeting their data management and analytics needs. To be more precise, enterprise data platforms must:

· Be scalable

· Deliver business intelligence to support increased profitability, mitigate risks, and enhance quality

· Effectively balance analytics capabilities with transaction-based functionality

Keeping the points mentioned above in mind, many organizations are moving to Big Data. However, making a move from EDW to big data can be a daunting task. An effective data migration strategy, along with a thorough understanding of requirements, processes, and scenarios, is essential for a smooth transition. Additionally, organizations need to be well-equipped with plans to deal with different risks such as data loss, or in the worst-case scenario – failed implementation.

Before taking the plunge to kick start their transformation journey, enterprises must also clearly establish the business requirements and end goals of their EDW transformation. some important questions/things you must consider for a robust enterprise data warehouse transformation strategy are as follows:

Why?

The first thing to consider is the reason behind transitioning from an enterprise data warehouse to big data.

Cost and Capacity:

You must analyze if your organization is primarily looking forward to freeing up some premium storage capacity and reduce the recurring cost of operations and ownership.

Development/Test Cycles:

You must choose a data migration process that can avoid lengthy, complex, and error-prone development, testing, and verification cycles. This is possible by selecting a validated, automated approach.

Agility:

How can the business agility be boosted and the architectural elasticity and scalability of business be prioritized is another question to be kept in mind.

Positioning:

Finding out a way to mitigate risks, reduce efforts, and save time by employing data-driven assessments and insight-driven recommendations.

Going Code-Free:

The skillset gaps can be avoided and the risks associated with manual logic transformation can be reduced by going code-free.

Existing Investments:

It must be analyzed whether EDW investments can be reused by transforming not just the data but also scripts, reports, views, business logic code, and more.

Driving Innovation:

One can stay ahead in the run and improve the data accessibility across the enterprise with the help of an innovation agenda.

A Proven Solution:

Reflecting upon the need for a platform that’s reliable, proven, fully automated, and capable of transforming all the required workloads is of great prominence.

Optimizing Efficiency:

Thinking about how to optimize the IT team’s productivity by simplifying, automation, and de-risking transformation of ED, ETL, reporting, and analytical workloads.

Hybrid Approach:

Considering an optimized performance approach for on-premise, cloud and hybrid strategy can greatly advance your EDW transformation process.