How to Address Big Data Challenges (PART 5 of 6)

Continution to previous post - How to Address Big Data Challenges (PART 4 of 6)

Managing large-scale data environments

As big data applications spread across more systems, data governance challenges become more difficult to solve. 

This challenge is exacerbated by the fact that modern cloud architectures allow businesses to acquire and store all of the data they collect in its raw form. Protected information fields might end up in a variety of applications via accident.

Much of the benefit of broader, deeper data access might be lost without a data governance policy and rules.

Treating data as a product with built-in governance standards from the start is a good approach. It will be easier to provide self-service access that does not require monitoring of each new use case if more work is spent up front identifying and handling large data governance challenges.

Ascertaining that the data context and use cases are comprehended

Enterprises also have a tendency to place too much emphasis on technology without fully comprehending the context of the data and its commercial applications.

When it comes to large data storage structures, security frameworks, and ingestion, there is frequently a lot of care put into it, but very little thought goes into onboarding users and use cases.

Teams must consider who will refine the data and how they will do it. To control risk and guarantee correct alignment, individuals closest to the business concerns must cooperate with those closest to the technology. 

This entails considering ways to make data engineering more democratic. To obtain early successes, understand the restrictions, and engage consumers, it's also beneficial to build out a few simple end-to-end use cases.

To be continued... How to Address Big Data Challenges (PART 6 of 6)