In a data-intensive industry like finance, data comes from numerous entries and goes to numerous exits. Such status quo can easily, and almost inevitably, lead to chaos in data analysis and management. For example, analysts from different business lines define their own financial metrics in data reports. When you pool these countless reports together in your data architecture, you will find that many metrics overlap or even contradict each other in definition. The consequence is, developing a simple data report will require lots of clarification back and forth, making the process more complicated and time-consuming than it should be.

As your business grows, your data management will arrive at a point when “standardization” is needed. In terms of data engineering, that means you need a data platform where you can produce and manage all metrics. That’s your architectural prerequisite to provide efficient financial services. 

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