Systems | Development | Analytics | API | Testing

This is the second post in a series about data modeling and data governance in the cloud from Snowflake’s partners at erwin. See the first post here. As you move data from legacy systems to a cloud data platform, you need to ensure the quality and overall governance of that data. Until recently, data governance was primarily an IT role that involved cataloging data elements to support search and discovery.

In recent years, organizations have been making massive investments in data analytics to transform their growing volume of data into actionable insights to inform decision-making. However, the pursuit of becoming data-driven has uncovered challenges earlier in the data pipeline that are preventing companies from reaping all the benefits from their data.

One of the most important shifts of the past few years in finance and banking was the movement from primarily branch-based banks to mobile-first banks. While these innovative products simplify the end-user experience, they also bring up more security concerns, since digital channels expose a number of vulnerabilities. These apps deal with sensitive user data, such as private financial or personal information, which means that the prevention of any type of breach is of utmost importance.

Since 2013 the UK Government’s flagship ‘Cloud First’ policy has been at the forefront of enabling departments to shed their legacy IT architecture in order to meaningfully embrace digital transformation. The policy outlines that the cloud (and specifically, public cloud) be the default position for any new services; unless it can be demonstrated that other alternatives offer better value for money.

Software vendors often ask me when is the ideal time to swap out a legacy analytics solution that they’ve embedded into their application. There's five key signs that can tell you your embedded solution isn’t right anymore and it’s time to switch.