Top Industry Practices toward an Agile Data Pipeline

By StartUp City | Wednesday, May 15, 2019

Current business scenario demands agility along with an in-depth analysis and management of data. Business decisions based on insights gained by encompassing data across various departments often pervade the unpredictable economic instabilities. Such decisions are impactful and resonate with the bigger picture of an organization. But such an arrangement requires a simple yet comprehensive data distribution that can be securely accessed. And that's where an agile data pipeline fits in.

Building an agile data pipeline is a daunting task. It requires multiple stages that must incorporate various stakeholders within the business. But mostly, the enterprises end up with insurmountable heaps of data furthering complicating data management.

Here are a few practices toward an agile data pipeline:

Data Catalog

Data Catalog involves storing, finding, and loading of data. It's an organized data management solution that helps the businesses to explore data sources and understand them. The sorted data saves time as the user has better data access in a cataloged list as compared to the non-cataloged list. However, to maximize the advantages of data catalogs, an organization needs to focus on people as well while promoting the use of such practices.

Discover Together

Data discovery is an important aspect required for business insights and has the potential to add tremendous value to the organization. Pushing such a platform that enables event data lineage and adds visibility is the crucial step toward effective data governance. It's an essential step before an organization can promote the culture of collaboration among its data stakeholders.

Data Culture

Searching the content, understanding the context, and using community interactions to gain trust in the results from data analysis is a healthy way of data consumption. Thus, the organization should identify those that contribute the most toward this culture. Moreover, the right data culture engages the people involved and promotes knowledge sharing among them.

Collaboration

During pre-data-focused days, mismanagement and duplication of data often led to deadlocks citing the crucial business decisions. Moreover, data and the resulting information tend to proliferate. Therefore, promoting social interaction among the users and enabling sharable platforms reduces the risk of redundancy and enhances impactful business decisions.

As already stated, an agile data pipeline is a process that needs the active engagement of an organization along with the various stakeholders involved. Initially, it seems daunting but gives dividends in the long run.

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