Agile Software Development

The Uncharted Prospects in Agile DataOps

In the competitive technology industry, agile principles have become a prerequisite, mainly if you aim to leverage data science (DS) projects. Most of these projects do not have certainty. They require trial and error to learn different paths and unique techniques. In this article at InfoWorld, Isaac Sacolick explains that organizations trying hard to become data-driven must leverage data visualization. Use analytics and machine learning to get actionable insights for the DataOps program and get a competitive edge.

Agile Execution

Agile methodology forms a working prototype that helps multidisciplinary teams prioritize, plan, and accomplish the delivery of incremental business values. DataOps itself is an agile approach to develop and deploy data-intensive applications like data science and machine learning. Its workflow encourages cross-functional collaboration. It prioritizes people and processes to empower platform technologies. It also allows each collaborating group to boost productivity by focusing on the core competencies that enable an agile workflow.

Redefining Vital Terms

Executing agility for the analytics and machine learning lifecycle requires reevaluation of the significant terms and concepts. For instance, analytics owners must lead the agile data science team. They must take responsibility to drive business outcomes from the insights. Also, capture the underlying needs of all the deliverables in the backlog. The agile data science team should execute backlogs and sprint commitments. Take a look at the primary requirements:

  • The team must adapt dashboards to help end-users’ queries. Enhance productivity to deliver features, address technical debt, and resolve production defects.
  • The practice of developing analytical and machine learning models involve segmentation and data tagging. It also requires operating data sets to configure multiple algorithms. Thus, an agile data science team must track user stories for new development and offer new experiences.
  • The team must search for new data sources to integrate strategic data warehouses and data lakes. The analytics owners should fill agile backlogs with story cards to research new data sources, validate sample data sets, and integrate prioritized repositories. It would help the agile teams integrate new data sources while automating and implementing data validation and quality rules.

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