3 ways to apply agile to data science and dataops

Just about every organization is trying to become more data-driven, hoping to leverage data visualizations, analytics, and machine learning for competitive advantages. Providing actionable insights through analytics requires a strong dataops program for integrating data and a proactive data governance program to address data quality, privacy, policies, and security.

Delivering dataops, analytics, and governance is a significant scope that requires aligning stakeholders on priorities, implementing multiple technologies, and gathering people with diverse backgrounds and skills. Agile methodologies can form the working process to help multidisciplinary teams prioritize, plan, and successfully deliver incremental business value.

Agile methodologies can also help data and analytics teams capture and process feedback from customers, stakeholders, and end-users. Feedback should drive data visualization improvements, machine learning model recalibrations, data quality increases, and data governance compliance.  

Defining an agile process for data science and dataops

Applying agile methodologies to the analytics and machine learning lifecycle is a significant opportunity, but it requires redefining some terms and concepts. For example:

  • Instead of an agile product owner, an agile data science team may be led by an analytics owner who is responsible for driving business outcomes from the insights delivered.
  • Data science teams sometimes complete new user stories with improvements to dashboards and other tools, but more broadly, they deliver actionable insights, improved data quality, dataops automation, enhanced data governance, and other deliverables. The analytics owner and team should capture the underlying requirements for all these deliverables in the backlog.
  • Agile data science teams should be multidisciplinary and may include dataops engineers, data modelers, database developers, data governance specialists, data scientists, citizen data scientists, data stewards, statisticians, and machine learning experts. The team makeup depends on the scope of work and the complexity of data and analytics required.

An agile data science team is likely to have several types of work. Here are three primary ones that should fill backlogs and sprint commitments.

1. Developing and upgrading analytics, dashboards, and data visualizations

Data science teams should conceive dashboards to help end-users answer questions. For example, a sales dashboard may answer the question, “What sales territories have seen the most sales activity by rep during the last 90 days?” A dashboard for agile software development teams may answer, “Over the last three releases, how productive has the team been delivering features, addressing technical debt, and resolving production defects?”

Copyright © 2020 IDG Communications, Inc.

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