Data Science and Agile Systems for Product Management

(3 customer reviews)

21,231.64

Category:

Description

Modern systems must be designed for agility to outpace the competition. Concepts like Agile, DevOps, and Data Science were once considered only for technology-based companies. Today, that means every company. There is no greater currency than timely information for optimizing operations and meeting customers’ needs.

Modern product management requires every development and operations value stream to be identified and continuously improved. This means using Lean and DevOps principles to streamline handoffs and information flows across teams. It means reorienting towards self-service and automation wherever possible. To avoid incrementalism means a robust Agile development process to keep innovations critical and aggressive enough to make noticeable improvements in value delivery.

Agile systems in a DevOps environment require products to be built entirely differently from traditional designs. Modularity, open set architectures, and flexible data management paradigms are a starting point. The product’s evolutionary nature with so much change enables functionality, design, and technology to drive and influence each other simultaneously. Beneath it is a data collection and feedback loop essential for anticipating and reacting to business needs for operations and marketing.

Data science and analytics are the lifeblood of any product organization and enable product managers to tackle risks early. New technologies allow us to collect and integrate data without extreme upfront constraints and onerous controls. This means all data is fair game and, when tagged and stored correctly, can be made available at nearly any scale for preparation, visualization, analysis, and modeling.