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InfoQ Homepage Articles Article Series: Data-Driven Decision Making

Article Series: Data-Driven Decision Making

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The Data-Driven Decision Making Series provides an overview of how the three main activities in the software delivery - Product Management, Development and Operations - can be supported by data-driven decision making.

It consists of several articles, each highlighting an area where data-driven decision making can be applied:

  • In Product Management, Hypotheses can be used to steer the effectiveness of product decisions.
  • In Development, Continuous Delivery Indicators can be used to steer the efficiency of the development process.
  • In Operations, SRE’s SLIs and SLOs can be used to steer the reliability of services in production.

In the series we also show how applying hypotheses, CD indicators and SRE’s SLIs / SLOs at the same time enables the software delivery organization to optimize for effectiveness, efficiency and service reliability in parallel.

Series Contents

1

Data-Driven Decision Making – Product Management with Hypotheses

In Product Management, hypotheses can be used to steer the effectiveness of product decisions about feature prioritization. 

2

Data-Driven Decision Making – Product Development with Continuous Delivery Indicators

In Development, Continuous Delivery Indicators can be used to steer the efficiency of the development process.

3

Data-Driven Decision Making – Product Operations with Site Reliability Engineering

In Operations, SRE’s SLIs and SLOs can be used to steer the reliability of services in production.

4

Data-Driven Decision Making – Optimizing the Product Delivery Organization

Applying Hypotheses, CD Indicators and SRE’s SLIs / SLOs enables a software delivery organization to optimize for effectiveness, efficiency and service reliability.

5

Data-Driven Decision Making - Software Delivery Performance Indicators at Different Granularities

Optimizing a software delivery organization is not a straightforward process standardized in the software industry. Getting the organization to analyze the data and act on it is a difficult undertaking. This article presents insights into how a socio-technical framework for optimizing a software delivery organization has been set up and brought to the point of regular use.

Series Author

Dr. Vladyslav Ukis graduated in Computer Science from the University of Erlangen-Nuremberg, Germany and, later, from the University of Manchester, UK. He joined Siemens Healthineers after each graduation and has been working on Software Architecture, Enterprise Architecture, Innovation Management, Private and Public Cloud Computing, Team Management and Engineering Management. In recent years, he has been driving the Continuous Delivery and DevOps Transformation in the Siemens Healthineers Digital Ecosystem Platform and Applications - "teamplay". In this capacity he has been helping a large, distributed and rapidly growing development organization adopt new ways of working, adapt the architecture and achieve culture changes required to keep the system throughout its development always in a releasable state reaching Continuous Delivery and to operate the system in Production reliably to the delight of its users reaching DevOps.

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