InfoQ Homepage Data Content on InfoQ
-
/articles/data-driven-decision-product-operations/en/smallimage/Product-Operations-with-Site-Reliability-Engineering-logo-1584955517034.jpg)
Data-Driven Decision Making – Product Operations with Site Reliability Engineering
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. In Operations, SRE’s SLIs and SLOs can be used to steer the reliability of services in production.
-
/articles/data-driven-decision-making-article-series/en/smallimage/Data-Driven-Decision-Making-Product-Management-with-Hypotheses-logo-1582896671856.jpg)
Article Series: Data-Driven Decision Making
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.
-
/articles/book-review-agile-machine-learning/en/smallimage/agile-machine-learning-logo-1581068455496.jpg)
Q&A on the Book Agile Machine Learning
The book Agile Machine Learning by Eric Carter and Matthew Hurst describes how the guiding principles of the Agile Manifesto have been used by machine learning teams in data projects. It explores how to apply agile practices for dealing with the unknowns of data and inferencing systems, using metrics as the customer.
-
/articles/data-driven-decision-product-management/en/smallimage/Product-Management-with-Hypotheses-logo-1582897683025.jpg)
Data-Driven Decision Making – Product Management with Hypotheses
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. In Product Management, hypotheses can be used to steer the effectiveness of product decisions about feature prioritization.
-
/articles/data-storytelling/en/smallimage/data-storytelling-logo-1568962410664.jpg)
How to Tell Compelling Stories Using Data: Q&A with Dr. Christine Bailey
The more evidence we have, the more likely our ideas are believed - or so we’re conditioned to think . But data doesn’t always engage people; this is where storytelling can help to combine data, insights, and emotion, said Dr. Christine Bailey. She presented techniques to tell compelling stories with data, and showed how that can increase our influence with external and internal stakeholders.
-
/articles/book-review-evidence-based-management/en/smallimage/book-review-evidence-based-management-s-1552675366460.jpeg)
Q&A on the Book Evidence-Based Management
The book Evidence-Based Management by Eric Barends and Denise Rousseau explores how to acquire evidence, appraise the quality of the data, apply it in your management decisions, and assess the impact of your decisions.
-
/articles/scaling-agile-data-driven-company/en/smallimage/scaling-agile-data-driven-company-s-1540327429583.jpeg)
Scaling Agile in a Data-Driven Company
The IT department of Cerved Group experimented with Scrum, Kanban, Lean, SAFe, and Nexus, to learn what works for them and fine-tune and continuously improve their way of working. In their transformation, they focused on the culture and mindset to cultivate high-performing teams, to improve the quality of products for customers, and to help managers transforming themselves in servant leaders.
-
/articles/managing-data-microservices/en/smallimage/logo-man-1523863324370.jpg)
Managing Data in Microservices
This article provides practical examples of how to manage data in microservices, with an emphasis on migrating from a monolithic database. It is recommended to build a monolith first, and only migrate to microservices after you actually require the scaling and other benefits they provide.
-
/articles/apache-sparkml-data-pipelines/en/smallimage/Spark-ML.jpg)
Big Data Processing with Apache Spark - Part 5: Spark ML Data Pipelines
With support for Machine Learning data pipelines, Apache Spark framework is a great choice for building a unified use case that combines ETL, batch analytics, streaming data analysis, and machine learning. In this fifth installment of Apache Spark article series, author Srini Penchikala discusses Spark ML package and how to use it to create and manage machine learning data pipelines.
-
/articles/personal-UX-widespread-global-mobilization/en/smallimage/logo-ux.jpg)
Personal UX -- Solving Unique Problems Created by Widespread Global Mobilization
Smartphone users are estimated to number 3.5 billion by 2019, and the different usages (mobile is most common during morning commutes and late at night, for example) create new challenges and opportunities. Data collection via our devices, smart-home gadgets and even our cars allows software engineers to offer increasingly personalized user experiences.
-
/articles/improving-data-management-DMM/en/smallimage/logo.jpg)
Improving Data Management with the DMM
The CMMI Institute has launched the Data Management Maturity (DMM)SM model. It can be used to improve data management, helping organizations to bridge the gap between business and IT. Using the DMM, organizations can evaluate and improve their data management practices. The model leverages the principles, structure, and proven approach of the Capability Maturity Model Integration (CMMI).
-
/articles/beyond-data-mining/en/smallimage/logo.jpg)
Beyond Data Mining
In this article, author talks about the need for a change in the predictive modeling community’s focus and compares the four types of data mining: algorithm mining, landscape mining, decision mining, and discussion mining.