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Experimentation and Testing: A Primer

Occam's Razor

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers.

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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

First, don’t do something just because everyone else is doing it – there needs to be a valid business reason for your organization to be doing it, at the very least because you will need to explain it objectively to your stakeholders (employees, investors, clients). Test early and often. Test and refine the chatbot.

Strategy 290
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Pitching a DataOps Project That Matters

DataKitchen

Your data consumers are focused on business objectives. They need to grow sales, pursue new business opportunities, or reduce costs. What would it mean for a company to lead its industry in savvy and business agility? Impactful DataOps projects are those that help colleagues and executives pursue their objectives.

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Bringing an AI Product to Market

O'Reilly on Data

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. Some of the best lessons are captured in Ron Kohavi, Diane Tang, and Ya Xu’s book: Trustworthy Online Controlled Experiments : A Practical Guide to A/B Testing.

Marketing 364
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Companies to shift AI goals in 2025 — with setbacks inevitable, Forrester predicts

CIO Business Intelligence

The rest of their time is spent creating designs, writing tests, fixing bugs, and meeting with stakeholders. “So Forrester predicts that vague business objectives and premature integration in decision-making will create confusion when it comes to leveraging AI agents.

ROI 127
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Data Quality Power Moves: Scorecards & Data Checks for Organizational Impact

DataKitchen

Why is the change necessary (alignment with business objectives or regulatory compliance)? Continuous feedback loops : DataOps emphasizes short feedback cycles, allowing teams to test data quality improvements and quickly refine them based on real-world outcomes. How the change should be communicated and implemented.

Scorecard 177
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6 enterprise DevOps mistakes to avoid

CIO Business Intelligence

Rick Boyce, CTO at AND Digital, underscores how a typical IT project mentality toward DevOps can undercut the CIO’s ability to deliver on business objectives. Applications sanctioned for frequent, continuous deployments should have robust continuous testing, enhanced observability, and a canary release strategy to minimize risks.