Remove Engineering Remove Metrics Remove Productivity
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How to Build Great Data Products

Harvard Business

Products fueled by data and machine learning can be a powerful way to solve users’ needs. Classic examples include Google search and Amazon product recommendations, both of which improve as more users engage. Data products are a team sport. Develop the data-savvy of product and business groups.

Data 123
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WhatsApp Grew to One Billion Users by Focusing on Product, Not Technology

Harvard Business

That’s our product, and that’s our passion. It launched a product, gathered feedback, and kept iterating as it scaled and added users. At the other is a product or service that solves the problem or addresses the market in a way nobody has thought of before. Your data isn’t even in the picture.

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Don’t Be Tyrannized by Old Metrics

Harvard Business

While effective metrics are essential for focusing attention and achieving results, they can also overpower better sense. Most industries cower to a few central metrics, the yardsticks that define the winners and losers. Metrics tried and proven over years become a guide to what’s important, driving resource allocation.

Metrics 70
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The Kinds of Data Scientist

Harvard Business

One type of data scientist creates output for humans to consume, in the form of product and strategy recommendations. Data science for humans: the consumers of the output are decision makers like executives, product managers, designers, or clinicians. They are decision scientists. They are modeling scientists. What is the output?

Data 132
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Great Digital Companies Build Great Recommendation Engines

Harvard Business

Recommendation engines (or recommenders ) force organizations to fundamentally rethink how to get greater value from their data while creating greater value for their customers. Online recommendations, divorced from high-pressure sales pitches, proved an excellent gateway for industry customers to get up to speed on complex product lines.

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There Are Two Types of Performance — but Most Organizations Only Focus on One

Harvard Business

Every step of the process was measured, and real-time metrics were easily accessible. When Bernstein hid a set of production lines from managers’ view, the performance of employees on those lines increased by 10% to 15%. They will represent the products more consistently. Metrics emphasized speed.

Metrics 136
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Scaling Customer Service as Your Startup Grows

Harvard Business

Your #1 focus at this stage is product-market fit. Customers are integral to this process because they provide feedback, and every member of your team should be answering support requests, meeting with customers, and thinking about how to build a product that suits the needs of the market. What to do. What not to do. What not to do.

Metrics 132