Remove Development Remove Engineering Remove Metrics
article thumbnail

What Inclusive Urban Development Can Look Like

Harvard Business

One of us is an urban theorist, the other a community-focused real estate developer. In order to achieve these goals, new, non-traditional metrics were needed to track the project’s community impacts while making sure that investors and capital partners were still accomplishing their financial objectives.

article thumbnail

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
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Using AI and Machine Learning for Agile Development and Portfolio Management - SPONSOR CONTENT FROM CA TECHNOLOGIES

Harvard Business

But, what about the application of AI and ML to agile development, testing and even portfolio management? For nearly two decades, many companies have utilized the principles within the Agile Manifesto to deliver faster time-to-market than traditional, or linear development models. Focus on Outcomes, not Metrics.

Agile 104
article thumbnail

The Kinds of Data Scientist

Harvard Business

These data scientists design, define, and implement metrics, run and interpret experiments, create dashboards, draw causal inferences, and generate recommendations from modeling and measurement. Modeling scientist: Direct improvements in the product or business from the code developed and shipped. Decision scientist: Humans.

Data 132
article thumbnail

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. But Bernstein and his team observed that when managers were not watching, employees secretly developed and shared better ways of doing the work. Metrics emphasized speed. Every spot on every line was visible to managers.

Metrics 134
article thumbnail

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. ”We didn’t think a recommendation engine was necessary at the time,” said one of the company’s web developers.

article thumbnail

Scaling Customer Service as Your Startup Grows

Harvard Business

Don’t obsess over metrics like inquiry volume or time to close tickets. Don’t let your engineers hack together workarounds that will need to be maintained down the road; provide real engineering solutions to the types of problems new software has. What not to do. Don’t optimize for efficiency. What to do.

Metrics 131