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Bringing an AI Product to Market

O'Reilly on Data

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 362
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Do You Need a DataOps Dojo?

DataKitchen

We’ll also discuss building DataOps expertise around the data organization, in a decentralized fashion, using DataOps centers of excellence (COE) or DataOps Dojos. Centralizing analytics helps the organization standardize enterprise-wide measurements and metrics. Test data management and other functions provided ‘as a service’ .

Metrics 243
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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. That metric is tied to a KPI.

Metrics 156
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Higher-ed CIOs embrace academia’s AI challenges

CIO Business Intelligence

One is knowledge of the emerging mega trends in technology — data, AI, and machine learning — and the other is understanding organizational culture needed to advance the technology goals and to inspire contributors,” he says. We’ve done a lot of experimentation on these adaptive tools that use AI,” says Ventimiglia.

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Keynote Takeaways From Gartner Data & Analytics Summit

Sisense

Every year there’s high anticipation to see what key message Gartner will present in the yearly Data & Analytics Summits. It’s always fun and insightful to be able to talk to so many CDOs, CIOs, data and BI professionals within 2.5 At Sisense we’ve been preaching for BI prototyping and experimentation for quite a while now.

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Web Analytics 2.0 Book: In Stores Now!!

Occam's Razor

I am absolutely thrilled that my book Web Analytics 2.0 The waterfall of positive feeling stems from the fact that this book was very hard to write. I only had one job, at Intuit, when I wrote my first web analytics book. The Pitch: I invite you to consider buying my second web analytics book. Request for help.

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Understanding Simpson’s Paradox to Avoid Faulty Conclusions

Sisense

Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. One of the simplest ways to start exploring your data is to aggregate the metrics you are interested in by their relevant dimensions. How can good data lead to faulty conclusions? How does this happen? 9/10 = 90%.

Testing 104