Remove Data Collection Remove Data Quality Remove Experimentation Remove Metrics
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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 361
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What you need to know about product management for AI

O'Reilly on Data

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. If you can’t walk, you’re unlikely to run.

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6 Case Studies on The Benefits of Business Intelligence And Analytics

datapine

It’s all about using data to get a clearer understanding of reality so that your company can make more strategically sound decisions (instead of relying only on gut instinct or corporate inertia). Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data.

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Product Management for AI

Domino Data Lab

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Another pattern that I’ve seen in good PMs is that they’re very metric-driven.

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Dear Avinash: Attribution Modeling, Org Culture, Deeper Analysis

Occam's Razor

The questions reveal a bunch of things we used to worry about, and continue to, like data quality and creating data driven cultures. That means: All of these metrics are off. This is exactly why the Page Value metric (in the past called $index value) was created. "Was the data correct?" EU Cookies!)

Modeling 124
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Five Key Elements For A Big Analytics Driven Business Impact

Occam's Razor

Data quality plays a role into this. And, most of the time, regardless of the size of the size of the company, you only know your code is not working post-launch when data is flowing in (not!). You got me, I am ignoring all the data layer and custom stuff! All that is great. For most of us, you plus the CMO/equivalent.].

Analytics 141
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Themes and Conferences per Pacoid, Episode 6

Domino Data Lab

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.