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How to Become a Data Product Manager Without Experience

The Product HQ

Do you want to become a data product manager? As the world moves towards a data-driven economy, businesses are keen on data trends and hiring people who know the ins and outs of data. Big data analytics is a growing global market expected to reach $550 billion by 2028. Otherwise, skip ahead.

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Why marketers should choose Mixpanel over Google Analytics 4 (GA4)

Mixpanel

Growing a digital product and company is a multi-team sport. For marketing teams focused on getting more user traffic and signups, Google Analytics has been the tool of choice. This is why we launched Mixpanel Marketing Analytics.

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Getting Started with Product Data Management

Amplitude

The power of product analytics becomes apparent when any team at a company—marketing, product, design, engineering—can quickly ask and answer questions about user behavior and customer journeys. But getting to that point requires an investment in product data management. They trust the data, and they can act quickly.

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User interviews and product analytics: My methods for how and when to use each

Mixpanel

As a product leader at Mixpanel, I can go on and on about the value of using product analytics in my work. ( Below are some of my best practices on wielding these two tools together, with a little added guidance on avoiding common misfires. Why use both user interviews and product analytics? But analytics come in the next stage.

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Data-Informed Retrospectives

The Product Coalition

TL; DR: Data-Informed Retrospectives In their book Agile Retrospectives , Esther Derby and Diana Larsen popularized the idea that a Sprint Retrospect comprises five stages. The second stage refers to gathering data so that the Scrum Team can have data-informed Retrospectives. Source : Scrum Guide 2020.

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Popular data validation techniques for analytics, and why you need them

Iteratively Blog

At the end of the day, your data analytics needs to be tested like any other code. If you don’t validate this code—and the data it generates—it can be costly (like $9.7-million-dollars-per-year To avoid this fate, companies and their engineers can leverage a number of proactive and reactive data validation techniques.

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Why data teams struggle with data validation (and how to change that)

Iteratively Blog

Chances are, you’ve probably heard that phrase in relation to your data hygiene. But how do you fix the garbage that is bad data management and quality? Especially if you don’t have control over the implementation of tracking code (as is the case with many data teams). Well, it’s tricky. according to IBM.