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Given that we have begun to think of re-opening the economy in the United States, I have been thinking of what the next few months and years would look like in terms of return to normalcy. I see two camps of people, one which is optimistic that things will return back to normalcy pretty soon. And, the other which thinks that things will never be the same as before.

The two major factors in terms of return to normalcy will be (1) how the governments, both at the federal and state level contain the health risks of the virus and thus, how they re-open the economy based on that, and (2) how consumer behavior changes in response to…


As we embark into a new decade, I was thinking about how the role of Product Management has transformed in the past 10 years. On a day to day basis, it doesn’t seem like much has changed at all, but thinking back to how PMs in the average company built products in the 2000s vs what they do now, there is indeed a sea change.

The trends I picked below are ones that affected the whole industry, not just a few companies. These are trends that transformed “how” PMs operate, not just what they work on. Arguably, some of these trends started at some companies sooner than later, especially the ones that were early adopters of the shift in some of the technology changes that drove these changes. …


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The decision making framework you build for your team/organization is going to be critical to its success or failure. In the era of the internet, data has become a key part of the decision making frameworks in companies. And, rightfully, so. Data is based in reality — what’s actually happening. And, your decisions should be grounded in reality and facts as much as possible. However, how you use data in your decisions also matters. And, that can have long term downstream impact on the quality of the product you build.

Data driven vs Data informed

Sometimes people think the difference between being ‘data driven’ and ‘data informed’ is like potayto potahto. Some organizations call themselves data driven, others call themselves data informed. …


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Persona based design thinking has been in vogue for a long time. I believe the origins of persona-driven design started in marketing and then moved into digital product design as well. While appealing in theory, I have found persona-based design to be quite challenging because it tries to define a set of average users of your product. I run into the following challenges usually

1) Defining an average user is tough. Especially in products that have millions of users. We come up with profiles, behaviors etc. from UX research but how do you know if they are truly representative of all the users who use your product.

2) Confusing user experience. As you start layering features on your product, it becomes hard to optimize your product for all the personas that it targets. This ultimately leads to a confused design that works for no one.


A technique I learned at Stanford Design School that I love to follow is to design for the extreme user. …


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There is a management crisis in organizations. If you ask anyone why they are unhappy at work, most would talk about bad management. …


This is a continuation of the three part series on machine learning for product managers.

The first note focused on what problems are best suited for application of machine learning techniques. The second note talked about what additional skill-sets a PM needs when building machine learning products. This note will focus on what are the common mistakes made in building ML products.

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The goal of the note is to provide someone with limited ML understanding a general sense of the common pitfalls so that you can have a conversation with your data scientists and engineers about these. …


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This is a continuation of the three part series on machine learning for product managers.

The first note focused on what problems are best suited for application of machine learning techniques. This note would delve into what additional skill-sets a PM needs when building products that leverage machine learning

As I had mentioned in Part I, the core skill sets required of a PM do not change whether you work in a machine learning driven solution space or not. Product managers typically use five core skills — customer empathy/design chops, communication, collaboration, business strategy and technical understanding. Working on ML will continue to leverage these skills. One area that does get stretched more is technical understanding, specifically of the machine learning space. It’s not to say that you cannot be a ML PM unless you have deep technical chops. …


Machine learning(ML) and AI are hot topics these days. So, I find a lot of product managers and would-be product managers come up to me and ask how they can become better ML PMs. Since the intersection of machine learning and product management is a fairly comprehensive topic, one post would not do justice to it. Hence, I am planning to break it down into three parts

Part I — Problem Mapping: What types of problems are best suited for machine learning

Part II — ML Skills : What additional skill-sets does a PM need in building products that leverage machine…


Snap Inc (Snapchat’s parent company) listed its stock on March 2, 2017 in the NYSE exchange. It was one of the most awaited IPOs of this year after an uneventful 2016 where the number of tech IPOs hit their lowest point ever in the decade. It didn’t disappoint as its shares popped 11% on the 2nd day of trading.

Snap’s S1 SEC filing provides quite a lot of insight about its current business. However, what exactly Snap aims to be in the future has been very cryptic. It calls itself a “Camera” company but it has left a lot of people scratching their heads as to what that really means. …

About

Uzma Barlaskar

Growth @WhatsApp. PM @Facebook. Former Entrepreneur (CEO, PatternEQ). Stanford MBA. Still an engineer at heart . Find me on twitter: @uzmabarlaskar

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