Product Collective - Data science for Product Managers

The art and science of data science

Everyone is talking about AI (including us last week). What should probably get more discussion is the importance of the data you dump in the AI hopper. Of course, data is valuable even when you don’t use it to fuel AI but for more mundane uses like, oh, I don’t know, seeing how well your product is performing. With that in mind, this week, we look at the world of data science and its importance to your product management efforts.

Meanwhile, in product news, one company wants you to get money when AI uses your data, another company wants to charge you to share data, a third company wants you to use a design system, and the federal government really doesn’t want you to use a fourth company’s product.

Analytics and data science fundamentals for product managers. Data science and analytics play a very important role in Product Management. They help product managers make informed decisions, refine product performance, and create more effective strategies. Jayendra More provides an overview of analytics and data science fundamentals for product managers.

Leveraging data science in product management: The Rise of Data Product Managers. Data plays a big role in shaping successful products in today's digital era. As products become more sophisticated and data-driven, you’ve probably seen the value of harnessing data to make informed decisions, enhance existing products, and create entirely new ones. Vishal Ranjan Pandey explores the synergy between data science and product management and sheds light on the pivotal role data product managers play in ensuring product success.

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This Week’s Interview: Why more data can lead to worse decisions

Despite the increasing availability of data, people seem to struggle to make the right decisions. The human element is easy to ignore when you’re talking about AI, machine learning, or the latest tool. However, this is where things can easily fall apart for teams striving to be data-driven. We assume that more data will lead to more confidence. The more we know, the better, right? In theory, yes, but this doesn’t seem to work that way in practice.

In this discussion, Mike Belsito digs in on this challenge with Ruben Ugarte, Principal at Practico Analytics.

This video and many others like it are available on our Member Hub. If you don’t have access to the Member Hub, you can join the community today for free.

Wrapping up the New York Product Conference

Apple has been keeping its cards close to the chest when it comes to generative AI, but according to The Verge – recent developments suggest that the tech giant is focusing on making AI run efficiently on its devices. This approach aligns with Apple's emphasis on user privacy and local data processing.

The company recently released OpenELM (Open-source Efficient Language Models), a series of lightweight language models that perform remarkably well on text-related tasks like email composition. What's intriguing about OpenELM is its size – the largest model has only 3 billion parameters, making it smaller than most lightweight AI models. This efficiency is key to running AI smoothly on devices like iPhones and MacBooks.

As Product Managers, efficiency is crucial when delivering seamless user experiences. By prioritizing local processing, Apple ensures its AI features will run smoothly without relying on cloud servers, reducing latency and enhancing privacy.

Apple's Data Science teams likely play a critical role in optimizing these models, leveraging techniques like feature selection, dimensionality reduction, and model compression. By carefully curating and preprocessing the training data, they can create models that deliver impressive performance while maintaining a small footprint.

While Apple hasn't revealed the specifics of its generative AI plans, it has released various AI models and frameworks, hinting at potential applications in image editing, UI navigation, and even code completion. As Product Leaders, it's essential to keep an eye on these developments and consider how they might shape the future of our products.

As the AI landscape evolves, seeing how Apple's approach influences the broader product ecosystem will be fascinating. Apple may not be the very first to market – but they never really have been, have they? But when they arrive… they usually make a splash.

Data is the new oil; a startup wants to help you sell your own. From Big Tech firms to startups, AI makers are licensing e-books, images, videos, audio and more from data brokers, all in the pursuit of training up more capable (and more legally defensible) AI-powered products. Shutterstock has deals with Meta, Google, Amazon and Apple to supply millions of images for model training. At the same time, OpenAI has signed agreements with several news organizations to train its models on news archives. The individual creators and owners of that data often haven't seen a dime of the cash changing hands. A startup called Vana wants to change that. Pay to Post is now a thing. X is planning to charge new users a small fee to enable posting on the social network and to curb the bot problem. Musk said charging a small fee to new accounts was the “only way” to stop the “onslaught of bots.” Depending on how serious bot makers are to keep pushing out their misinformation, the change might have the unintended consequence of slowing new users that Musk wants to join the platform. Figma wants your organization to use its design system more. Design systems are core to Figma. Over the past few years, they’ve watched their community grow and the design systems they create become more powerful, flexible, and sophisticated. But with power and sophistication comes complexity, and they’ve found that one of the biggest challenges facing design systems managers today is gaining organizational adoption. To help design system managers encourage adoption, Figma launched several features designed to drive design system adoption across your team. We mean it this time. The US House of Representatives passed another divest-or-ban TikTok bill as part of a foreign aid package. That forced the Senate to stop kicking the TikTok can down the road. They approved the measure and President Biden signed it. That means China-based parent company ByteDance has to divest TikTok in nine months to a year in order to avoid an effective ban in the US.

THURSDAY, MAY 2, from 11 am EST

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Attending this event will give you insights from top-notch speakers like Petra Wille (Author of Strong Product People), Zane Van Dusen (AI Product Leader at Bloomberg), and many others.

You'll dive deep into topics such as:

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This is your chance to level up your product skills, network with like-minded professionals, and unlock your potential. Don't miss out on this opportunity.

Data products and their management. There are two categories of data operations where product managers sit in the data science realm. One is managing data and the potential of data to provide data as a service. The second is to manage services to capture, transform, and store data to use it to create models that produce valuable information. Chitransha Seth shares lessons learned from building data services and a SaaS platform that manages data as a service.

Data science: The driving force in modern product management and development. Data science emerges as the beacon guiding companies toward groundbreaking product advancements in an age where digital innovation is paramount. Andrii Shchur explores the journey of a transformative data science project aimed at refining and elevating a product. With data analytics, predictive modeling, and machine learning, businesses help you anticipate market trends, tailor user experiences, and streamline development workflows with unprecedented precision. Andrii shows how data science enhances product features and functionality and optimizes management strategies to keep pace with evolving consumer expectations and technological advancements.

Latest roles from our friends at The Home Depot All roles are full-time and featured on our Jobs Board.

​Data Product Management Manager - Personalization​

​SR. Product Manager - Voice IVR (Remote)​

​Product Manager​

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Resources and news curated by Kent J. McDonald.