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Mind the Product

Product Management

Building your own user experience and software stack: This week’s product news

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Why teams often struggle with OKRs: A conversation with Jeff Gothelf

Lessons from my first year as a Prod Ops Manager

How Google makes AI work at Enterprise scale – Miku Jha (Director, AI/ML and Generative AI, Google Cloud)

What are product features? Definition, examples, and 5 types

Annie Witcombe produces editorial for Mind The Product on a freelance basis. She has experience in conducting client research and telephone interviews, and she keeps relevant information accessible and up to date. 

Annie wrote for five business publications during the final year of her undergraduate degree, and she has worked on various stages of the editorial process since. The experience has sparked her enthusiasm for journalism, which she hopes to continue in her future career.

Annie Witcombe

<p><a href="https://www.linkedin.com/in/jenessalancaster/" target="_blank" rel="noopener noreferrer">Jenessa Lancaster</a> is a data scientist at Ocado Technology. In this ProductTank London talk, she discusses the importance of communicating data concepts to non-specialist audiences.</p>
<p>We live in promising times for data science in business development. Many organisations are increasing investments to their data science and machine learning departments, acknowledging the valuable insights that these areas offer, particularly in terms of problem solving. But there is still a gap between the work of data scientists and the engagement of the wider team; sometimes, it simply comes down to a lack of conversation. For Jenessa, communicating to non-technical, non-specialists is a crucial part of the job, and this is why she believes it is important&#8230;</p>
<h3>Data Science in Business</h3>
<p>Data science is a multi-disciplinary role, and data scientists come from many different backgrounds (computer science, analytics, and mathematics to name a few). In business, data scientists come together and act as the interface between technical people and a domain problem, just as product owners do. The job of the data scientist is to translate between these fields while trying to learn about the domain as much as possible: then, apply the data and find ways to make changes and progress. Jenessa believes that spreading awareness of data science will have a big impact on wider conversations in business and on the field in general. Essentially, the discussion of data can affect people’s perceptions of AI and machine learning, which may inspire great changes in company culture and resource allocation. According to Jenessa, the more we talk about data concepts, the more we can put it on people’s radar as we strive towards data-driven cultures that promote smart solutions.</p>
<h3>Communicating Data Science</h3>
<p>In business and retail, as a data scientist you may have a certain idea about how you think a business problem could be solved. In order to convince others of your solution, or prove that data and machine learning is valuable for your business, you may have to advocate for your work. Jenessa explains that experiments can be instrumental in generating proof of your concepts, and are very useful when relaying information to the wider business. She aims to demonstrate how solutions can be robust, reliable and repeatable based on data, but her work does not stop there &#8211; she shares the results with as many of her colleagues as she can. Her belief? “Don’t skip the technical conversations &#8211; share the knowledge, enthuse others,” because it is bound to make a difference.</p>

core/freeform

LLM workflows for product managers: 3 key takeaways (Niloufar Salehi, Assistant Professor at UC Berkeley) – ProductTank SF

The future of product management: Insights from ProductTank San Francisco

Product lessons learned making early moves with AI in media: Lindsey Jayne (CPO, Financial Times)

A year with ChatGPT and product innovation: Navigating the AI landscape

How to keep your head about generative AI (when everyone is losing theirs) by Claire Woodcock

What we get wrong about technology by Tim Harford

Product management in the age of ChatGPT by Yana Welinder

How Canva uses AI-powered features to drive PLG

The role of an AI product manager by Hiroki Nakamura

Talking About Data Science by Jenessa Lancaster

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