As a concept, the North Star principle gained a lot of attention in Product Management recently. Amplitude, a vendor of analytics tools, has a guideline to this concept. Their playbook walks product managers, those that want to enter the domain or even those just curious about methods and principles through the ideas. But also sets the scenes for potential applications by walking through exemplary goals to achieve with this approach.
The playbook comes in 7 chapters, starting by describing the ideas to apply with the North Star concept. Only after the introduction the playbook enters the practical application of the concept, and with a chapter on product metric checklist checklists, it emphasises the importance of metrics. With this it also stresses the importance of selecting the right metric and not to lose a product in vain. E.g. active users would be the wrong metric, given the goal that shall be achieved.
More practical guidelines come with the chapter on running workshops in part 3, and the chapter on defining the right guiding metrics. In between, the document also gives success stories: there are sections that talk through a successful implementation of North Star at Netflix. But also Amplitude is leveraging the methodology and shares their experience in a section.
The closing chapters dedicate to debugging the processes attached, implementing them and over time changing directions.
In all the recent hype around the method, the key take away is to simplify ideas for your organisation. The approach is supposed to make it easy for your teams to understand the direction the product is taking. And even more following this direction. For a product management, communicating ideas should be a core skill. This approach gives great tools in doing so.
The guide to discovering your product’s North Star to improve the way you manage and build products.
While I spent my time split in half between the weekend newspaper and a lake, I did some very quick research and came across Wired‘s 13 reading recommendations for this fall, that all seem to be worth a closer look.
Leseempfehlung: Ein Journalist spricht mit einem anonymen Big Data Engineer/Analyst über die Komplexität von Algorithmen. Wie erschreckend die Abhängigkeit von undurchschaubaren Komponenten geworden ist gegenüber dem Einfluss den Maschinen damit auf unser Leben haben.
Man kann das auch als Laie verstehen, wie ich meine, selbst mein Verständnis von Big Data reicht nur so weit als das als realistisch einzuschätzen.
‘I’ll lose my job if anyone knows about this.”There was a long silence which I didn’t dare to break. I had begged to make this meeting happen. And now the person I had long been trying to meet leaned towards me. “Someone is going to go through your book line by line,” he said, “to try to work out who I am.”He’d been a talented researcher, an academic, until his friend started a small technology company. He had joined the company and helped it to grow. It eventually became so big that the company had been acquired by one of the tech giants. And so, then, was he.He was now paid a fortune to help design the algorithms that were central to what the tech giant did. And he had signed solemn legal documents prohibiting him from speaking to me, or to anyone, about his work. But as the…