The Data-Driven CPO | Build Better Products

Updated May 17, 2024 to fit M* architecture My favorite development environment on my MacBook includes an improved Zsh shell and an extended .vimrc configuration file with syntax highlighting, error checking, TextMate snippets, and the Solarized color scheme.  Here's a guide for setting up similar features:  The features include directional key navigation for directories and files, developer-friendly colors, command highlighting, improved history search, auto-complete for options and SSH connections (if keys are known), and many more useful enhancements.   Get Xcode:  AppStore => Xcode => Install Xcode From now we use a terminal window. Install Brew /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" Install git and wget:   brew install git   brew install wget Install oh-my-zsh:   wget --no-check-certificate https://github.com/robbyrussell/oh-my-zsh/raw/master/tools/install.sh -O - | sh The script want

I like product ideation brainstorming—done right and focused,  it opens my mind to think much more analytically about an idea, its development, and its trajectory. But on the other hand, I often had brainstorming sessions, and they were just a waste of time. And to be honest, can you count how often a session went sideways, got stuck in the same old thought patterns, and the loudest voices in the room dominate the conversation?  I did a test yesterday with GPT-4o, and it blew the lid off my creative potential. I had tried the same exercise with the earlier models, and it was a colossal waste of time and energy. Adding AI To The Product Team, worth? Short, after the test, yes, it's definitely worth. Why? We as startup founders, product managers or developer, our job isn't just about executing on a roadmap, we have to build the roadmap and come up with the  right  product idea at the right time - in the first place. That means staying ahead of the curve, spotting opportunities w

First: XOps is not a new Marvell movie, waiting for Wolverine's revival. Period. XOps FTW  I'm a CPO. I'm not an HR expert, and I sure as hell don't want to spend my days mediating squabbles between product, design, sales and data teams. But here's the thing I've learned the hard way: if you want to build products that actually solve user problems and hit your business goals, you better figure out how to make these folks play nice in the sandbox. XOps might sound like something out of a comic book, but it's a mindset shift, a way of structuring your teams and their workflows to truly put the customer at the core of everything. Think of it as the secret sauce that turns a bunch of smart individuals into a cohesive product-building machine. I'm too lazy to write what XOps means, DevOpsSchool did it already:  XOps stands for “Cross-functional Operations,” which refers to the practice of bringing together teams and individuals from different functional area

Let's be honest, most product definitions suck. They're either packed with jargon that makes your eyes glaze over, filled with features nobody gives a crap about, or so vague they could be about anything. And most importantly, they totally miss the unfair advantage. Wait, what the hell is an unfair advantage?  Simply, it's the killer feature or a strategic edge that's so good, the others can't even copy it. It can be so simple as a dark mode, or an App Store feature to let competitors hook in. It's like building with Lego: you want that one foundational piece that's the base for everything else. Start with a simple square? Cool. But with the right unfair advantage, you can build it into a freaking skyscraper that everyone wants a piece of. Let me break down how I start to build new products. Step 1: Forget the "What," Focus on the "Why" (and How It Makes Users' Lives Easier)  Simplified: Customer Problem > Fancy Features  If you c

Last weekend I got again fed up by SaaS companies and their permanent "digital engagement" noise, so I canceled. You guess what really fed me up then? The extortion when I cancel my subscription, leading to mandatory, useless interrogation practice - surveys: "We want to understand why you want to cancel" with dozens of questions! Folks, when you DON'T understand why and when customers cancel and why your awesome product has more churn than all wholesale companies combined , then your complete product metrics (if any) are wrong and your product team needs to reevaluate how they build products. Yes, the tech startup bubble obsesses over user and customer engagement. Your investors tell you that, the "marketing" gurus, "influencers," and I don't know who else. They tell you to push notifications, implement annoying gamification,and endless "sticky" features, desperately trying to keep eyeballs locked on our products and services.

I've been building products for a long time now, moving from Solution Engineer and Solution Architect over Product Manager to my current role as CPO. Along the way, I've seen the landscape shift dramatically. One thing's for sure: if you want to create products that customers truly love (and that drive real business results), you need to stay obsessed with their experience. That means rethinking some of the old "tried and true" ways of doing things. Don't: Just Adding Features Experience is critical for building customer loyalty: A great interface sells software. No great customer experience, no sales. Product management isn't about mindlessly churning out features. It starts with a deep understanding of your customers, the market and your competition. What drives the customer behavior? What are their biggest pain points?  To answer those questions, you need a toolkit that includes research, analytics, and direct feedback channels. This empathy for your cu

Artificial Intelligence (AI) has leapfrogged to the poster child of technological innovation, on track to transform industries in a scale similar to the Industrial Revolution of the 1800s. But in this case, as cutting-edge technology, AI presents its own unique challenge, exploiting our human behavior of "love to trust", we as humans face a challenge: AI hallucinations. This phenomenon, where AI models generate outputs that are factually incorrect, misleading, or entirely fabricated, raises complex questions about the reliability and trust of AI models and larger systems. The tendency for AI to hallucinate comes from several interrelated factors. Overfitting – a condition where models become overly specialized to their training data – can lead to confident but wildly inaccurate responses when presented with novel scenarios (Guo et al., 2017). Moreover, biases embedded within datasets shape the models' understanding of the world; if these datasets are flawed or unreprese

ETL has been the traditional approach, where data is extracted, transformed, and then loaded into the target database. ELT flips this process - extracting data and loading it directly into the system, before transforming it. While ETL has been the go-to for many years, ELT is emerging as the preferred choice for modern data pipelines. This is largely due to ELT's speed, scalability, and suitability for large, diverse datasets generated by multiple different tools and systems, think about CRM, ERP datasets, log files, edge computing or IoT. List goes on, of course. Data Engineering Landscape Data engineering is the new kind of DevOps. With the exponential growth in data volume and sources, the need for efficient and scalable data pipelines and therefore data engineers has become the new standard . In the past, limitations in compute power, storage capacity, and network bandwidth made the famous 3-word "let's move data round" phrase Extract, Transform, Load (ETL) the d

OpenAI and Bard   are the most used GenAI tools today; the first one has a massive Microsoft investment, and the other one is an experiment from Google. But did you know that you can also use them to optimize and hack your startup?  For startups, reating pitch scripts, sales emails, and elevator pitches with one (or both) of them helps you not only save time but also validate your marketing and wording. Curios? Here a few prompt hacks for startups to create / improve / validate buyer personas, your startups mission / vision statements, and USP definitions. First Step: Introduce yourself and your startup Introduce yourself, your startup, your website, your idea, your position, and in a few words what you are doing to the chatbot: Prompt : I'm NAME and our startup NAME, with website URL, is doing WHATEVER. With PRODUCT NAME, we aim to change or disrupt INDUSTRY. Bard is able to pull information from your website. I'm not sure if ChatGPT can do that, though. But nevertheless, now

Searching and filtering large IP address datasets within PostgreSQL can be challenging. Why? Databases excel at data storage and structured queries, but often struggle with full-text search and complex analysis. Apache Solr, a high-performance search engine built on top of Lucene, is designed to handle these tasks with remarkable speed and flexibility. What do we need? A running PostgreSQL database with a table containing IP address information (named "ip_loc" in our example). A basic installation of Apache Solr. Setting up Apache Solr 1. Create a Solr Core: solr create -c ip_data -d /path/to/solr/configsets/ 2. Define the Schema ( schema.xml ) <field name="start_ip" type="ip" indexed="true" stored="true"/> <field name="end_ip" type="ip" indexed="true" stored="true"/> <field name="iso2" type="string" indexed="true" stored="true"/> <field

Apache Wayang is an open-source Federated Learning (FL) framework developed by the Apache Software Foundation. It provides a platform for distributed machine learning, with a focus on ease of use and flexibility. It supports multiple FL scenarios and provides a variety of tools and components for building FL systems. It also includes support for various communication protocols and data formats, as well as integration with other Apache projects such as Apache Kafka and Apache Pulsar for data streaming. The project aims to make it easier to develop and deploy machine learning models in decentralized environments. It's important to note that this are just examples and they may not be the way for your project to interact with Apache Wayang, you may need to check the documentation of the Apache Wayang project ( https://wayang.apache.org ) to see how to interact with it. I just point out how easy it is to use different languages to interact between Wayang and Spark. Also, you need to mak