Agile | Coaching | Product Management

  Taming the Velocity Beast: A Guide to Stable Sprints Are you tired of your team's unpredictable velocity? One sprint, you're flying high, the next, you're crashing and burning. This inconsistency can make sprint planning a nightmare, leading to overcommitted or underutilized sprints. Why is Velocity So Unstable? The root cause of unpredictable velocity often lies in poor work estimation. When teams struggle to accurately assess the size and complexity of user stories, they resort to guesswork or worst-case scenarios. This leads to inconsistent point assignments and unreliable velocity metrics. How to Stabilize Your Velocity: Refine Your User Stories: Clarity is Key: Ensure that user stories are well-defined, clear, and specific. Avoid vague or ambiguous requirements. Break It Down: Encourage your team to break down large user stories into smaller, more manageable tasks. This will help them estimate more accurately. Collaborative Estimation: Use techniques like Pl...

 Spiral Model  The Spiral Model: A Risk-Driven Approach to Software Development The Spiral Model is a software development process model that combines elements of both iterative and sequential development models. It is particularly useful for large, complex projects where risks are high and requirements are uncertain. Key Characteristics of the Spiral Model: Risk-Driven: Risk analysis is a crucial part of each phase. Iterative: The development process is divided into a series of iterations, each building on the previous one. Incremental: Each iteration delivers a more complete version of the product. The Spiral Model's Phases: Planning: Risk Assessment: Identify potential risks and develop strategies to mitigate them. Planning: Define the project objectives, scope, and resources. Development Plan: Create a detailed development plan, including tasks, timelines, and resource allocation. Risk Analysis: Analyze the risks identified in the planning phase. Dev...

  Spotify engineering model Spotify's Agile Approach: A Culture of Principles and Values Spotify, a global music streaming giant, has revolutionized the way we consume music. But beyond its innovative product, Spotify's unique approach to software development has also captivated the tech industry. A Departure from Traditional Frameworks Unlike many tech companies that rigidly adhere to specific frameworks like Scrum or Kanban, Spotify has taken a more flexible and adaptive approach. Rather than relying on a single methodology, Spotify has cultivated a culture of principles and values that guide its development practices. The Challenges of Scaling Scrum Around 2008, Spotify, like many other companies, adopted Scrum as its primary agile framework. However, as the company grew and scaled, they encountered challenges in applying traditional Scrum practices. They found that scaling Scrum often led to bureaucratic overhead, reduced agility, and decreased team autonomy. Spotify's ...

 Product Discovery  Rapid learning in disvoery and building stable and solid releases in delivery.  Usually the second goal will not be an issue but solving the customers real problem is important for product success. The key here is discover great products, it is really essential that you get yoru ideas in front of real users and customers early and often. If youwant to deliver great products. you want use best practices for engineering and try not to overried the engineer concerns.  Princinple of product discovery identifies the early risk of the product.  1. WHat value this product brings to the customer? What is this for (value risk) 2. Will the customer be able to use without complication ? (usability risk ) 3. Can my engieerring team build it , does our infra and architect support ?  (Feasibility risk) 4. Does this solution work for our business in term of legal, sales, marketing and etc  (Viability risk) our go to tool for product discovery is p...

Branching Strategies

 A Great Project Manager Preparation  https://www.youtube.com/watch?v=7eV3AyCcV3c Below capture from the PM interview you tube video.  This fun virtual game that can be conducted for us to build understanding, learning individual cultures and build team cohesion, example games like the link below.  https://www.youtube.com/watch?v=-KflXKhN2Uc

 Transform DATA to working Solution  Data Lifecycle in AI Projects: A Simplified Explanation 1. Data Gathering and Assessment: Collect: Gather relevant data from various sources. Assess: Evaluate data quality, quantity, and relevance. Clean: Remove errors, inconsistencies, and missing values. 2. Exploratory Data Analysis (EDA): Understand: Analyze data to gain insights and identify patterns. Visualize: Create visualizations to better understand data distribution and relationships. 3. Data Splitting: Training Set: Used to train the model. Validation Set: Used to fine-tune the model. Test Set: Used to evaluate the model's performance on unseen data. 4. Model Training and Feature Engineering: Feature Engineering: Select and transform relevant features to improve model performance. Model Training: Use algorithms to learn patterns from the training data. Avoid Data Leakage: Prevent the model from accessing information it shouldn't have. 5. Model Evaluation...