Agile | Coaching | Product Management

  Future Strategy: Fast, Safe Mobile Releases with CI/CD & Feature Flags To meet growing demands for faster updates and reliable mobile experiences, we’re transforming how we deliver mobile apps. Our new approach emphasizes automation, flexibility, and control through CI/CD pipelines, feature toggles, and regular release cycles. 1️⃣ Separating Code Deployment from Feature Launch By using feature flags , we decouple code deployment from feature activation . This means we can ship code early but only make it visible to users when we’re ready. text Copy Edit Code Merged → CI/CD → Staging/UAT ↘ → Production (Feature OFF) → Enable in Production (Feature ON) 💡 Benefits : Release features silently (aka “dark launch”) Gradually roll out new functionality Instantly turn off features without pushing a new build 2️⃣ Environment-Based Versioning Each environment follows a structured version for...

  Why Consider a Paywall (Subscription Model) 🎯 1. Strategic Benefits Add More Value : Users get more than just basic banking — like personalized advice, insights, and smart tools. Earn More : Monthly fees = steady new income. Money can be used to improve the app and add new features. Stronger Loyalty : People who pay are more likely to stay. They feel like they’re part of something exclusive. Stand Out : No other Malaysian banks are doing this yet —  can lead the way. 🧠 Current State in Malaysia No Paywalls Yet : Local banks stick to free accounts and only give “premium” perks to high-balance customers. No Subscriptions : Unlike overseas, you won’t find any RM/month options in traditional Malaysian banks. Fintech Growing Slowly : A few digital banks are experimenting but haven’t offered true subscription bundles yet. Regulations Matter : must follow rules, especially compliance and digital banking laws. 🌍 Global Inspiration (What's Happenin...

  🔍 How Generative AI Differs from Other Types of AI — A Beginner-Friendly Guide Artificial Intelligence (AI) is no longer just a futuristic concept; it’s reshaping industries, customer experiences, and the way we live and work. Among the various types of AI making waves, Generative AI has captured the public imagination like never before — thanks to tools like ChatGPT, DALL·E, Midjourney, and more. But what exactly is Generative AI, and how does it differ from other types of AI? Let’s break it down. 🧠 What is Generative AI? Generative AI is a type of AI that is designed to create new content — whether it’s text, images, videos, music, or even code. This creation is not just rearrangement or retrieval from a database; it’s original output generated based on learned patterns from existing data. For example: ChatGPT generates text that mimics human conversation. Midjourney or DALL·E generates images from text prompts. MusicLM (by Google) creates musical compositi...

 Here's a breakdown of the terms CIF Sanction Screening , AML Watch Screening , and Risk Profiling in the context of financial institutions, specifically in relation to compliance and anti-money laundering (AML) efforts: 1. CIF Sanction Screening CIF (Customer Information File) Sanction Screening involves checking customer information (such as names, addresses, and other identifiers) against lists of sanctioned individuals or entities. Sanctioned lists typically come from global regulatory bodies, such as the United Nations , EU , or OFAC (Office of Foreign Assets Control) . The aim is to ensure that the bank or financial institution does not engage in any transactions or provide services to individuals or entities that are on these sanctioned lists (due to reasons such as terrorism, criminal activities, or economic sanctions). Example : If a customer’s name matches one on the OFAC list , the institution will block transactions and report it for further investigation....

 The evolution of Continuous Integration/Continuous Delivery (CI/CD) and continuous delivery was not the result of a single genius but a gradual, industry-wide shift driven by the need for better software delivery practices. In the early days, automation was seen as a luxury due to limited tools and infrastructure, making automated deployments challenging. However, over the past 15 years, advancements like Git, AWS, Heroku, GitHub, and Docker transformed software development, enabling seamless CI/CD workflows. This shift wasn’t a sudden revolution but a natural progression driven by developers and toolmakers striving for efficiency. Continuous delivery has replaced outdated, siloed approaches, bringing consistency, collaboration, and pride to software development. Automation not only simplifies workflows but also enhances productivity and team alignment. Today, CI/CD is essential for delivering functional software quickly and reliably, highlighting the importance of automation ...

 While both MoSCoW and WSJF are prioritization methods used in Agile environments, they are not exactly the same, and they serve different purposes and approaches to prioritization. Let’s break down the key differences: 1. MoSCoW Prioritization: MoSCoW is a simple and intuitive prioritization method that helps categorize work into four groups based on urgency or importance. It stands for: M – Must have (critical to success, no compromise) S – Should have (important but not critical, can be delayed) C – Could have (nice to have, not crucial) W – Won't have (won't be delivered in this time frame or iteration) How it works : MoSCoW helps teams prioritize work by giving them a way to define what’s absolutely essential to deliver, what’s nice to have, and what can be excluded from the scope. It works well for breaking down the backlog in a straightforward way based on stakeholder needs, business objectives, and team capacity. Focus : The primary focus ...

  Data Analytics and Machine Learning in Digital Transformation 1. The Role of Data Analytics and Machine Learning: Digital transformation creates value by transforming data into useful information . Internal data is gathered through IoT sensors , while external data is obtained from system APIs . Analytics and machine learning are critical for processing this data, enabling smart products and operations . 2. How Data Analytics and Machine Learning Work: Data Analytics combines traditional statistics and modern machine learning to analyze data. Linear Regression is an example that can function as both a statistical and machine learning method: Statistics : A one-time calculation. Machine Learning : Continuously trained with large datasets to make predictions. Example: A model can predict tire blowouts by analyzing tire pressure and miles driven. The system uses linear regression to create a model: Y = M X + B Y = MX + B Y = MX + B , where: Y = Ti...

Hyperpersonalization Hyperpersonalization takes personalization to the next level. While personalization uses data to tailor experiences to broad segments of users (e.g., "customers who bought X also bought Y"), hyperpersonalization uses individual user data to create highly unique and relevant experiences. It's about understanding each customer on a deep, granular level and anticipating their needs and preferences in real-time. Recognizing and Avoiding Data Biases In customer analytics, there's a common belief that "numbers don’t lie," but while numbers are generally reliable, they can still be biased. It’s crucial to identify these biases, especially when using AI algorithms to help with decision-making. Here are three common biases to watch out for, along with tips to avoid them in your projects: Confirmation Bias : This bias happens when you focus more on information that supports your existing beliefs. It can lead you to ignore different viewpoints a...

  SRE Senior Engineering Manager interview, focusing on the key responsibilities and success factors: 1. Deep Dive into SRE Principles SRE Pillars: Understand the core principles of SRE: Error Budget: How would you define, manage, and utilize error budgets within your team? Service Level Objectives (SLOs): How would you define, track, and communicate SLOs to stakeholders? Automation: How would you prioritize automation efforts within your team and across the organization? Monitoring and Alerting: How would you design and implement robust monitoring and alerting systems? Incident Response: How would you lead incident response efforts, including post-mortem analysis and implementing preventative measures? Google SRE Book: Review the Google SRE book for a comprehensive understanding of SRE principles and best practices. Answer : SRE Pillars Error Budget: Definition: An error budget represents the acceptable amount of service degradation or downtime within a speci...