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Imon Rashid
Jan 28, 2025
In Agile Forum
Summary: The DeepSeek-V3 technical report presents a detailed analysis of the development, architecture, and performance of DeepSeek-V3, an advanced Mixture-of-Experts (MoE) language model with 671 billion parameters. Here are the key highlights: Overview • Architecture Innovations: • Utilizes Multi-Head Latent Attention (MLA) for efficient inference and DeepSeekMoE for economical training. • Implements an auxiliary-loss-free load balancing strategy and a Multi-Token Prediction (MTP) training objective for enhanced performance. Pre-Training • Pre-trained on 14.8 trillion tokens using FP8 mixed precision training for efficiency and reduced GPU memory consumption. • Introduced DualPipe pipeline parallelism for communication-computation overlap, reducing training bottlenecks. • Achieved economical training costs (2.788 million GPU hours or $5.576 million) with significant stability and efficiency. Post-Training • Refined using Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to align with human preferences and distill reasoning capabilities. • Integrated knowledge from earlier DeepSeek-R1 models for enhanced reasoning performance. Performance Benchmarks • Outperforms all open-source models in educational and factual benchmarks (MMLU: 88.5%, GPQA: 59.1%). • Excels in math and coding-related benchmarks, demonstrating robust technical abilities. • Competes closely with leading closed-source models (e.g., GPT-4 and Claude-Sonnet-3.5). Infrastructure and Deployment • Utilizes a cluster of 2048 NVIDIA H800 GPUs with NVLink and InfiniBand for communication. • Employs advanced communication techniques like dynamic redundancy strategies for load balancing and inference optimization. Limitations and Future Directions • Acknowledges the challenges of scalability and inference latency in extremely large models. • Proposes advancements in hardware co-design and further optimization for deployment strategies. The report positions DeepSeek-V3 as a cutting-edge model that bridges the performance gap between open-source and proprietary language models, with innovations in architecture, training efficiency, and cost-effectiveness. Let me know if you need further details on any specific section.
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Imon Rashid
Jan 15, 2025
In Agile Forum
By Imon Rashid The business analyst (BA) plays a critical role in translating business goals into reality through technology. Generative AI, with its ability to process information and produce human-like outputs, is revolutionizing the BA landscape. This powerful tool doesn't replace BAs; it empowers them to work smarter, not harder. Let's explore how generative AI can maximize efficiency and add significant value to business analysis. 1. Effortless Requirement Gathering Challenge: Gathering requirements often involves lengthy stakeholder interviews, meticulous note-taking, and detailed documentation. Ensuring clear and consistent requirements can be time-consuming. Generative AI to the Rescue: • Automated Transcriptions: Tools like Otter.ai or Bard can transcribe stakeholder meetings in real-time, eliminating manual effort. • Smart Summarization: AI can condense lengthy discussions into concise action points or requirements. • Natural Language Conversion: Platforms like ChatGPT can transform unstructured input into polished requirements or user stories, saving analysts valuable time. Imagine this: During an interview with a product manager about a new feature, the generative AI tool transcribes the conversation, highlights key points, and drafts a comprehensive functional requirement document for review. 2. Data Analysis on Autopilot Challenge: Extracting insights from complex datasets can be overwhelming for BAs. Generative AI Steps Up: • Data Summarization: AI models can analyze raw data and generate summaries that pinpoint key trends and anomalies. • Visualization Assistance: AI-powered tools like Tableau can recommend the most effective data visualizations, making insights crystal clear. • Scenario Simulation: AI can simulate "what-if" scenarios based on historical data, helping BAs predict the impact of proposed solutions. For Example: A BA examining sales data leverages a generative AI tool to identify a decline in customer retention rates. The AI then simulates the effects of various retention strategies. 3. Bridging the Communication Gap Challenge: Communicating technical details to non-technical stakeholders and ensuring everyone is on the same page can be tricky. Generative AI to the Rescue: • Jargon Busting: AI can translate technical terms into plain language, making information understandable for diverse audiences. • Presentation Powerhouse: Tools like Canva AI or GPT-based systems can create visually appealing presentations and reports tailored to stakeholder preferences. • Chatbots for Consistent Communication: AI-powered chatbots can answer stakeholder questions based on pre-fed project data. Imagine this: A BA uses generative AI to create a clear and concise PowerPoint presentation summarizing a complex system upgrade for executive stakeholders. 4. Smarter Decision-Making Challenge: Business analysts often need to recommend solutions based on incomplete or evolving information. Generative AI to the Rescue: • Solution Generation: AI can suggest multiple approaches to solving a business problem, complete with potential benefits and drawbacks. • Risk Assessment: AI can analyze historical data and trends to assess the risks associated with different options. • Predictive Analysis: AI can forecast potential outcomes of different solutions, aiding BAs in making informed recommendations. For Example: A BA tasked with improving a customer onboarding process uses AI to simulate different onboarding strategies and their potential impact on customer satisfaction metrics. 5. Automation Army Challenge: Repetitive tasks like updating documents, tracking changes, and maintaining version control can steal valuable time from strategic activities. Generative AI to the Rescue: • Version Management Made Easy: AI tools can track document changes and suggest updates automatically. • Template Magic: AI can populate document templates based on existing project data, reducing manual effort. • Meeting Summaries on Autopilot: AI can generate post-meeting summaries, action items, and timelines automatically. Imagine this: After a sprint review meeting, an AI tool generates a summary of discussed items, assigns tasks to team members, and updates the project timeline. 6. Bias? No Thanks! Challenge: Bias in requirement analysis or decision-making can lead to flawed solutions. Generative AI to the Rescue: • Bias Detection: AI can analyze requirements or data to flag potential biases. • Language Champion: AI can ensure that requirement documents use neutral language. For Example: An AI tool reviews a product requirement document and identifies language that might unintentionally exclude certain user groups, prompting the BA to make adjustments. Integrating Generative AI: A Few Pointers • Training Matters: Business analysts need proper training to leverage generative AI tools effectively. • Ethical Considerations: Ensure AI-generated content aligns with organizational policies and ethical standards. • Human Expertise is Key: AI should assist, not replace. Human judgment is essential to validate AI outputs. • Customization is King: Tailor AI tools to the specific needs Conclusion Integrating generative AI into the business analysis role is not about replacing the human element but amplifying it. By automating repetitive tasks, enhancing communication, and providing actionable insights, AI allows business analysts to focus on what they do best: solving complex problems and creating value for stakeholders. Organizations that embrace this integration will not only boost the productivity of their BAs but also gain a competitive edge in an increasingly data-driven world. The future of business analysis is here, and generative AI is leading the way—one insightful suggestion at a time.
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Imon Rashid
Jan 13, 2025
In Agile Forum
Imon Rashid Jan 13, 2025 Generative AI Aiding Business Analysis : A Transformational Shift The role of a business analyst (BA) is pivotal in bridging the gap between business needs and technological solutions. Generative AI, with its ability to process and create human-like outputs, has emerged as a game-changing tool that can augment the effectiveness of business analysts. Instead of replacing BAs, generative AI empowers them to work smarter, faster, and with greater precision. This blog explores how generative AI can be integrated into the business analysis role to maximize efficiency and value creation. 1. Enhancing Requirement Gathering and Documentation Challenge: Requirement gathering often involves extensive stakeholder interviews, note-taking, and documentation. Ensuring clarity and consistency in requirements can be time-consuming. How Generative AI Helps: • Automated Transcriptions: AI tools like Otter.ai or Google Bard can transcribe stakeholder meetings in real-time, reducing the time spent on manual note-taking. • Summarization: Generative AI can summarize lengthy discussions into concise action points or requirements. • Natural Language Conversion: Tools like ChatGPT can convert raw, unstructured input into polished requirements or user stories, saving analysts hours of effort. Example: A business analyst interviews a product manager about a new feature. The generative AI tool transcribes the conversation, highlights key requirements, and drafts a detailed functional requirement document for review. 2. Streamlining Data Analysis and Insights Challenge: Analyzing complex datasets to identify trends and provide actionable insights can be overwhelming for BAs. How Generative AI Helps: • Data Summarization: AI models can ingest raw datasets and generate summaries highlighting key trends and anomalies. • Visualization Assistance: Tools like Tableau integrated with AI can suggest the most effective visualizations for data, making insights more comprehensible. • Scenario Simulation: Generative AI can simulate "what-if" scenarios based on historical data to help BAs predict outcomes of proposed solutions. Example: A BA analyzing sales data uses a generative AI-powered tool to identify a decline in customer retention rates and simulate the potential impact of various retention strategies. 3. Facilitating Communication with Stakeholders Challenge: Communicating technical information to non-technical stakeholders and ensuring alignment can be tricky. How Generative AI Helps: • Simplifying Technical Jargon: AI can rephrase technical terms into simple, easy-to-understand language for diverse audiences. • Presentation Generation: Tools like Canva AI or GPT-based systems can create visually appealing slides and reports tailored to stakeholders' preferences. • Chatbots for Queries: AI-powered chatbots can answer stakeholder queries based on pre-fed project data, ensuring consistency in communication. Example: A BA uses generative AI to create a PowerPoint presentation summarizing a complex system upgrade in layman’s terms for executive stakeholders. 4. Supporting Decision-Making Challenge: Business analysts often need to recommend solutions based on incomplete or evolving information. How Generative AI Helps: • Generating Solution Options: AI can suggest multiple approaches to solving a business problem, complete with pros and cons. • Risk Assessment: By analyzing historical data and trends, AI can assess the risks associated with different options. • Predictive Analysis: Generative AI can forecast the potential outcomes of different solutions, helping BAs make informed recommendations. Example: A BA tasked with improving a customer onboarding process uses AI to simulate different onboarding strategies and their likely impact on customer satisfaction metrics. 5. Automating Repetitive Tasks Challenge: Routine tasks like updating documents, tracking changes, and maintaining version control can divert time away from strategic activities. How Generative AI Helps: • Version Management: AI tools can track changes in documents and suggest updates automatically. • Template Creation: Generative AI can populate document templates based on existing project data, reducing manual effort. • Meeting Summaries: AI can generate post-meeting summaries, action items, and timelines automatically. Example: After a sprint review meeting, an AI tool generates a summary of discussed items, assigns tasks to team members, and updates the project timeline. 6. Identifying and Mitigating Bias Challenge: Bias in requirement analysis or decision-making can lead to flawed solutions. How Generative AI Helps: • Bias Detection: AI can analyze requirements or data to flag potential biases. • Inclusive Language Suggestions: Generative AI can ensure that requirement documents use neutral and inclusive language. Example: An AI tool reviews a product requirement document and flags language that unintentionally excludes certain user groups, prompting the BA to make adjustments. Key Considerations for Integration 1. Training: Business analysts must be trained to use generative AI tools effectively. 2. Ethical Use: Ensure AI-generated content aligns with organizational policies and ethical standards. 3. Human Oversight: AI should assist, not replace. Human judgment is essential to validate AI outputs. 4. Customization: AI tools should be tailored to the specific needs of the organization and its workflows. 5. Conclusion Integrating generative AI into the business analysis role is not about replacing the human element but amplifying it. By automating repetitive tasks, enhancing communication, and providing actionable insights, AI allows business analysts to focus on what they do best: solving complex problems and creating value for stakeholders. Organizations that embrace this integration will not only boost the productivity of their BAs but also gain a competitive edge in an increasingly data-driven world. The future of business analysis is here, and generative AI is leading the way—one insightful suggestion at a time.
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Imon Rashid
Jan 12, 2025
In Agile Forum
AI  increasing importance in software development life cycle.   Artificial Intelligence (AI) is revolutionizing the Software Development Life Cycle (SDLC) by automating and enhancing various stages of the process. From requirements gathering to deployment and maintenance, AI-driven tools and techniques are transforming the way software is developed, tested, and maintained. By leveraging AI, developers can achieve greater efficiency, accuracy, and speed, leading to higher quality software products and reduced time-to-market. Requirements Gathering and Design In the initial stages of the SDLC, AI-powered tools can assist in analyzing and understanding user requirements. Natural Language Processing (NLP) and machine learning algorithms can process large volumes of data, extracting relevant information and identifying patterns that inform the design phase. AI-driven design tools can also generate design prototypes and wireframes, ensuring that the final product aligns with user needs and expectations. Development and Testing During the development phase, AI can automate repetitive coding tasks, reducing the burden on developers and minimizing the risk of human error. AI-powered code review tools can analyze code for potential issues and suggest improvements, enhancing code quality. In the testing phase, AI-driven testing tools can automatically generate test cases, execute tests, and identify defects. This not only speeds up the testing process but also ensures comprehensive test coverage. Deployment and Maintenance In the deployment and maintenance stages, AI can optimize the release process by automating deployment tasks and monitoring the software in production. AI-driven analytics can predict potential issues before they arise, allowing for proactive maintenance and reducing downtime. Additionally, AI can analyze user feedback and operational data to continuously improve the software, ensuring that it remains relevant and effective over time. Here's how AI aligns with SDLC phases and the potential for replacing roles: Requirement Analysis What AI Can Do: • Automate documentation and create detailed requirement summaries from stakeholder inputs. • Analyze past projects, user behavior, and market trends to recommend features or improvements. • Use natural language processing (NLP) to convert vague requirements into actionable items. Human Roles at Risk: • Business Analysts: AI tools may assist in requirement gathering but lack the contextual understanding and human judgment needed to negotiate or refine requirements effectively. Current Limitation: • Understanding complex, ambiguous, or politically sensitive requirements still needs human expertise. Design What AI Can Do: • Generate system architectures, UML diagrams, or prototypes based on inputs. • Provide design recommendations and optimize architecture for performance or scalability. Human Roles at Risk: • Junior Designers/Architects: Basic designs for standard use cases can be handled by AI. Current Limitation: • Strategic decisions about architectural trade-offs and innovation in complex systems require experienced humans. Development What AI Can Do: • Generate code from natural language prompts (e.g., GitHub Copilot, OpenAI Codex). • Suggest code improvements, refactor code, and debug issues. • Automate repetitive coding tasks or boilerplate generation. Human Roles at Risk: • Junior Developers: Tasks like writing boilerplate code or basic algorithms are being efficiently handled by AI. Current Limitation: • Complex problem-solving, integrating systems, and innovating new solutions require senior developers. Testing What AI Can Do: • Automate test case generation based on code or requirements. • Conduct regression testing, performance testing, and unit testing more quickly and reliably than humans. • Predict potential bugs or vulnerabilities using predictive analytics. Human Roles at Risk: • Manual Testers: Routine functional and regression testing is highly automatable. Current Limitation: • Exploratory testing, usability testing, and testing edge cases still require human creativity and contextual understanding. Deployment What AI Can Do: • Automate CI/CD pipelines to streamline deployments. • Predict deployment issues and roll back or fix errors autonomously. • Optimize infrastructure configurations based on workload predictions. Human Roles at Risk: • Release Engineers: Routine deployment processes are becoming fully automated. Current Limitation: • Managing complex deployments, especially in multi-cloud or hybrid setups, needs human intervention. Maintenance and Monitoring What AI Can Do: • Monitor systems for anomalies, predict failures, and recommend fixes. • Automate log analysis and performance tuning. Human Roles at Risk: • Support Engineers: Routine monitoring and maintenance tasks can be automated. Current Limitation: • Handling unforeseen issues or incidents requiring nuanced decision-making. Project Management What AI Can Do: • Generate project plans, schedules, and resource allocations. • Monitor project progress and predict risks or delays. • Facilitate communication through chatbots and automated updates. Human Roles at Risk: • Junior Project Managers: Routine planning and tracking can be handled by AI. Current Limitation: • Strategic decision-making, conflict resolution, and managing team dynamics are beyond AI's capabilities. Conclusion AI is most effective in automating repetitive, rule-based, and data-driven tasks. In SDLC: • At Risk Roles: Junior developers, testers, designers, release engineers, and basic project management tasks. • Secure Roles: Senior developers, architects, business analysts, and project managers, due to the need for creativity, strategic thinking, and human interaction. AI enhances efficiency but lacks the depth to replace humans entirely in SDLC, especially in tasks requiring intuition, innovation, and stakeholder management.
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Imon Rashid
Nov 11, 2024
In Agile Forum
Data Driven Application Modernization Priority: Imon Rashid November 11, 2024. (All Rights Reserved)   Introduction to Data Driven Application Modernization Priority As our organization evolves, managing and modernizing legacy applications has become critical to maintaining operational efficiency, security, and scalability. This requires an evaluation to determine their viability in today’s technological landscape. The assessment aims to identify which applications should be maintained, modernized, or retired to better align with organizational priorities and technological advancements. Given the age and varied functionality of these applications, it is essential to assess each against specific criteria, such as user engagement, frequency of use, strategic relevance, vulnerability, vendor support, and infrastructure costs. This structured analysis will enable us to prioritize investments and focus on applications that continue to add value while reducing resources devoted to outdated or redundant systems. The findings and recommendations from this evaluation are typically presented to a decision-making team to guide the organization’s long-term technology strategy. By systematically addressing the need for application modernization, we can ensure that our technological infrastructure supports future growth, enhances productivity, and reduces operational risks.   The Role of Data-Driven Modernization in Application Transformation Data-driven modernization leverages insights from data analytics to optimize legacy application transformation, ensuring that modernization efforts are strategic, efficient, and aligned with organizational goals. By analyzing relevant metrics—such as user engagement, application performance, cost efficiency, application support, and security vulnerabilities—organizations can make informed decisions on which applications to retain, modernize, or retire. A data-driven approach enables organizations to assess the actual value each application brings. For instance, applications with high user engagement and critical business functions can be identified as candidates for enhancement or re-platforming, while those with minimal usage or functionality overlap may be considered for consolidation or decommissioning. Analyzing usage frequency, infrastructure costs, and performance trends over time also provides insight into the resource demands of each application, enabling more cost-effective planning. Moreover, data-driven insights help reduce modernization risks by identifying dependencies, security risks, and operational inefficiencies early in the process. Understanding these factors allows for targeted, strategic investments that minimize disruption and maximize return on investment. Automated analytics tools and predictive modeling can further support modernization by projecting future needs, optimizing resource allocation, and enabling scalable, future-proof technology choices. Ultimately, data-driven modernization allows organizations to align their technology assets with current and future business needs, ensuring that modernization decisions are based on objective, measurable factors. This strategic approach fosters a resilient and adaptable IT environment that supports sustainable growth and operational excellence.   Factors to consider in what data is important: The data required for application modernization efforts will depend on the type of organization, operational needs, and industry. Assessing which data is essential for application modernization efforts requires a strategic approach tailored to your organization's unique context. Here are expanded factors to consider: Organizational Goals: Align data collection with your organization's strategic objectives. Identify which applications are critical for achieving short-term and long-term goals. Data that supports these initiatives is crucial. Operational Needs: Evaluate the specific operational requirements of your organization. Determine which data points are necessary to streamline processes, improve efficiency, and reduce costs. This might include data on application performance, user interactions, and operational workflows. Industry Standards and Regulations: Different industries have specific standards and regulatory requirements. Identify data that ensures compliance with industry regulations, market trends, and best practices. For example, in the financial sector, data security and transaction integrity are paramount. Stakeholder Requirements: Understand the needs and expectations of various stakeholders, including customers, employees, and investors. Determine what data is essential for them to make informed decisions and ensure satisfaction. This may involve collecting feedback, usage patterns, and satisfaction metrics. Data Quality and Integrity: Prioritize high-quality, accurate data. Ensure the data collected is reliable, consistent, and relevant to your modernization efforts. High-quality data enables better decision-making and reduces the risk of errors. Security and Compliance: Assess data security requirements, especially for sensitive information like personal data and financial records. Ensure that the data collected complies with privacy laws and security standards to protect against breaches and ensure trust. Cost-Benefit Analysis: Weigh the costs associated with collecting, storing, and analyzing data against the potential benefits. Prioritize data that offers the highest return on investment and contributes significantly to your modernization goals. Key Stakeholders for Data Collection Effective data-driven application modernization requires accurate and comprehensive data, which can only be obtained through collaboration with key stakeholders across various departments. The following roles within the organization are essential contacts for gathering relevant data: IT and Infrastructure Teams Role in Data Collection: IT and infrastructure teams are responsible for managing the organization's technology stack, including application usage, infrastructure costs, and vendor support details. They can provide insights into application performance, maintenance costs, security compliance, and dependencies on other systems. Key Contacts: IT Operations Manager, Infrastructure Lead, Systems Administrator. Application Owners and Product Managers Role in Data Collection: Application owners or product managers are directly involved in the strategic and operational use of each application. They can provide details about the application's purpose, user engagement, and alignment with business goals. These individuals understand the criticality of each application and its role within the organization. Key Contacts: Product Manager, Application Owner, Business Analyst. Finance and Budgeting Teams Role in Data Collection: Finance teams are essential for assessing the financial impact of each application, including infrastructure and maintenance costs. They can provide data on cost allocations, historical spending, and financial projections that are critical for a cost-benefit analysis. Key Contacts: Financial Analyst, Budget Manager, CFO (Chief Financial Officer). Cybersecurity and Compliance Teams Role in Data Collection: These teams can supply data on each application’s security status and regulatory compliance. They are instrumental in identifying applications that pose security risks or require urgent updates to meet compliance standards. Key Contacts: Chief Information Security Officer (CISO), Compliance Officer, IT Security Lead. Human Resources (HR) Role in Data Collection: HR can provide data on employee roles and user engagement by specific teams, allowing a better understanding of who uses each application and how frequently. They also assist in identifying any training needs that modernization may necessitate. Key Contacts: HR Business Partner, HR Data Analyst. Executive Leadership and Decision Makers Role in Data Collection: Executives and department heads often provide strategic insights, defining long-term goals and priorities for the organization. Their input is crucial in aligning modernization efforts with organizational objectives and in ensuring that high-level goals are integrated into prioritization. Key Contacts: CEO, CIO (Chief Information Officer), Department Heads. Line Managers Role in Data Collection: End users interact with applications daily, providing practical insights into usability, efficiency, and pain points. Their feedback on functionality, ease of use, and frequent issues can reveal essential information for modernization decisions, especially regarding user experience improvements and workflow optimization. Key Contacts: Team members across departments who actively use the applications in question. End Users Role in Data Collection: Line managers oversee teams that use these applications and understand both team needs and application effectiveness in supporting daily tasks. They can identify high-impact issues, suggest specific enhancements, and provide feedback on how well each application meets their team’s operational needs. Their insights are also valuable in assessing the criticality of applications within specific workflows. Key Contacts: Department or team managers directly managing users who rely on each application. By collaborating with these stakeholders, the data collection process becomes more robust, ensuring that all relevant metrics are considered. Engaging these key contacts will provide a holistic view of each application's operational and strategic value, supporting a more accurate and effective prioritization for modernization.   Data Criteria to Consider: Creating a strategic approach to application modernization is crucial for maintaining efficiency, security, and relevance in today's rapidly evolving technological landscape. To guide this process, it's important to evaluate each application based on a set of key criteria. These criteria ensure that modernization efforts are aligned with organizational goals and resource allocation is optimized. The following list outlines the essential factors to consider when assessing which applications should be prioritized for modernization. By systematically evaluating each application against these criteria, organizations can make informed decisions that support their long-term technology strategy, enhance productivity, and mitigate operational risks. Number of Users: Assess the total number of active users for each application. This helps determine the application's reach and its importance within the organization. Applications with more users generally have a higher priority for modernization to avoid disruption to a large user base. Ensure a minimum of two users to justify its continued use. Frequency of Use: Evaluate how often the application is used (e.g., daily, weekly, monthly). Applications with high-frequency usage are critical to daily operations and thus may need more immediate attention. Less frequently used applications might have lower priority unless they serve vital functions. Application Purpose: Understand the core purpose of each application. Determine if it supports essential business functions, strategic goals, or specific department needs. Applications central to mission-critical operations warrant higher priority for modernization. Application Vendor Support: Assess the level of support provided by the application’s vendor. Consider factors like the availability of updates, customer service, and technical assistance. Applications with strong vendor support may be easier to maintain, while those with limited or no support may need urgent modernization or replacement. Infrastructure Support Cost: Calculate the costs associated with supporting the application’s infrastructure. This includes hardware, software, and other resources required to keep the application running. High infrastructure costs can justify modernization efforts to streamline operations and reduce expenses. Security Compliance: Evaluate the application’s compliance with current security standards and regulations. Applications that handle sensitive data or are crucial for regulatory compliance need to be secure and up to date to mitigate risks. Non-compliant applications may require urgent modernization to address security vulnerabilities. Maintenance Cost: Determine the ongoing costs of maintaining each application, including bug fixes, updates, and troubleshooting. High maintenance costs can indicate that an application is outdated or inefficient, suggesting a need for modernization. Used by Decision Makers: Identify if the application is used by key decision-makers within the organization. Applications that are critical for executive decision-making processes hold higher strategic importance and may need prioritization to ensure they are reliable and efficient. Alternate Application Availability: Check if there are alternative applications available that can serve the same purpose more efficiently. If a modern and supported alternative exists, it might be more cost-effective to transition to the new application rather than investing in modernizing the old one. Considering these criteria will help create a comprehensive evaluation framework for determining which applications should be prioritized in your modernization efforts. This structured approach ensures that resources are allocated effectively, addressing the most critical needs first.   Create a Matrix : Create a matrix for prioritizing application modernization efforts using the collected data, follow these steps: Begin by identifying key criteria such as user engagement, frequency of use, strategic relevance, vulnerability, vendor support, and infrastructure costs. Assign weight to each criterion based on its importance to your organization’s goals. Next, evaluate each application against these criteria, scoring them on a consistent scale (e.g., 1 to 10). Multiply each application’s score by the corresponding weight for each criterion, then sum these weighted scores to calculate a total score for each application. The resulting scores will indicate which applications have the highest priority for modernization based on their overall importance and impact. This matrix provides a structured and objective approach to decision-making, ensuring resources are allocated efficiently to enhance operational efficiency and support strategic growth.       Sample Process to Create the Matrix: To create an Excel matrix that helps evaluate which legacy applications should be modernized or discontinued, use a weighted scoring approach for the given criteria. Here is a basic framework for your requirements, which can be translated into an Excel sheet: Applications : Each application should have its own row.   Criteria Weighting: You need to assign a weight to each criterion, as some are more important than others. Below is a sample example of weighting suggestion:                   Number of Users                                                                         (Weight: 15%)                 Frequency of Use                                                                        (Weight: 10%)                 Application Purpose                                                                 (Weight: 15%)                 Vendor Support                                                                          (Weight: 10%)                 Infrastructure Support Cost                                                   (Weight: 10%)                 Security Compliance                                                                (Weight: 15%)                 Maintenance Cost                                                                      (Weight: 10%)                 Used by Decision Makers                                                      (Weight: 10%)                 Alternate Application Available                                          (Weight: 5%) ================================================= Total                                                                                                     ( Weight: 100%)   Scoring: You need to define scoring values for each criterion. For example: Number of Users: ·   0-1User: 0 points ·         2-10 Users: 3 points ·         10+  Users: 5 Frequency of Use: ·         Daily: 5 points ·         Weekly: 3 points ·         Less frequent: 1 point Application Purpose: ·         Operational: 5 points ·         Supports Operation: 3 points ·         Informational: 1 point Vendor Support: ·         Supported: 1 point ·         Discontinue 1-2 year: 3 points ·         Not supported: 5 points  Infrastructure Support Cost: ·         Low: 1 points ·         Moderate: 3 points ·         High: 5 point   Security Compliance: ·         Compliant: 1 point ·         In-risk of non-compliance: 3 points ·         Not compliant: 5 points   Maintenance Cost: ·         Low: 1 point ·         Moderate: 3 points ·         High: 5 points    Used by Decision Makers: ·         Yes: 5 points ·         No: 0 points    Alternate Application Available: ·         No: 5 points ·         Yes: 0 points   Weighted Score Calculation: In a spreadsheet, use formulas to calculate the weighted score for each application.                 Example formula: `= (Number of Users Score 0.15) + (Frequency of Use Score        0.10) + ... + (Alternate Application Score * 0.05)`   Matrix Layout: In the spreadsheet, the columns will be:    Decision Criteria: After calculating the weighted score for each application, you can sort them by score to prioritize which applications to modernize, maintain, or discontinue. This approach should help in presenting a clear, data-driven recommendation to the decision-making team.         Conclusion This data-driven framework for application modernization prioritization provides a structured approach to assess legacy applications based on criteria like user engagement, operational relevance, and cost efficiency. However, it’s essential to recognize that this is not a one-size-fits-all solution. Each organization’s unique context, goals, and constraints should guide the application of this framework. The importance of each criterion may vary depending on specific organizational priorities, industry standards, and operational needs. For instance, in some organizations, security compliance may outweigh user engagement, while in others, cost efficiency could take precedence. Decision-makers should carefully tailor the weighting of criteria and the scoring methodology to reflect their organization’s strategic objectives and operational realities. In essence, this framework serves as a flexible guide to help navigate the complexities of application modernization. By customizing it to your organization's unique requirements, you can ensure that modernization efforts are both strategic and aligned with your long-term goals, ultimately fostering an IT environment that is both resilient and adaptable to future demands.
Data Driven Application Modernization Priority content media
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Imon Rashid
Nov 08, 2024
In Agile Forum
By Imon Rashid November 8, 2024. (All Rights Reserved) In` today's rapidly evolving digital landscape, financial institutions must continually enhance their online platforms to meet customer expectations and maintain a competitive edge. Recognizing this imperative, our bank is embarking on a comprehensive project to upgrade the user interface (UI) of online banking application. The primary objectives of this initiative are twofold: 1. Enhance User Experience: Simplify and streamline the online banking process to ensure customers find it intuitive and efficient. 2. Promote Products: Integrate and highlight offerings such as Certificates of Deposit (CDs) and other investment options, encouraging customers to explore and utilize these services. To achieve these goals, it is essential to gather detailed requirements from all relevant stakeholders. This includes external users (bank customers), internal users (bank employees and line managers), system administrators, application owners or managers, and higher management. By engaging each group through targeted questionnaires, we aim to collect valuable insights that will inform the design and functionality of the upgraded UI, ensuring it aligns with user needs and business objectives To effectively upgrade the user interface (UI) of the banking application, it's essential to gather comprehensive requirements from all relevant stakeholders. This ensures that the redesigned UI not only enhances user experience but also aligns with business objectives and operational needs. Below are tailored questionnaires for each stakeholder group, designed to elicit valuable insights for the project. Some of the Depending on the project type, need, and circumstances there may not be a interview certain stakeholders Very Important: Always send the questionnaire prior to the 1. External Users (Bank Customers) Objective: Understand customer experiences, challenges, and preferences to improve usability and promote investment products. 2. Internal Users a. Bank Employees Objective: Collect insights on operational challenges, workflow efficiency, and customer interactions. b. Line Managers Objective: Understand managerial perspectives on team performance, customer satisfaction, and system effectiveness. 3. System Administrators Objective: Understand technical challenges, system performance, and security considerations. 4. Application Owners or Managers or PoC Objective: Align UI enhancements with business goals, user engagement, and product offerings. 5. Higher Management Objective: Ensure the UI upgrade aligns with strategic vision, compliance, and overall customer satisfaction.
Sample Questionnaire for UI stakeholders   content media
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Imon Rashid
Nov 04, 2024
In Agile Forum
How your Credit Card Company Processes Payment Disputes By Imon Rashid (All Rights Reserved) In our digital world, credit card payment disputes happen quite often. These disputes occur when a cardholder questions a transaction, asserting it was either unauthorized or inaccurately described. This triggers a complex process involving various parties: the cardholder, the merchant, and the card issuer. This exploration will dive into the details of credit card payment disputes, covering everything from the initial filing to the final resolution. We'll examine the key participants, the frequent causes of disputes, and the resolution process. So the following diagrams should be taken as illustration only, as they are not done to scale, and kept vague not to coincide with real life process. Understanding Credit Card Payment Disputes 1) Issuer Processing Dispute In this flow, the issuer directly handles the dispute process. • Dispute Intake: The cardholder initiates the dispute through one of several channels, such as a call center, mobile app, or web portal. • Create Dispute Case: The dispute information is sent to the Issuer Dispute System/Gateway, which creates a dispute case with the provided details. • Dispute Request to VROL/MasterCom: The Issuer Dispute System/Gateway sends a dispute request to Visa Resolve Online (VROL) or MasterCom (the Mastercard equivalent) through a Dispute Request API. • Process Dispute Request: VROL/MasterCom processes the dispute request and sends the information to the acquirer or merchant for review. • Process Response: VROL/MasterCom responds to the Issuer Dispute System/Gateway through the Dispute Response API, confirming that the dispute request has been processed. • Update Cardholder: The issuer’s system updates the cardholder with the dispute status, notifying them that the case is under review. 2) 3rd Party Processing Dispute In this flow, a third-party processor manages the dispute process on behalf of the issuer. • Dispute Intake: The cardholder initiates the dispute, using a call center, mobile app, or web portal as before. • Create Dispute Case: The dispute information is sent to the third-party processor, who creates the dispute case. The third party may also perform a Transaction Inquiry or gather Transaction Details as part of the case creation. • Dispute Request to VROL/MasterCom: The third-party processor sends a dispute request to VROL or MasterCom using a specific Request API. • Process Dispute Request: VROL/MasterCom processes the dispute request and forwards it to the acquirer or merchant for review. • Process Response: VROL/MasterCom sends a response back to the third-party processor through the Dispute Response API, confirming that the dispute request has been processed. • Update Cardholder: The third-party processor relays the dispute status back to the issuer, which then updates the cardholder, confirming that the case is being handled. Key Differences • In the Issuer Processing Dispute flow, the issuer’s own system manages the dispute. • In the 3rd Party Processing Dispute flow, a third-party processor handles the dispute case creation, inquiries, and communication with VROL/MasterCom on behalf of the issuer. • Both processes ultimately aim to resolve the dispute by involving VROL/MasterCom and notifying the cardholder, but they differ in whether the issuer or a third-party processor handles the dispute case. You can read more here how MasterCard processes disputes in this link.: https://developer.mastercard.com/mastercard-processing-dispute/documentation/use-cases/create-dispute-case/
Anatomy of a Payment Dispute content media
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Imon Rashid
Nov 02, 2024
In Agile Forum
Mastercard First-Party Trust is a program designed to combat friendly fraud, a type of fraud where a cardholder disputes a legitimate charge. This program aims to improve the efficiency and accuracy of dispute resolution processes. Key Features: 1. Enhanced Data Sharing: • Merchants and issuers can share additional data points, such as device information, shipping addresses, and email addresses. • This richer data helps to verify the authenticity of transactions and identify potential fraud. 2. Compelling Evidence: • The program leverages historical data to build a strong case for legitimate transactions. • By analyzing past purchase patterns and behaviors, the program can identify unusual activity and flag potential fraud. 3. Liability Shift: • When compelling evidence supports a merchant's claim, the liability for the dispute can be shifted from the merchant to the issuer. • This helps to reduce chargebacks and protect merchants from unfair disputes. 4. Improved Dispute Resolution: • By providing more detailed information upfront, the program can accelerate the dispute resolution process. • This reduces the time and effort required to resolve disputes for both merchants and issuers. 5. Enhanced Consumer Experience: • The program aims to create a more seamless and secure shopping experience for consumers. • By reducing fraudulent chargebacks, merchants can offer better customer service and avoid potential service disruptions. Overall, Mastercard First-Party Trust is a valuable tool for combating friendly fraud and improving the payment ecosystem. By fostering collaboration between merchants and issuers, the program helps to protect businesses and consumers alike.
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Imon Rashid
Oct 27, 2024
In Business Analysis Forum
FX Tiger trading Expert Advisor (All Rights Reserved) Background: Meta Trader is a globally popular trading platform enables users to trade Forex, Stocks & Futures, Indices, Commodities, and Cryptocurrencies. Meta Trader 4 and 5 are both available on Windows, Android, and iOs systems. MetaTrader is a powerhouse in the world of trading platforms, designed to provide traders with a comprehensive suite of tools for analyzing financial markets and executing trades. With its intuitive interface, robust charting capabilities, and support for automated trading through Expert Advisors (EAs), MetaTrader caters to both novice and seasoned traders. Meta Trader 4 and 5 platform offers the option to install custom forex Expert Advisors (Autonomous Robots), Scripts, and Indicators. This allows retail forex traders to deploy custom developed software to identify and execute trading opportunities. However, currently there is no reliable SW that will trade (scalping method) with 5 to 10 PIPs margin. Traders should have the opportunity to automate orders using a preset Long or Short order. The shortage of a simple effective scalping robots for MetaTrader 4 (MT4) and MetaTrader 5 (MT5) has created a significant demand among traders. Scalping, a strategy that involves making numerous trades for small profits within a short timeframe, requires precise and fast-executing tools. However, finding a reliable scalping robot that can consistently perform well in varying market conditions has become increasingly challenging. This scarcity has led traders to seek out high-quality scalping robots that can enhance their trading efficiency and profitability      High level process flow: High level process flow      Prototype/Demo:      High Level Business Requirements: Platform: 1)      The EA will be deployed  on Production MS Win 10/11 MT4 and/or MT5 trading platform (chart) in conjunction with any major forex broker, mainly Oanda. 2)      The EA shall also be operational on cloud-based Production Virtual Private Server or Virtual Machines. Initiating\activating the EA: How the EA is in activated ·         The EA .exe file will be uploaded to o   MT4> File > Open Data folder MQL4> Experts (Folder) ·         DLL files to be uploaded to o   MT4> MQL4> Libraries (Folder) ·         EA user will be authenticated by entering url in, o   MT4> Tools> Options> Experts > Allow WebRequest for ·         The user will be authenticated by the user name in the resource located in the url.   Autonomous Capability: 3)      The EA will be autonomous in trade engagement. 4)      The EA will determine trade Entry and Exit point on long and short positions using industry standard technologies. 5)      The EA shall trigger an alert by pop up prompt or SMS message about a trading opportunity. 6)      The EA will trigger an alert after three consecutive Stop/Loss activation. Data Processing: 7)      The EA shall have the capability to extract chart data from past 30 to 60  various time frame periods. 8)      The EA will employ statistical tools such as Exponential Moving Average (EMA), MACD, and/or Stochastic RSI indicators. 9)       to process the chart data Trading Time: 10) The EA will trade from Sunday 5 PM EST to Friday 5 PM EST 11) Any trading long or short position not closed by Friday 5 PM EST ( weekly forex trading Closing time) shall be carried to Sunday 5 PM EST ( weekly forex opening session) Transaction Records or Journal: ( Nice to have)) 12) Each trade shall record data processed for that trade including a.       Order #, b.     Time of execution c.      Chart data considered. d.     Any other pertaining data. 13) This data shall be exported as a CSV file. Non-functional requirements:   14)             System Functional Requirements Features: Authentication: ·         Authenticate user with FX Tiger Auth. ·         Handle authentication requests and responses. Chart Attachment: ·         Attach the EA to the desired trading chart. Data Retrieval: ·         Retrieve chart data, including market price and historical data. Indicator Calculation: ·         Calculate technical indicators based on the retrieved data § Exponential Moving Averages ( Indicator 1) §  Indicator 2 §  Expert Advisor Order Generation: ·         Determine whether a new order is needed based on the, §  Calculated indicators §  Predefined trading rules. Create buy or sell orders with specified parameters §  Entry price, §  Stop-loss, §  Take-profit Order Management: §  Monitor open orders for, §  Stop-loss §  Take-profit §  Close orders manually if necessary. Additional Features Risk Management: §  Implement risk management strategies, §  Position sizing §  Stop-loss limits, and §  Maximum loss per trade. Backtesting: 1. Allow for backtesting the EA's performance on historical data to evaluate its effectiveness. Optimization: 1. Provide options for optimizing the EA's parameters to find the best settings for a given trading strategy. Alert Notifications: 1. Send alerts or notifications when specific events occur, such as order placement, execution, or stop-loss/take-profit triggers. Customizable Trading Rules: 1. Allow users to customize trading rules and strategies to suit their individual preferences. Integration with Other Platforms: 1. Integrate with other trading platforms or tools for additional functionalities (e.g., portfolio management, data analysis).     User Stories: User Story 1: Authenticate User As a forex trader, I want to authenticate my account using FX Tiger Auth in MT4 via a web/API request to the FX Tiger site, So that I can securely access my trading account and manage my trades. Acceptance Criteria: 1. The user initiates the authentication process in MT4. 2. The system sends a web/API request to the FX Tiger site for authentication. 3. The system receives a response from the FX Tiger site confirming successful authentication. 4. The user gains access to their EA for trading account in MT4.   User Story 2: Handle Authentication Requests As a system administrator, I want to handle authentication requests from MT4 to the FX Tiger site, So that I can ensure users are securely authenticated before accessing their trading accounts. Acceptance Criteria: 1. The system receives authentication requests from MT4. 2. The system validates the authentication credentials via the FX Tiger site. 3. The system sends a response back to MT4 with the authentication status. 4. The system logs all authentication attempts for audit purposes.   User Story 3: Handle Authentication Responses As a forex trader, I want to receive clear and immediate feedback on my authentication status, So that I know whether I can proceed to access my trading account or need to reattempt authentication. Acceptance Criteria: 1. The system sends authentication responses to the user's MT4 platform. 2. The user receives a confirmation message for successful authentication. 3. The user receives an error message for failed authentication attempts, with details on the failure reason. 4. The system provides guidance on how to reattempt authentication if needed. User Story 4: Attach Expert Advisor to Trading Chart As a forex trader, I want to attach the Expert Advisor (EA) to my desired trading chart, So that I can automate my trading strategy and improve my trading efficiency. Acceptance Criteria: 1. The user selects the desired trading chart in MT4. 2. The user attaches the EA to the selected chart. 3. The EA is successfully attached and ready for configuration.     User Story 5: EA Trade setting configuration As a forex trader, I want to configure the EA to set the trade setting values of long/short positions, equity size, lot size, profit taking, stop-loss, magic number, simultaneous trading So that I can tailor the EA to my trading strategy and market conditions.   Acceptance Criteria: 1. The user opens the EA settings window. 2. The user selects whether to allow long, short, or both types of positions. 3.      The user enters the desired lot size. 4.      The user enters the desired equity size. 1. The user enters the desired profit-taking level. 2. The user enters the desired stop-loss level. 3. The magic number is pre-populated 4. The EA saves and applies the simultaneous trading settings 5. The EA saves and applies the position settings.   User Story 6: EA Filter Configuration: Variable EMA 1 As a forex trader, I want to configure the period for the first Exponential Moving Average (EMA 1) in the EA Filter settings, So that I can tailor the EA to my specific trading strategy. Acceptance Criteria: 1. The user opens the EA settings window. 2. The user locates the configuration option for EMA 1. 3. The user enters the desired period for EMA 1. 4. The EA saves and applies the entered period for EMA 1.   User Story 7: : EA Filter Configuration: Variable EMA 2 As a forex trader, I want to configure the period for the second Exponential Moving Average (EMA 2) in the EA Filter settings, So that I can further customize the EA to fit my trading strategy. Acceptance Criteria: 1. The user opens the EA settings window. 2. The user locates the configuration option for EMA 2. 3. The user enters the desired period for EMA 2. 4. The EA saves and applies the entered period for EMA 2. User Story 8: Configure Predefined Trading Rules   As a forex trader, I want to configure the predefined trading rules in the system, So that I can customize the trading strategy according to my preferences and risk management plan. Acceptance Criteria: 1. The user accesses the trading rules configuration settings. 2. The user sets parameters such as maximum risk per trade, allowed trading hours, and entry/exit criteria. 3. The system saves and applies the configured trading rules.     User Story 9: Validate Trade Orders Against Predefined Rules As a trader, I want the system to validate trade orders against predefined trading rules, So that only valid and compliant orders are executed. Acceptance Criteria: 1. The system checks each generated trade order against predefined trading rules (e.g., maximum risk per trade, trade timing restrictions). 2. Invalid orders are flagged and not executed.     User Story 10: Generate Buy Order Based on Calculated Indicators As a forex trader, I want the system to generate a buy order based on calculated indicators, So that I can capitalize on potential upward market movements. Acceptance Criteria: 1. The system calculates indicators (e.g., moving averages, RSI, MACD) based on current market data. • Exponential Moving Average (EMA) parameter. • Relative Strength Index (RSI) parameter • Moving Average Convergence and Divergence (MACD) parameter 2. When the indicators meet predefined buy conditions •  Short EMA crosses over Long EMA •  RSI crosses over the Simple Moving Average the system generates a buy order. 3. The buy order includes details such as currency pair, lot size, entry price, stop loss, and take profit levels. 4. The buy order is executed automatically in the trading platform. User Story 11: The  Buy (Long Position)  Order is displayed on MT4 Terminal "Trade Tab" As a trader, I want to see a buy order displayed on my MT4 terminal "Trade Tab" , So that I can confirm that the order has been executed successfully. Acceptance Criteria: • A new buy order with the correct parameters (symbol, volume, price, stop-loss, take-profit) is visible in the "Trade" tab of the MT4 terminal. • The order status is displayed as "Pending" or "Filled" as appropriate. • The order's ticket number is unique and can be used to identify the order. • The order's parameters (symbol, volume, price, stop-loss, take-profit) match the values specified in the trading system. • The buy order can be closed or completed from the "Trade" tab. User Story 12: Generate Sell Order Based on Calculated Indicators As a forex trader, I want the system to generate a sell order based on calculated indicators, So that I can take advantage of potential downward market movements. Acceptance Criteria: 1. The system calculates indicators based on current market data. • Exponential Moving Average (EMA) parameter. • Relative Strength Index (RSI) parameter • Moving Average Convergence and Divergence (MACD) parameter 2. When the indicators meet predefined buy conditions •  Short EMA crosses under Long EMA •  RSI crosses under the Simple Moving Average the system generates a buy order. 3. The sell order is executed automatically in the trading platform. User Story 13: The  Sell Order is displayed on MT4 Terminal "Trade Tab" As a trader, I want to see a buy order displayed on my MT4 terminal "Trade Tab" , So that I can confirm that the order has been executed successfully. Acceptance Criteria: • A new sell order with the correct parameters • symbol, volume, price, stop-loss, take-profit) is visible in the "Trade" tab of the MT4 terminal. • The order status is displayed as "Pending" or "Filled" as appropriate. • The order's ticket number is unique and can be used to identify the order. • The order's parameters (symbol, volume, price, stop-loss, take-profit) match the values specified in the trading system. • The sell order can be closed or completed from the "Trade" tab.   User Story 14: Monitor and Adjust Trade Orders As a forex trader, I want to monitor and adjust generated trade orders, So that I can ensure they align with my trading strategy and market conditions. Acceptance Criteria: 1. The user accesses a dashboard displaying all active and pending trade orders. 2. The user can modify parameters such as entry price, stop loss, and take profit levels. 3. The system updates and executes the modified orders accordingly. 4. The system logs all modifications for audit purposes. User Story 15: FIFO Rule Compliance   As a trader I want to open a trade and close or complete that trade only So that I am compliant with the broker FIFO rules. Acceptance Criteria: ·         The EA did not generate any new order when one order is open state either by the EA or manually by user. ·         The EA logs all order open and close data for audit purpose. ·         If the user opens a new order when an order is already opened by the EA, then the EA must close its order first for FIFO compliance. User Story 16: Manually Close Trade Orders   As a trader, I want to be able to manually close trade orders opened by the trading EA (robot), So that I can intervene in the trading process and manage risk. Acceptance Criteria: • The user can select an open trade order from the "Trade" tab in the MT4 terminal. • The user can choose to close the position. • The system should allow the user to close the trade at the current market price. • The system should provide confirmation before closing the trade. • The system should update the “Account History” and  trading journal to reflect the closed trade, including the closing price, profit/loss, and other relevant details. • The system should notify the user of the successful closure of the trade.
FX Tiger (EA Application) for scalping strategy
Business Requirements
By Imon Rashid 
Oct 27, 2024 
 content media
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Imon Rashid
Jul 06, 2020
In Agile Forum
Quarantine Scrum, leading remote teams We are faced with the reality that we can’t make use of our project “war” rooms and are forced to work from home. Well.. challenge accepted! Scrum actually provides a perfect framework for remote teams. If you’re not already working with such a framework, maybe you should. Set goals and ask for commitment Stop asking for estimations of time to complete a task and start explaining what goal you are trying to attain. When are you satisfied with the result? Describe what you want to accomplish or (preferably) what you want for your customer. This could also be another department within your company. Discuss thoroughly the context of the result and the criteria when your customer is satisfied. This gives your team member an insight of why something needs to be done and who it’s for. They are able, an capable, to determine the best course of action to accomplish this. Let them be the experts and you the leader. With a framework, such as scrum, you can define a goal for a set period of time. This period is called a sprint. A goal could be delivering something to you customer, but could also be testing an assumption or addressing some risks. A sprint usually consists of several thing that need to be done, that are part of the sprint goal. Explain these different parts and how they contribute to the goal. We call them backlog items. Tell your team when you would be satisfied if it’s delivered at the end of the sprint. If you can't use sprints, and sprint goals, at least use clear backlog items. Explain the goal, the context and who it's for, your team is capable enough to determine how this needs to be accomplished. When you explained what you want to accomplish, ask your team what would be feasible. Team members that are forced to work at home during this crisis also have some social obligations to attend to and limited in their time. They are the best judge of what is feasible. Research also shows that when you can determine to what extend you can give commitment, you are more likely to be committed and have more fun. In this way you are the person who determines what is most valuable. We call them the product owner within the scrum framework. You are not bogged down by discussing how your experts are going to do their job and can focus on prioritizing the goals you want to pursue. Keep in touch and review the result Working remotely can create isolation and that will result in a downturn in communication and lessened quality of results. To maintain the team spirit you need to make sure that everybody feels like a team. Start everyday with a quick get together. Not more then 15 minutes. It’s called a Daily Scrum. First of all it gives everybody the feeling they belong to a team, but you can also keep track of anything that impedes your team from reaching their goal. You discuss the progression towards the sprint goal; what they have been working on, what they are going to work on today and where they could use some help. This could result in some of your team member having some additional discussions afterwards to keep alignment. Keep the team spirit by coming together daily and check in on the progress and impediments of your team. After every part that you have described within the sprint, make sure you and the team members that worked on that part, get together to discuss the result. You can evaluate the result and see if the goal is reached within the criteria that you set. If not, discuss openly your concerns and let your team advice you for possible solutions. Naturally you'll have new ideas, so describe them as a part of your next sprint. At the end of the sprint, set up a meeting with your team and the customers that requested result. Your team has the chance to show what they have been working on and your customer can give them feedback. You'll see that there will be appreciation from the customer and empowerment of the team. The shared understanding will grow during the discussion and miscommunication will be quickly identified. All this information will lead to better alignment for upcoming sprints and new tasks that will be taken on. Fake face to face communication “The most efficient and effective method of conveying information to and within a development team is face-to-face conversation.”, is one of the principles of the Manifesto for Agile Software Development. Well, that might not be possible in the coming weeks, but luckily there is a second best. Video conferencing! Use Microsoft Teams, Zoom, WhereBy, GoTo Meeting or some other tool. We're fan of Microsoft Teams, but there are lots of others out there (we like to hear your opinion). We like to have our meetings so we can see everybody and their screens. It ensures we pickup non-verbal communication. Working remote this makes it a little bit tricky, but you'll have more information if you can at least see the other person through a video stream. So make sure everybody's camera is on. Non-verbal communication is important when conveying information, so share your camera feed. If you have a meeting with a bigger group ensure that everybody respects the others in the group. Don't start talking while others are talking. Use the chat function in your conference call to raise your virtual hand, so that everybody knows you want to add to the story or ask a question. This way your meetings keep running smooth. Inspire others to collaborate! Are you managing a remote team and you have any tips and tricks, please feel free to send me a message or add them in the comments. Collaboration is key to working at home efficiently. Keep collaborating and stay healthy! https://www.linkedin.com/pulse/quarantine-scrum-leading-remote-teams-erik-poels/?articleId=6645785750447153152
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Imon Rashid
May 18, 2020
In Agile Forum
Rafael Maranzato tells the story in this short video of a team who initially failed to adopt Scrum, but they tried again, successfully adopting it and moving to Scrum of Scrums within one year. Click here for the Video.
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Imon Rashid
May 18, 2020
In Agile Forum
ahoo! is a large enterprise with a $32 billion market cap and has one of the largest Agile implementations in the world. The adoption of Scrum and Agile practices has been steadily growing over the past two years, and now encompasses more than 150 Yahoo! teams in the United States, Europe, and Asia-Pacific. The projects range from new product development for properties such as Yahoo! Autos to heavy-duty infrastructure work on Yahoo! Mail, which serves 250 million users each month around the globe. Read the complete article at the following link: http://static1.1.sqspcdn.com/static/f/447037/6486321/1270929190703/YahooAgileRollout.pdf?token=TEKsdddU%2FjH9HIMMDMZzjOTqMPc%3D
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Imon Rashid
May 14, 2020
In Agile Forum
As the pace of business continues to accelerate, more and more organizations are turning to agile methodologies to keep up. And with top business priorities revolving around fulfilling customer needs, improving time to market, and reducing cycle time, the Scrum team structure has become the obvious answer for many organizations. Below we will cover what Scrum is and how can you build an effective Scrum team for agile development.  What is a Scrum team? Scrum is an iterative project management framework for implementing the agile methodology. The Scrum framework focuses on continuous improvement and learning to facilitate an agile mindset and allow teams to work together to develop projects. With only a few sets of rules, the Scrum framework provides a flexible guideline for teams to follow and adapt to their specific projects and development environments. This flexibility makes it an appealing structure across teams and organizations.  The basic Scrum Framework is composed of the following elements: 3 agile Scrum team roles: product owner, Scrum Master, and development team A prioritized backlog of user requirements Sprints Scrum events Scrum events include sprint planning meetings, daily Scrum meetings, sprint review meetings, and sprint retrospectives. Scrum team composition The typical Scrum team size is five to nine people (with seven being the ideal—one product owner, one scrum master, and five developers).  Unlike traditional development structures, Scrum teams don’t have a structural hierarchy. Instead, they are self-managing and cross-functional. All team members are equally important and together have all the skills and knowledge necessary to deliver a working product. While everyone has an equal voice, there are three distinct roles within the Scrum team structure. Product owner The product owner is the champion of the product and the foundation for its success. Their main responsibility is to understand the business and customer requirements and define and prioritize the work accordingly.  This role entails:  Building and managing the product backlog Communicating with the business and team to ensure everyone is on the same page Guide the team on which features to deliver next Decide when to ship the product In other words, the product owner acts as a guidepost for the team throughout the development process. While all team members will collaborate and discuss how to tackle the work, the product owner has the final say on what to prioritize and when.  Dive deeper into the seven key responsibilities of the product owner. Learn more Scrum Master The Scrum Master helps the team apply the Scrum framework successfully. They ensure the team functions smoothly by reigning in overbearing product owners, minimizing distractions, and coaching the team on best practices.  The Scrum Master also leads the daily Scrum meeting, which helps the team stay on task and on process.   Development team The developers are the foundation of the Scrum team. While the product owner outlines the priorities and the Scrum master monitors the process, the development team is responsible for determining how to get the work done.   They are essentially autonomous (one of the features that make Scrum unique from other methodologies). This feature makes Scrum teams highly collaborative and close-knit, often resulting in higher morale, satisfaction, and purpose.  Benefits of an agile team structure The Scrum team structure is a popular approach for many teams—and for good reason. Scrum has several advantages:  1. Shorter feedback cycle Due to their incremental approach to development, Scrum teams can receive and act on feedback faster.  For example, instead of spending six months developing and then releasing a product based on the original requirements, Scrum teams shorten the development cycle with multiple, shorter releases (often within a few weeks).  This structure allows them to get feedback earlier in the development process and adapt the product based on their learnings and user feedback.  2. Greater ability to adapt to change Scrum teams are designed to expect and adapt to change. Agile frameworks like Scrum make it easy for teams to pivot based on user feedback and changing requirements as they arise—instead of letting these changes disrupt or derail the development process. 3. Higher quality products Because Scrum teams are agile, they can deliver higher quality products with greater consistency. In addition to receiving and adapting to incremental feedback, Scrum teams also test the product at every sprint, ensuring issues are identified and handled as they occur.  4. Transparency Transparency and communication are key principles of the Scrum framework. The product owner and stakeholder(s) take active roles in the development process.   Therefore, transparency is critical for both internal team collaboration and external client (or user) communication so that the work always aligns with the product goals and requirements.     5. Higher user satisfaction With higher-quality outcomes, responsive feedback loops, clear communication, and managed scopes, it is no wonder Scrum teams so frequently experience higher user satisfaction.    6. Shared team purpose The culture of Scrum is one of collaboration. The heart of the Scrum team is the developers. Because there isn’t a traditional hierarchy with a team boss, and the work itself is structured collaboratively, members have a shared sense of ownership for the product.  This sense of ownership improves morale, gives the team purpose, and helps everyone work more productively.  When to use a Scrum team structure Scrum teams can work on all types of software development projects, including full software packages, client or internal work. While Scrum is a flexible and valuable approach for many types of projects, there are a few ways to recognize when it is best applied.  When requirements are not clearly defined Sometimes clients have a general vision for their product but lack clear requirements. This can make it difficult to estimate the scope of time and costs—which are necessary for fixed cost projects or more traditional methodologies.  The Scrum framework is built to adapt to evolving requirements, making it the natural choice for projects with undefined scopes. When changes are expected during development Similarly, Scrum works especially well for projects that anticipate changes during development. This can happen even when requirements are clearly defined from the outset.  For example, changes in the business environment or evolving technologies can affect product requirements mid-project. Scrum’s agile structure makes it easy to pivot to accommodate changes throughout the development process. When the project is complex Complex problems are difficult to address effectively and efficiently in traditional development methodologies. The more complex the project, the more issues that can arise as you go.  Scrum is well equipped to handle complex projects because it breaks them down iteratively and incrementally. Rather than trying to anticipate all the requirements in one plan at the beginning of the project, Scrum teams work on it piece by piece, adapting as they learn.  Picking the right people for your Scrum team To build a successful Scrum team, you need to bring together the right people. But what do you look for?  A good Scrum team is: Collectively accountable for the work  Autonomous and self-organizing Cross-functional and balanced Colocated and everyone works full-time together Additionally, look for a product owner who is fully available for the project. They must be fully involved to ensure the team has the right priorities and guiding requirements along the way.  When you have determined the key competencies required for a given project, consider using a visual workspace to highlight employees with the necessary strengths and skill sets. In Lucidchart, you can generate an org chart from employee data, add conditional formatting, and group employees by various factors. Though Scrum is not difficult to implement, consistently delivering real value is never easy. Teams that want to succeed in an Agile environment must commit to the process and their own personal and collective growth. Those that do are the ones who will stay ahead of the curve. This article first appeared on this link.
How to Build a Scrum Team Structure for Agile Development content media
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Imon Rashid
May 11, 2020
In Agile Discussions
The work of a Scrum Development Team is pretty complex. People with a wide range of technical skills need to organise themselves to bring multiple work-streams together and produce production-grade code in as little as 1-2 weeks. While the tasks involved are carefully planned with detailed implementation steps, the novel nature of the work means that plans need to be revised on the fly as new information comes to light. By the end of the sprint, the broad objectives and goals may have been met in ways that were not foreseen at the start. As a result, a daily synchronisation event is absolutely essential to keep focus, share information, and allow time to re-plan. The trouble is that in the real world, there is an awful lot that can get in the way of this self-organised utopia. We regularly work with teams that see the daily scrum as irrelevant, disruptive, and boring. They are often right. Here are three common anti-patterns and three potential solutions. 1. The team is all working on separate things Scenario: A team works together in the same functional area of IT, perhaps maintaining the same software product, but the pattern of work is such that each individual works on their own task for a few days or weeks at a time, such as: Small client projects Business-as-usual minor enhancements Configuration changes Of course, the team members help each other out when needed and there’s the monthly curry-night to keep up morale, but people find very little value in getting daily updates on the meetings their colleagues attended. Potential Solution: Change the Process Scrum is optimised for complex work, originally in the software product development space. Teams work closely together towards regular incremental goals, that form part of a long-term product strategy. If this is not what your team are doing, then maybe there are other modern, productive delivery processes that might be a better fit. Kanban is the obvious mainstream example, and adopting this methodology would allow your team to: Define the service your team is providing Visualise your process and populate it with work Design a series of events that is optimised for the work you are actually doing This might lead you to conclude that a Daily Scrum is not a good fit, and perhaps a weekly catchup might serve you better. 2. ‘Mini-waterfall’ syndrome There is a multi-disciplinary team of analysts, designers, software engineers, testers, etc. The team forms into distinct sub-teams, where groups of specialists work in batches, handing work over throughout the Sprint. During the Daily Scrum, the following comments may be overheard: “I can’t start on that until you’ve deployed it to the QA environment” “I haven’t finished the specs for that, but maybe you can pick it up tomorrow” “Hmmm, I wonder if we should start having a separate testing sprint after we’re code-complete…” The value of meeting face to face to figure out how to creatively meet shared goals is beaten into submission by rigid “waterfall” handover procedures. Potential Solution: Change the work Some teams really are doing complex work that is a good fit for Scrum, but they are still organising this work in a batch-based or individual-focused way. For a daily scrum to make sense, the somewhat superficial act of batching work together into 2-week chunks is not enough. Fundamental changes to the way the work is done are also required: Find technical solutions enabling non-sequential work, rather than following a mini-waterfall. For example: Implementing automated tests while others write the software Effective use of continuous integration and a well thought-through source control strategy Building (or re-building) the product using modern architectures that allow many developers to work on the system without the need for complicated merge and integration issues Establish a common purpose, such as a Sprint Goal. Creating a high-level business objective at the start of the Sprint gives teams something to focus on during the daily scrum, and if all team members are playing a part in meeting this goal then one of the main barriers to creative whole-team collaboration has been cleared. Reduce the amount of parallel work by helping team mates work together on a single feature. This might be done by pair-programming, especially by people with different specialisms. A database programmer pairing with a UI developer will increase the flexibility and skill levels of the team. 3. Self-organisation is a buzz word that has little practical meaning for the team The team is not engaged in their plan for theSsprint and has no idea how much will be done by the end. The Sprint Backlog is just a dumping ground for tasks, with work routinely carried over from one sprint to the next. Tools like burn-down charts are a Scrum Master thing and the team don’t pay much attention to them. A typical “update” at the daily scrum follows the same well-trodden path: “Yesterday I did task 342 and went to a meeting. Today I will go to another meeting and start task 471. I have no impediments. Very little ever changes. The sole outcome of the daily scrum is that each individual takes 2-3 minutes to make their own plan for the day, with an audience of their peers (probably taking a sneaky look at their mobile phones). Worse still, it becomes a reporting session where developers feel that they have to justify their work over the past day, and be assigned new tasks. Potential Solution: Change the Culture People have a natural instinct to organise themselves in groups, but it takes skill and experience to apply this to building software. This is especially true if people are accustomed to being assigned tasks and do not see the benefit of self-organisation. Even skilled people will struggle if operating within unnecessary constraints. This situation may be improved by: Training the team at the start so that everyone understands this style of working, either informally by the scrum master or by attending a course Establishing clear accountabilities, so the team understand that they are collectively responsible for maximising the value delivered by the end of the Sprint Coaching the team to actively manage their work, keeping track of their forecast and escalating to the product owner if they foresee a need to adjust the scope of the Sprint Working with the leaders of the organisation to establish a culture of autonomy for technical teams, where constraints are limited, and those present are based on genuine IT strategy The Daily Scrum is highly revealing of team dynamics During my time as a Scrum Master I found that this event was highly revealing of the dynamics at play within the teams I worked with. In most cases, problems observed during the daily scrum pointed to deeper problems and were a starting point to help teams improve. In these cases, tackling the symptom directly was ineffective until the root cause had been addressed: Routine lateness for a team in London was not solved by moving the time of the event, but by addressing the unhappiness at a change in technical strategy Technical problems for a distributed team across Europe and Asia were not solved with new video conferencing facilities, but by ensuring that the whole team were included in product strategy meetings A genuinely self-organising team is as powerful as it is rare. Daily Scrums become focused but unstructured as the group takes control and explores the possibilities of the day ahead. Getting there takes time, skill, and commitment on the part of the team; and attention to the things that might be holding them back on the part of their Scrum Master. This article was first published at Agility in Mind.
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Imon Rashid
May 02, 2020
In Business Analysis Forum
Business Analysis is simple! Almost anyone can do it. Just kidding 😊. Actually, as we all know, SDLC is a team work. Since the BA is in between the various stake holders, BA does play a central role. A good BA is a great listener, drills down to details, stays sharp on the basics, resourceful, and gets along with everybody! Let me indulge to elaborate, 1) Listening is so underrated: Listening is how we understand what others or stakeholder/s are saying or not saying. This helps to find out what someone said or didn’t say. It is your job to pet Only after carefully listening we find out what follow up questions to ask and what other factors to consider in the project. Project is served best when all relevant information is considered and collected in the initial stages. 2) The devil is in the detail: Take any topic or part of a project to drill down to the minute detail relating to the project relying on your experience to sift through the details. Attention to detail can make or break your project. 3) Need to know the basics: You seriously need to know the basics of Business Analysis. The complexities of the Software systems are enormous, especially the ERP type systems. Therefore, it is important to know the basics of software development to understand how the developers work and think. How a front end typically interacts with the back end, the Database. Process flow diagrams are critical to visualize the flow of information. Use cases shows the user interaction with the system. These are the basics any good BA worth his or her salt should know. 4) Resourceful: I cannot say enough about this. If You don’t have an answer, go find the person in the organization who has it, search for it on the web. If you decide to approach someone outside the organization be discreet so as not to reveal sensitive information. The BA being in a central position can smooth tensions between teammates or between departments as tension often seem to flare up but do not make a bad situation worse! Never forget the point that you as a facilitator wear multiple hats at the same time! 5) Interpersonal skills: IMHO you catch more flies with honey. Well a BA is not exactly out there to catch flies, but gather information to put together a document. When you put people at ease by your friendly demeanor you are more likely to get the proper information to fulfill your responsibilities.
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Imon Rashid
Jul 04, 2019
In Current Topics
SDLC stands for Software development life cycle. It is a process that describes how to develop, design and maintain the software project ensuring that all the functional & user requirement, goals and objective are met. This methodology improves the quality of the software project and over all process of software development.
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Imon Rashid
Jul 04, 2019
In Current Topics
Is it ever enough ? That is the question ! If you like to take a lot of pictures aka selfies and also download lots of music then you need something like 32 GB storage.
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Imon Rashid
Jul 04, 2019
In Agile Discussions
Scrum is a framework within which people can address complex adaptive problems, while productively and creatively delivering products of the highest possible value. Scrum itself is a simple framework for effective team collaboration on complex products.  Scrum co-creators Ken Schwaber and Jeff Sutherland have written The Scrum Guide to explain Scrum clearly and succinctly.  This Guide contains the definition of Scrum. This definition consists of Scrum’s roles, events, artifacts, and the rules that bind them together.  Scrum is: Lightweight Simple to understand Difficult to master Read more, https://www.scrum.org/resources/what-is-scrum
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Imon Rashid
Jul 04, 2019
In Agile Discussions
MIT Sloan professor Steven Eppinger makes the case that agile applies not just to software development, but to large-scale systems engineering too. Nearly 20 years into the agile revolution, it’s a given that software development has been inextricably altered by the avid embrace of agile practices. Are large-scale systems engineering initiatives next? Steven Eppinger, professor of management science at MIT Sloan, recently laid out a case for applying agile to development realms outside of software. “Every company is trying to be more agile — it’s become part of the regular engineering management lexicon,” he said. “It's shocking how quickly it’s being adopted.” Agile replaces the traditional, linear “waterfall” development model, in which projects are preplanned and fully built out before they are tested, with an iterative process where small parts of projects are being built and tested simultaneously.Companies have embraced agile for a variety of reasons — to boost product innovation, more quickly meet customer demands, accelerate delivery cycles, reduce risk, and improve productivity. But while agile has a proven track record in software development, it hasn’t been put to the test or used extensively in other types of development, such as  manufacturing or engineering, said Eppinger, faculty co-director of the MIT System Design and Management program. In a recent webinar, Eppinger outlined agile practices that, with some minor adjustments, can deliver benefits to other engineering practices and industries. A major telecommunications company, for example, used agile to facilitate the design of hardware and cloud-based connected services Read more , https://mitsloan.mit.edu/ideas-made-to-matter/10-agile-ideas-worth-sharing
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Imon Rashid
Jul 04, 2019
In Agile Discussions
Scrum is defined completely in the Scrum Guide by Ken Schwaber and Jeff Sutherland, the originators of Scrum.  The Scrum Guide is maintained independently of any company or vendor and therefore lives on a brand neutral site.  The Scrum Guide is translated and available in over 30 languages.  You can read and download the Scrum Guide here.
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