Data Literacy for AI: A Guide for Product Managers
Data literacy in AI refers to the ability to understand, interpret, and utilize data effectively...
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Understanding Product Manager (PM) and Product Owner (PO) Roles in the IT Industry
In this article, we will explain what Product Managers (PM) and Product Owners (PO) do...
Customer Loyalty for GenZ
It was not that difficult to retain the Boomers or GenX customers but it’s very...
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Data Literacy for AI: A Guide for Product Managers
Data literacy in AI refers to the ability to understand, interpret, and utilize data effectively within the context of artificial intelligence. For product managers, this skill is crucial for making informed decisions, collaborating with technical teams, and ensuring the success of AI-driven products. Here’s a breakdown of the core competencies required:
1. Understanding Data Types and Sources
- Structured Data: Organized into formats like rows and columns (e.g., SQL databases, CSV files).
- Unstructured Data: Lacks predefined structure (e.g., text, images, videos, social media posts).
- Data Sources: Familiarity with diverse sources such as public datasets (Kaggle, UCI), APIs, web scraping, sensors, and internal databases.
2. Data Collection and Preprocessing
- Data Collection: Techniques include surveys, experiments, web scraping, and automated logging.
- Data Cleaning: Removing duplicates, handling missing values, and correcting errors to ensure quality.
- Data Transformation: Converting data for usability through normalization, encoding, and aggregation.
- Feature Engineering: Enhancing datasets by creating domain-specific features or interaction terms.
3. Statistical and Analytical Skills
- Descriptive Statistics: Key measures like mean, median, and standard deviation.
- Inferential Statistics: Techniques such as regression analysis and hypothesis testing for predictions.
- Exploratory Data Analysis (EDA): Leveraging visualizations and statistical tools to uncover insights.
4. Machine Learning Fundamentals
- Supervised Learning: Algorithms that use labeled data for predictions (e.g., decision trees, linear regression).
- Unsupervised Learning: Identifying patterns in unlabeled data (e.g., clustering, PCA).
- Model Evaluation: Metrics like accuracy, precision, recall, F1 score, and cross-validation.
- Overfitting and Underfitting: Addressing model complexity with techniques such as regularization.
5. Data Ethics and Privacy
- Ethical AI: Tackling bias, ensuring fairness, and maintaining accountability in AI systems.
- Privacy Regulations: Knowledge of GDPR, CCPA, and other laws governing data use.
- Responsible AI: Developing systems aligned with ethical and legal standards.
6. Tools and Technologies
- Programming: Proficiency in Python or R for analysis and machine learning.
- Libraries: Expertise in pandas, NumPy, and SciPy for data manipulation.
- Frameworks: Experience with scikit-learn, TensorFlow, or PyTorch for model development.
- Databases: Competency in SQL and NoSQL databases for data querying and management.
7. Communication and Visualization
- Visualization Tools: Creating insights with Matplotlib, Seaborn, Plotly, or Tableau.
- Storytelling with Data: Presenting findings in a compelling way for non-technical audiences.
- Dashboards: Building interactive tools (e.g., Power BI, Tableau) to track metrics.
8. Critical Thinking and Problem Solving
- Evaluating Data: Assessing reliability, validity, and relevance of sources.
- Questioning Assumptions: Staying open to new insights during data analysis.
- Iterative Approach: Refining models and processes through continuous improvement.
9. Interdisciplinary Knowledge
- Domain Expertise: Understanding industry-specific contexts (e.g., healthcare, finance).
- Integrative Thinking: Drawing insights across disciplines to build robust AI solutions.
Mastering these skills requires continuous learning and practical application to stay relevant in the ever-evolving AI landscape. By developing data literacy, product managers can drive smarter decision-making and unlock the full potential of AI in their products.
Understanding Product Manager (PM) and Product Owner (PO) Roles in the IT Industry
In this article, we will explain what Product Managers (PM) and Product Owners (PO) do in the IT industry.
A Product Manager seeks information about what customers need, how to satisfy them, and how to encourage them to buy the product. They focus on strategic decisions and long-term product vision.
On the other hand, a Product Owner focuses on prioritizing requirements, planning what to deliver in the next three, six, or twelve months, and ensuring that Agile practices are implemented effectively. They aim to optimize the delivery process.
Now let us delve deeper into the key differences between the two roles and their responsibilities.
Building a Product Vision
Developing a product vision is not an easy task. A PM seeks to answer what and why the product is being built and assesses its long-term potential. Using well-researched data, the PM creates a realistic and inspiring vision that aligns with organizational goals.
A strong product vision helps the PM set expectations about market entry, growth, and strategies for mitigating declining trends.
Keeping Customers in Mind
To build a successful product, knowing the customer is essential. PMs conduct various research methodologies, including primary research (e.g., interviews, focus groups) and secondary research (e.g., market reports, competitor analysis).
They create detailed personas to segment customers and tailor the product to meet specific needs. Remember, one size does not fit all when it comes to customer satisfaction.
Thinking About KPIs
While developing products, it’s vital to adopt a KPI-driven approach to measure success. For instance, if your KPI is reducing customer returns by 10%, you need to investigate why customers return products—wrong size, quality issues, defective items, etc.—and collaborate with the Business Intelligence team to design effective solutions.
User Research
Understanding users is the foundation of building a great product. User research involves:
- Observing user behavior and pain points.
- Conducting surveys, interviews, or usability testing.
- Creating journey maps to identify gaps in the customer experience.
The insights gained from this research guide the PM in creating features that truly resonate with users.
Knowing Risks & Dependencies
Risk assessment is a crucial responsibility for both PMs and POs. Identifying risks—be it technological limitations, market uncertainties, or resource constraints—early in the product lifecycle can save time and money.
Similarly, mapping dependencies ensures that all teams, systems, and processes align seamlessly to meet delivery timelines.
Knowing the Priority
Prioritization is key to delivering value. The PO uses frameworks like MoSCoW (Must-Have, Should-Have, Could-Have, Won’t-Have) or Weighted Scoring to determine what features to deliver first.
This ensures that teams focus on high-impact work without being distracted by lower-priority tasks.
Product Pricing
Pricing is a strategic decision that reflects your product’s value and competitive positioning. PMs analyze market trends, customer willingness to pay, and competitor pricing models to decide on the most effective pricing strategy—be it freemium, subscription-based, or premium pricing.
Aligning with the Delivery Team
The PO bridges the gap between the product vision and delivery. They ensure that Agile ceremonies like sprint planning, daily stand-ups, and retrospectives are conducted effectively.
By maintaining a clear and prioritized backlog, the PO helps the delivery team stay focused and aligned with business goals.
Keeping the Budget in Check
PMs often act as custodians of the product budget, ensuring that resources are allocated wisely. Tracking burn rates, avoiding scope creep, and constantly re-evaluating ROI ensures the product remains financially viable.
Additional Suggestions
Here are a few more aspects that could be explored to enrich your PM journey:
- Collaboration Between PM and PO: How these roles complement each other and foster a healthy balance between strategy and execution.
- Product Roadmap Planning: The role of PMs in creating roadmaps and aligning stakeholders.
- Stakeholder Management: Handling internal and external stakeholders effectively.
- Tools & Techniques: Discuss tools like JIRA, Trello, or Aha! used by PMs and POs to streamline their workflows.
Customer Loyalty for GenZ
It was not that difficult to retain the Boomers or GenX customers but it’s very difficult to engage the GenZ in terms of loyalty.
They try to shop from different places.
Now in order to make them loyal customers, the sellers have to understand the psychology behind the GenZ.
If you deeply look at the way they have been brought up; they have been playing video games, they are active on social networking platforms, they love challenges. We have to use these psychological senses in our way to position our brands.
Attracting and retaining Gen Z customers requires a tailored approach that aligns with their values, preferences, and behaviors. Here are some strategies to attract and retain Gen Z as loyal customers:
- Authenticity: Gen Z values authenticity and transparency. They can easily spot inauthentic marketing tactics. Ensure your brand messaging, content, and interactions are genuine and transparent.
- Engaging Content: Gen Z is highly engaged with digital content, especially on social media platforms like TikTok, Instagram, and Snapchat. Create visually appealing, interactive, and relatable content that resonates with their interests and values.
- Social Responsibility: Gen Z is passionate about social and environmental issues. Demonstrate your commitment to social responsibility through sustainable practices, ethical sourcing, and involvement in causes they care about. Incorporate purpose-driven initiatives into your brand identity.
- Personalization: Gen Z expects personalized experiences. Utilize data analytics and AI to gather insights about their preferences and behaviors, then tailor your products, services, and marketing efforts accordingly. Personalized recommendations and offers can enhance their shopping experience.
- Mobile Optimization: Gen Z is a mobile-first generation. Ensure your website, apps, and digital platforms are mobile-friendly, easy to navigate, and optimized for fast loading speeds. Implement mobile payment options to streamline the purchasing process.
- User-generated Content: Encourage user-generated content (UGC) by involving Gen Z customers in your brand storytelling. Run contests, challenges, or campaigns that prompt them to create and share content related to your brand. UGC fosters community engagement and authenticity.
- Embrace Technology: Gen Z is tech-savvy and early adopters of new technologies. Incorporate innovative technologies such as augmented reality (AR), virtual reality (VR), or gamification into your marketing strategies to captivate their interest and drive engagement.
- Social Proof: Gen Z heavily relies on peer recommendations and online reviews when making purchasing decisions. Encourage satisfied customers to leave reviews and testimonials on social media platforms and review websites. Leverage influencer partnerships to amplify your brand’s credibility and reach.
- Fast and Convenient Service: Gen Z values convenience and efficiency. Offer fast shipping options, hassle-free returns, and responsive customer support to meet their expectations for convenience and service excellence.
- Continuous Engagement: Maintain ongoing communication and engagement with Gen Z customers beyond the point of sale. Utilize email marketing, social media, and messaging apps to stay connected, share relevant content, and nurture long-term relationships.
By incorporating these strategies into your marketing and customer experience efforts, you can attract Gen Z customers and foster their loyalty to your brand over time.
Mastering Customer Research in Product Management
One of the primary responsibilities of a Product Manager (PM) is conducting successful research during product development. Customer research is pivotal for creating products that truly resonate with users, but its complexity can overwhelm even seasoned professionals. For those new to the process, the question often arises: Where do I start?
To simplify this, I’ve outlined a comprehensive framework that PMs can follow or adapt to their specific needs while conducting research. This guide provides clarity on objectives, types of research, methodologies, and steps for actionable insights.
1. Research Objective: Define the “Why” and “What”
Every research journey begins with a clear purpose. Before diving into methodologies, ask yourself:
- Why are we conducting this research?
- What outcomes do we aim to achieve?
- What if this research isn’t conducted?
Defining a strong objective ensures your research has a clear scope and aligns with broader product goals.
2. Types of Research: The Three Pillars
Identify the type of research needed to address your objectives. Customer research typically falls into three categories:
a. Market Research
Market research focuses on understanding the larger ecosystem around your product. Key areas include:
- Market size estimation (e.g., TAM, SAM, SOM)
- Future market trends and growth potential
- Macro and microeconomic factors influencing the market
- Guesstimation techniques for decision-making
b. User Research
User research digs into the needs, behaviors, and preferences of your target audience. Explore these facets:
- Needs, Wants, and Wishes: What drives your customers?
- Current Usage: What solutions do they currently use?
- Problems: What challenges or pain points do they face?
- Pricing: What price point resonates with them?
- UI/UX Preferences: What design elements appeal to them?
- Loyalty: How can we ensure customer retention?
- Spending Habits: What are their budget constraints or spending patterns?
c. Competitive Research
Understanding your competitors is vital. Investigate the following:
- Competitor Analysis: Who are the key players?
- Success Factors: Why are their products successful?
- Gaps: What gaps exist in their offerings, and why?
3. Research Methodologies: From Data to Insights
Once the type of research is identified, choose your methodologies:
Primary Research
- Conduct surveys, interviews, or focus groups to gather firsthand insights.
- Perform usability testing with prototypes or live products.
Secondary Research
- Analyze market reports, case studies, and competitor reviews.
Data Collection and Analysis
- Data Collation: Organize data systematically for clarity.
- Sample Size: Ensure your sample is representative of the target audience.
- Synthesis: Interpret data trends, group information logically, and forecast potential outcomes.
4. Implementation: Driving Actionable Insights
After collecting and synthesizing data, integrate findings into your product strategy:
- Feature Prioritization: Focus on features that align with user needs.
- Value Proposition Refinement: Highlight unique aspects of your product.
- Marketing Strategy: Craft messaging that resonates with your target audience.
5. Ongoing Research: Iterate and Adapt
Customer research isn’t a one-time effort. Continuously gather feedback post-launch to refine your product and stay ahead of market trends.
Conclusion
Customer research in product management is a structured yet adaptable process. By defining clear objectives, selecting the appropriate research type, and utilizing robust methodologies, PMs can uncover valuable insights to drive product success. Remember, the best products aren’t just built—they’re built around the customer.
Roadmap to Becoming an AI Product Manager
This article covers essential aspects of transitioning into an AI Product Manager role for existing or potential PMs.
The rise of AI has transformed the landscape of product management, creating opportunities to innovate and enhance user experiences. This guide outlines the key skills and knowledge areas to excel as an AI Product Manager (AIPM).
1. Master the Fundamentals of AI
- Key Concepts: Understand the differences between AI, machine learning (ML), and deep learning (DL). Familiarize yourself with terms like neural networks and natural language processing (NLP).
- Real-World Applications: Explore industries where AI delivers value, such as personalized recommendations in e-commerce, predictive analytics in healthcare, and fraud detection in finance.
2. Cultivate Data Literacy
- Data-Driven Decision-Making: Learn how data fuels AI models and product decisions.
- Data Collection: Recognize the importance of clean, relevant, and unbiased data.
- Privacy and Ethics: Understand data privacy regulations like GDPR and ensure your product aligns with ethical AI practices.
3. Learn AI Techniques and Algorithms
You don’t need to become a data scientist, but familiarity with AI methods is crucial:
- Foundational Algorithms: Gain a high-level understanding of regression, classification, clustering, and reinforcement learning.
- Machine Learning Basics: Differentiate between supervised, unsupervised, and reinforcement learning.
- Deep Learning: Explore applications of neural networks in image recognition, NLP, and recommendation systems.
4. Integrate AI into Products
- Opportunity Identification: Look for areas where AI can solve user pain points or improve functionality.
- Impact vs. Feasibility: Prioritize initiatives that balance business impact and technical feasibility.
- Case Studies: Study successful AI integrations in e-commerce, finance, and healthcare to identify best practices.
5. Collaborate with AI Teams
Building synergy with technical teams is crucial:
- Understand Workflows: Learn how AI models are developed, trained, and deployed.
- Effective Communication: Clearly articulate product requirements and align them with AI capabilities.
- Hands-On Tools: Familiarize yourself with platforms like Jupyter Notebooks, TensorFlow, and Power BI to better understand team workflows.
6. Evaluate AI Performance
Measure the success of AI initiatives through robust evaluation frameworks:
- Performance Metrics: Monitor accuracy, precision, recall, F1 score, and other relevant KPIs.
- User Impact: Regularly assess how AI features improve user experience and align with business objectives.
7. Address Ethical and Regulatory Considerations
- Bias and Fairness: Understand common biases in AI models and implement strategies to minimize them.
- Legal Compliance: Stay informed about AI-related regulations such as GDPR and industry-specific standards.
8. Embrace Continuous Learning and Adaptation
The field of AI evolves rapidly, and staying ahead is essential:
- Stay Updated: Follow industry leaders, attend AI conferences, and subscribe to research journals.
- Experimentation Culture: Encourage small-scale experimentation to validate AI features before scaling them.
9. Develop an AI Strategy
- Vision Alignment: Create a long-term roadmap for integrating AI into your product vision and strategy.
- ROI Analysis: Evaluate the return on investment for AI initiatives to ensure they contribute to business goals.
Resources for Aspiring AI Product Managers
- Courses: Enroll in AI-focused product management courses on platforms like Coursera, Udemy, or edX.
- Books: Read “Artificial Intelligence for Product Managers” by Bruno Aziza and other industry texts.
- Communities: Join forums, webinars, and professional networks focused on AI and product management.
Tips for Success
- Cross-Functional Collaboration: Partner with engineering, data science, and UX teams to bring AI features to life.
- User-Centric Approach: Prioritize user needs to ensure AI solutions provide tangible value.
- Iterative Refinement: Use data and feedback loops to continually enhance AI-driven features.
Conclusion
Becoming an AI Product Manager requires a blend of technical understanding, strategic thinking, and user empathy. By mastering these areas, you can confidently lead the integration of AI into products, driving innovation and value for your users and business alike.
Importance of Domain Knowledge for a Product Manager
In today’s world domain knowledge is very important, not only for the Product Manager but also to other like Business Analyst, Product Owner and Solution Architect to know customer’s business well.
Now the question is domain itself is a big chapter all together. Do we need to know everything?
The answer is NO, you don’t need to know everything to help you customer in domain consulting. But you must focus on deep knowledge on a particular area of the specific domain.
For example, if you are expert in Retail domain, it does not necessarily mean you have to know everything about Retail domain.. you may know a bit about e-commerce, Brand loyalty, Retail operation, Retail supply chain management, Retail consumer behavior, Retail customer experience, Retail compliance, Retail technology trends (like AR, VR etc.).
Now, coming to the topic why it’s helpful for the business/product/ solution people know know a bit about a domain..
- Understanding User Needs: Having domain knowledge allows product managers and business analysts to better understand the needs, pain points, and preferences of users within a specific industry or domain. This understanding helps them create products and solutions that effectively address customer requirements.
- Effective Communication: Domain knowledge enables product managers and business analysts to communicate more effectively with stakeholders, including customers, development teams, and executives. They can speak the language of the domain, understand technical requirements, and translate business needs into actionable insights.
- Identifying Opportunities: With deep domain expertise, product managers and business analysts can identify market opportunities, emerging trends, and areas for innovation within their industry. This allows them to develop strategic product roadmaps and business plans that align with market demands.
- Making Informed Decisions: Domain knowledge empowers product managers and business analysts to make informed decisions based on a thorough understanding of the industry landscape, competitive dynamics, and regulatory environment. They can assess risks, evaluate trade-offs, and prioritize initiatives more effectively.
- Driving Product Innovation: Product managers and business analysts with domain knowledge are better positioned to drive product innovation and differentiation. They can leverage their understanding of customer needs and industry trends to conceptualize new features, functionalities, and solutions that add value to the product.
- Enhancing Stakeholder Relationships: By demonstrating domain expertise, product managers and business analysts can build trust and credibility with stakeholders, including customers, partners, and internal teams. This fosters stronger relationships and collaboration, leading to more successful product launches and initiatives.
- Optimizing Product Performance: With a deep understanding of the domain, product managers and business analysts can monitor and analyze key performance metrics to track product performance, identify areas for improvement, and make data-driven decisions to optimize product outcomes.
- Mitigating Risks: Domain knowledge allows product managers and business analysts to anticipate potential risks and challenges associated with product development and implementation. By proactively identifying risks, they can develop contingency plans and mitigation strategies to minimize negative impacts on the business.
- Facilitating Cross-Functional Collaboration: Product managers and business analysts often work closely with cross-functional teams, including engineering, marketing, sales, and customer support. Domain knowledge enables them to collaborate more effectively with these teams, aligning efforts towards common goals and ensuring successful product delivery.
Overall, domain knowledge provides product managers and business analysts with a valuable foundation for understanding customer needs, driving innovation, making informed decisions, and ultimately delivering successful products and solutions within their respective industries.
Presales Job Responsibilities for a BA
Ideally a BA works in project or operation. Sometimes due to lack of dedicated resources, our organizations tend to involve BAs in presales activities. Ideally an IT organization should have a dedicated team for presales consultants. But in reality, we see that along with the project work, BAs are involved in presales activities. So the million dollar question is can a Business Analyst get into a Presales job dedicatedly? The answer is yes they can provided they have gained enough experience in certain areas. You may start your job as a fresh BA but not as a fresh Presales Consultant.
Let us understand what we mean by Presales job. There are various definitions depending upon the organization and the business process. For example, some organizations consider “cold calling as pre sales“. I am not referring to that profile. Here we are concerned about IT presales (mainly software). As it happens in IT organization, you have an Account Manager who is purely into Sales and his key performance indicator is sales target attainment but when it comes to presales guy, he will not have any sales target but he will have to create technical proposal, coordinate with an architect to get an estimate and high-level project plan. So the presales person will mainly work closely with the Account Manager to build the proposal including solution and cost estimate. Now the overall responsibility of the presales consultant varies from company to company; e.g. in some organizations the presales person is also involved in customer demonstration of the solution, presales clarification (questionnaire session). Remember we are talking about all these activities when there is a sales lead in the presales state. So the process looks like below –
RFI / RFP / RFQ Process –
- A potential customer is interested >
- Account Manager gets in touch to understand what the customer needs >
- The customer wants to understand your company’s capability on a ERP/CRM >
- AM gets in touch with Presales Consultant to get Capability document on ERP/CRM >
- Customer is interested for a proposal >
- AM will bring in the Presales guy in the picture to discuss the requirements with customer >
- After that Presales person will prepare a proposal, consult with Architect on the solution, risk, assumptions, estimate >
- The proposal is shared with AM who will schedule another appointment for solution Demo >
- Presales person will join the demo to showcase the solution and handle customer queries >
- AM will close the deal >>>>> After the order is received the BA will be involved to gather project requirements; so here the project work will start as per SDLC >>>
What a BA has to know about Presales?
As a BA you must understand that in Presales we do not get into the functional or technical requirements but only on high-level business requirements. Based on this we have to provide a high-level solution and estimate our efforts, and provide a timeline. So a lot of assumptions, dependencies, risks need to be considered. You should be clear on the in scope and out of scope work items. You should also have good knowledge about your organization’s solutions, accelerators, frameworks etc. so that you can plug them in as part of your solution. Presales consultants must be able to articulate the Value Additions for the potential clients and show the Unique Selling Propositions.
Once you start working on some of the RFPs on Implementation, Consulting, Upgrade, Support & Maintenance and build solution documents (Proposal) you will understand the basic expectations and over a period of time you will continue to improve.
How does it differ from BA Job?
Well, every day or week you will see new requirements, new solutions, new industry. So you have to be very dynamic to adapt. Also knowledge on various industries, technology trends (AI, Machine Learning, Big Data, DevOps, Cloud, Digital Platform like AWS, Azure), software integration (SOA Architecture, Service Bus, ETL, Batch Jobs, Real Time, API) etc. will definitely add value towards your contribution.
Strategy for offshore Business Analysts
Business Analysis is a job that makes more sense when you work closely with customers at onsite locations but it doesn’t always happen that way. Many a times Business Analysts work from offshore and help the technical team clarify key questions. The offshore BA sends questions through email, schedules virtual meetings with business stakeholders and tries to get clarity on requirements. Now, all of a sudden, an onsite person from your team starts to interact with the customer and takes the lead on requirement clarification but you know it will not work as you have been through the questions and you only know the background of them. Hence the onsite person needs a knowledge transfer from you so that he understands the intention of the questions. You seem to feel bad that why you are sitting in offshore and not at the client side. You may have your personal reasons to stay back in offshore but it’s painful sometimes.
What should be your strategy when as a BA you are stuck in offshore:
- You cannot be a generic BA in offshore, rather you need to know the technology to become Functional Consultant so that you can discuss key techno-functional aspects with developers and suggest a solution to your client.
- You cannot be in a 9 to 6 job, rather make yourself flexible to overlap the office schedule to spent some hours with customer schedule either early morning or late night.
- Define a clear responsibility for the onsite person of your team. Both of you should not do the same set of work, rather ask him to take handover from where you leave it day before.
- Make sure you are not left out from some critical meetings that happen in onsite. If the schedule doesn’t match with you, ask someone to record the meeting and share with you.
- Always take email or chat approvals from your stakeholders for any decision you make. Do not assume. Keep all approval mails in a single folder.
- Don’t take up unimportant and unnecessary works just because you are in offshore. Avoid saying YES. Learn to understand your role in the project and focus on the specific outcomes only.
- Don’t get too much involved in outside project work to impress your boss. If you manage some time, learn something, document it, align it to your current engagement and share it with others to show your Knowledge.
BAs in offshore may face a lot of issues for not being available in the client site. I have tried to highlight some points so that you can enjoy your place and still be key player in the team.
Will you be a Business Analyst for ever?
Well many people argue that Business Analysis is a lifelong profession but I have a different take on this topic.
The Career Architecture for a BA mainly depends on his or her learning abilities and applications on the same. If you start your career as a Junior BA, you work under the guidance of a Senior BA and your responsibilities are scheduling meeting with stakeholders, taking meeting notes, preparing BRD.
Have you ever noticed what the Senior BA is upto?
Well, he is probably working with more complex requirements and explaining the problem to the technical team. He is also giving input for Technical Design.
If you observe one ladder up, you will probably see the there are BA Managers, Functional Architect, Solution Architect who are working on more complex assignments.
So my point is never consider yourself a BA for lifetime. We all know requirements are key in any project and as a BA we have to be on top of it but you also need to see what next. Get involved in Design discussions, don’t hold yourself back because you are a BA. With more experience you will probably be able to guide the tech team in Design.
But….. before you go there maintain the flow of your understanding as below –
Business Requirements Clarity > Functional Architecture and Process Clarity > High-level Design Approach > Low-level Design Approach
Make yourself comfortable but don’t be too proud to restrict yourself to chop the onion. You will end up learning more, you will end up creating value.
Career Options for Business Analysts:
Continue as Business Analyst:
Product Manager:
Product Owner:
Domain Expert:
Functional Expert:
Process Consultant:
Pre-sales Consultant:
Business Architect:
Iteration Manager:
5 Crucial Factors for Agile Business Analyst
Most of the Business Analysts are now working in Agile engagements. We all know that a Business Analyst in Agile project will work as a Product Owner and he should be responsible for the product backlog (requirements). In this article I’m going to highlight crucial factors that an Agile Business Analyst should take into consideration.
- Business Priorities – You may know all the requirements that are listed in product backlog but that doesn’t mean you know the business priorities. With each and every requirements you must have a priority ranking which will be provided by Business Stakeholders. Do not assume the priority and add the stories in sprint. Also take help from Business team or sponsor to know which epic or story should be part of which sprint.
- Requirements Mapping – Whenever you add a user story in the sprint, you should also validate the related user stories and map them logically. Suppose you have added a user story for customer’s dashboard design, then you should also look for what data are required on the dashboard and which system should provide the data. In this scenario you should create two user stories, one for UI and another for Data. Finally map them in Jira.
- Team Structure – Don’t always depend on the scrum master to take the call on team’s capabilities. You must know the capacity and capabilities of your team members in terms of technical Knowledge and delivery assurance. Blindly don’t pick up any story that will be too complex for the team to deliver, rather based on their capabilities you may split the complex story in small ones.
- Testing Team – As a Business Analyst you are supposed to clarify all the requirements not only to the development team but also to testing team. Sometimes you will have to handle conflicts between development and testing teams when they argue on a defect.
- Agile Learning Curve – However it’s scrum master’s responsibility but still you should also measure if the team’s learning curve is inclined or declined. All the members should improve their productivity as they progress in the sprint. E.g. a functionality took 5 days to develop in sprint 1, should take much less time to develop in sprint 5 if the requirements are similar. Also it should require less discussion for clarification.
Agile is vast area for project management prospective but a Business Analyst one must ensure there is a common understanding across the team members on the requirements and functionality. Hence the 5 Crucial Factors are undeniably the responsibility of Business Analyst during Agile engagement.
How to Analyze Requirements?
Requirement Analysis is a sequential process in which we try to unfold the deep secrets. As a Business Analyst, your analytical skill is going to be your goodwill. Ideally a BA has to perform the requirement analysis, however the other team members can also support and contribute.
Before we start requirement analysis we must have the requirements handy either as BRD or any other template.
The sequence that I follow –
- Functional Architecture Design
- Integration Requirements
- Functional Requirements
- User Requirements
- Report / Statement Requirements
- Nonfunctional Requirements
Sometimes functional requirements are analyzed before integration requirements. Both ways are fine as long as we can keep a balance between the 6 areas of requirement analysis.
I will post seperate post on each areas.
The deliverables of Requirements Analysis is Functional Specification Document (FSD) or Functional Requirement Document (FRD). In addition we also produce –
- Use Case,
- Data Dictionary,
- Data Mapping,
- Activity Diagram,
- Sequence Diagram,
- Class Diagram etc.
We will later learn about Unified Modeling Language (UML) to understand more about the diagrams.
I will also suggest to go through http://mybatrainer.com/what-is-functional-knowledge-for-a-business-analyst/
How to Gather Requirements?
Many formal techniques are used to gather requirements from stakeholders. Before we go into the formal techniques, I would advise the BA to be prepared and disciplined.
- You must know your stakeholders and their roles in the project
- You must know what requirements you are going to gather
- You should write your questions before
- You should know the key business terminology of your stakeholders
- You should conduct some web research on the topic or business process before meeting the stakeholders
If above points are taken care of, let’s now go and elicit the requirements.
- The meeting is scheduled and you introduce yourself to the stakeholders and they also do the same
- You should first discuss and decide on requirement gathering approach. You start the topic and let the conversation begin. You need to provide your input on various approaches and their pros and cons. You can suggest a mixed approach also, e.g. interview, document analysis, questionnaire techniques etc.
- Now you start gathering the requirements step by step
- Let’s know the vision, objective and reason for the project
- What are the challenges they are facing in the current situation
- What should change
- Why the change is required now
- How will the new software or system will help
- Where does the software fit in within the enterprise landscape
- What are the risks they foresee
- What are the dependencies and assumptions
- What is the expected and ideal future landscape
Now we have to get into the current and future landscape.
After that we will discuss on users, teams, security aspects.
Let’s get functional.
Let’s understand non functional.
Will update this post.