Hey, BA Community! 👋

Thinking about starting your IT career or just want to level up your skills? Andersen has got your back, offering FREE courses for beginners that are perfect for anyone who’s ready to dive into the world of IT.

🌟 Business Analysis — November 2025
Want to be the bridge between business and tech? This course’s got you covered!
Apply here

🌟 UX/UI Design Intensive — September 2025
Get hands-on with design and create awesome user experiences!

🌟 QA Manual & Java — October 2025
Learn the ropes of quality assurance and get started with Java programming.

No catch, just good learning and great opportunities! ✨

Hey, dear Community! The much-awaited and biggest event is happening soon this October! 🔥

On October 16, we’ll reveal the secret at a BA meetup with experts from Andersen and IREB.

🎙 Radosław Grębski, Lead Business Analyst and Business Analysis Trainer, will speak on: “From Communication to Validation: Why Model Requirements?”
We’ll take a closer look at best practices and common mistakes in requirements modeling.

🎙 Emil Abazov, PSPO I™, Senior Business System Analyst and Product Owner, will share his experience: “Turning Requirements into Action: Modeling in Practice.”
Through diagrams and models, we’ll explore how to prevent misunderstandings and improve collaboration between business and technical teams.

➡️ Register here

Live discussions, insights from experts, and a professional community — all this at our BA Meetup!

⏰ Time: 18:00 (CEST)
⏳ Duration: 2 hours
🗣 Language: English
📍 Offline: Andersen’s office in Warsaw
💻 Online: The link to the stream will be sent to your email specified in the registration form.

See you!

📊 BPMN 2.0: Simplifying Complex Processes and Driving Digital Transformation

✨ We all, of course, know and have used bpmn 2.0 for modeling to show how a business process works or sometimes to show how different components of a process are connected to each other.

👌 We have used it as a visual representation that helps us explain complex processes in a simple and understandable way for everyone.Thanks to its rich notation, BPMN 2.0 is used today not only as a process modeling technology, but also as a process automation tool where no code is needed or sometimes simple code is needed.

✅ It allows people without much technical knowledge to create practical backend processes, perform various integrations such as databases, message brokers, third-party systems, perform logical checks, etc.

👉 This model is widely used in industries such as fintech as they modernize their technologies, they are designed to use such tools to minimize complex logic in their backend and make future changes easily. Startups that want to quickly test new business models without a backend team and so on.

📍Here is the list of platforms that I have used during my work: Camunda 8, Smart Vista Integration Platform, ProcessMaker. Have you ever used such technology, and what do you think about its future?

#WorkflowAutomation #ProcessAutomation #DigitalTransformation #FutureOfAutomation #ProcessOrchestration #AutomationStrategy

📎Data mapping tool for smooth legacy transfer

❗️Orphaned records, inconsistent data formats, unclear parameters - sounds familiar?

For a BA on a migration project, such challenges are routine. Data mapping is the key tool to reduce risks by linking Source and Target systems, defining transformation logic, and documenting parameters.

In order to bring it to action, follow these seemless steps:

Step 1: Define Source and Target systems
Source system - the existing system that currently stores the organizational data.
Target system - the destination system where the data must be migrated.

Step 2: Identify Data Entities and Attributes
Extract a data inventory of entities, tables, fields, and attributes from both systems. For example:
Source entity: Customer → Attributes: Customer_ID, Name, Address, Phone
Target entity: Client → Attributes: Client_ID, FullName, Street, MobileNumber

Step 3: Define Transformation Rules
Map how each source field aligns to a target field. Transformation rules may include:
Data Type Conversion (e.g., VARCHAR(50) -> TEXT)
Format Change (e.g., date format DD-MM-YYYY -> YYYY-MM-DD)
Data Cleansing (removing duplicates, correcting errors, trimming spaces)
Business Rule Adjustments (e.g., combining First Name + Last Name into FullName)

Step 4: Define Mapping Relationships
Establish 1:1, 1:Many, or Many:1 mappings between fields/entities. For example: One "Customer" record in the legacy system may map to multiple "Client_Contacts" in the new system (1:Many).

Step 5: Validate and Document Business Rules
Document mandatory vs. optional fields. Identify default values if data is missing.

✅ Once these steps are completed, your Data Mapping Document is good to go. Your team has a formal artifact describing how data fields in the legacy (source) system correspond to those in the new (target) system.

#SystemAnalysis #DataMapping #LegacySystems #BusinessAnalysis

Hi, dear Community! 👋

We together with BA Community representatives recently attended the IIBA Poland Summit 2025 and brought back a wealth of valuable insights for all of you - here are some moments we captured from the event for you! 📸

This remarkable event provided us with practical knowledge and fresh perspectives on how business analysts and change professionals can unlock potential, align with strategic goals, and create measurable impact. 💡

Organized expertly by the IIBA Poland Chapter, the summit united passionate, talented professionals who are deeply committed to their craft. Throughout the event, our team engaged with industry experts, learned about the latest trends—including AI’s transformative role in business analysis—and explored innovative approaches that will improve our work.

Stay tuned for more detailed takeaways and updates from our team as we continue to learn and grow together!

#IIBA2025 #BusinessAnalysis #BACommunity #ProfessionalGrowth #Networking

📊 How State machines make businesses more transparent

One of SA goals is to kill ambiguity in the product’s business logic and documentation. And one of great weapons for creating crystal-clear understanding is the State Machine Diagram.

It’s a powerful communication tool that builds a bridge between business stakeholders and developers, fostering incredible transparency.

Where Do We Use State Machines?

💡Order Management: Draft → Paid → Shipped → Delivered

💡Loan Application: New → Under Review → Approved/Rejected

💡Support Ticket: Open → In Progress → Resolved → Closed

State Machine Diagram describes the discrete states an object or system can be in and the events that cause transitions between those states.

The key elements include:

1. State: A distinct stage in the life cycle of an object (e.g., Shipped).
2. Initial State: The solid circle representing the starting point.
3. Final State: The bullseye circle representing the end of the life cycle.
4. Transition: The arrow showing movement from one state to another, triggered by an event.
5. Event: The trigger that causes a transition (e.g., Customer Pays, Shipment Scanned).
6. Guard Condition: A boolean condition (in [brackets]) that must be true for the transition to occur (e.g., [payment validated]).

The finalized diagram becomes a precise spec for Database (The status field's allowed values), UI (Which buttons to show based on the current state), Analytics ("Why do orders get stuck in Awaiting Response?") etc.

You move from messy, paragraph-long denoscriptions to a single, shared visual. Everyone - from the CEO to the intern - understands the rules. This eliminates confusion, ensures the system is built right, and builds trust.

#SystemsAnalysis #BusinessAnalysis #UML #StateMachine #ProcessImprovement #TechTransparency #SoftwareDevelopment #BusinessProcess

📊 Why the Wrong Diagram Can Mislead Your Project

Diagrams are a powerful tool for system analysts — but also a dangerous one. A wrong diagram can be the root of major mistakes.

❗️ A common trap: choosing the wrong type. Drafting a UML use case to show system interactions instead of a sequence or context diagram. Result? Clients imagine one thing, developers build another, and QAs are left guessing.

The problem isn’t the notation. It’s forgetting that a diagram is not the end goal — it’s just a way to communicate.

Here’s the hidden risk: an over-detailed diagram can mislead more than a simple one. It creates an illusion of completeness — the team stops asking questions and assumes the picture is final. But truth is born in questions.

Practical tips:

💡 Always ask: who is this diagram for?
💡 Match the level of detail to the audience.
💡 Sometimes a short text or table explains more than a polished diagram.

✅ Diagrams should clarify, not confuse. And it’s the analyst’s job to make sure they work for the team, not against it.

#SystemsAnalysis #BusinessAnalysis #UML #VisualThinking #AnalystMindset

🌐 AI Integration in Business Analysis: From Prompts to Practical Workflows

Artificial intelligence has become an integral part of business and systems analysis — no longer a curiosity but a daily necessity.

From processing large datasets to automating reports, prototypes, and forecasts, analysts increasingly rely on AI-powered tools for text generation, coding assistance, image processing, and behavioral analytics.
To stay effective and influential in shaping business decisions, it’s not enough just to use these tools — it’s crucial to understand how they evolve and how to embed them into real analytical workflows.
Keeping your ear to the ground is key: regularly tracking updates and new features helps analysts stay ahead of the curve.

Below are three of the most relevant AI updates from the past week that may be useful for business and system analysts 👇
💻 Microsoft 365 Copilot introduced a new “Copilot Actions” mode in Windows 11, allowing analysts to delegate app, file, and task management to the system with minimal human input.
⚙️ Google Gemini updated its Code Assist feature — improving code support and adding new capabilities for developers, analysts, and system integrators.
☁️ YandexGPT, part of Yandex Cloud, expanded its model quotas and limits — a significant update for analysts working with scalable cloud-based AI solutions.

For business analysts, these updates mean three things:
1️⃣ Faster execution of routine tasks and reduced manual input.
2️⃣ A chance to test new AI features and adapt workflows accordingly.
3️⃣ The need to evaluate which innovations truly add value — by improving accuracy, saving time, or enhancing integration between systems.

Keep track of such developments every week — and let AI work for you, not the other way around. 💡

#BusinessAnalysis #SystemAnalysis #AIforBA #PromptEngineering #ArtificialIntelligence #AIAssistants #DataAnalysis

Operationalizing Dark Data: How to Get from Use Cases to Value Cases

🌠 “Dark matter” is not only about physics. Thousands of companies and organizations sit on piles of dark data - collecting and storing tons of data but never using them. Whether it is ancillary data or data required for compliance, their storage and handling incur more expense than benefit. So why not monetize them or at least extract business value?

Approaching dark data with traditional use cases (describing behavior) might be painful early on. Shifting to value cases (stating measurable outcomes) can help spark better ideas. This move is from “what the system does” to “what business result it delivers”, and dark data is the fuel.

✅ Example: “Cut the contracting cycle by 20% by activating legacy contract PDFs and emails to auto-surface clauses, risks, and owners” instead of pure scenario thinking “User uploads contract; system stores PDF; legal reviews.” This rephrasing helps to prioritize the highest-value uses of dark data and tie it to a decision.

While structured, contextualized data eases generating value cases, most of the untapped data remains unstructured. To make it actionable, AI can provide a solid support in these steps:

1. Digitalization (if needed): for example, OCR (Optical Character Recognition) for physical documents, ASR (Automatic Speech Recognition) for audio.

2. Classification: indexing and labelling data so the system retrieves the right pieces of information to the right people (permission-based access).

3. Retrieval + generation: using RAG (retrieval-augmented generation) to provide reliable answers, and routing the edge cases to humans.

4. Non-functional requirements: tracking latency, accuracy, coverage, measuring cost per query, monitoring model/data drift (→ reindex/retrain regularly).

❗️ Check everything works correctly
Acceptance criteria: answers include clickable citations; access controls enforced; low-confidence responses escalate by rule.

📏 Measure impact on business
Business metrics: faster cycles and shorter time-to-resolution, fewer do-overs and escalations, happier users (better user satisfaction scores), more value cases and insights for business initiatives.

💡 Takeaway: activate one dataset against one decision, measure impact, and iterate. Prove the value case, then scale.

#BusinessAnalysis #SystemAnalysis #DarkData #BusinessValue #ValueCases #DataLineage

Hello, BA Community! 👋

Congratulations to all of you on the recent Global Business Analysis Day celebrated on November 14, 2025! We are proud that together we have created such a large and vibrant community of business analysts worldwide. Your passion, dedication, and knowledge make this network a place where we learn, grow, and support each other from day to day. 🫂

In honor of this special occasion, I’m excited to share a presentation of Key Takeaways from our recent joint Meetup with IREB, where we explored "Why Model Requirements?" and shared practical techniques on how modeling simplifies the analyst’s work. This session sparked great discussions and provided valuable insights for all participants (check below)!

Moreover, if you still have any questions for our speakers Emil Abazov and Radosław Grębski - feel free to ask them here or in our LinkedIn channel!

Thank you for being part of this incredible community. Let’s continue growing and elevating the profession together! 💛

🤖 AI as a New Stakeholder?

AI is no longer just a tool inside a system - have you ever thought of it to play a role as an active participant? Recommender systems shape what customers see. Chatbots decide how requests are handled. Generative write text, code, even business documents now.

If stakeholders once meant business, users, and developers - today there’s a new player. AI itself is the stakeholder.

What this means for business and system analysts:

✔️ Defining the role of AI clearly.
Document AI decision making points and make sure that humans stay in control.
✔️ Capture AI-specific requirements.
Accuracy, explainability, bias, and data quality now matter as much as functionality.
✔️ Allow for unpredictability.
There could be fallbacks or exceptions to models, and thus a need for manual overrides.
✔️ Think complex and lifecycle-wise, not just a feature.
AI models degrade (“model drift” - unfortunately, the real world data can become too much different from the training data). Plan re-check, re-training periods, and steps for feedback and monitoring.

💼 Case in point: chatbots that do not escalate complex cases fast enough often bring more harm than they help. Anticipating these bottlenecks in process design will help to minimise costly redesign or rework.

💡 One insight to take away: treating AI as a “black box” is risky. Analysts who take the time to clearly define AI’s role and its lifecycle upfront are paving the way to build reliable and responsive systems.

❓Question for you: Do you already consider AI a stakeholder in your work?

#SystemAnalysis #BusinessAnalysis #StakeholderManagement #ResponsibleAI #AnalystMindset

Measurement System Analysis (MSA): Why It Matters and How It Works

Measurement System Analysis (MSA) is a methodology used to evaluate and improve the accuracy and reliability of measurement systems.
In simple terms: MSA checks if you can trust your numbers.

💡 Why MSA Is Important:
Even the best business decisions fail if they’re based on bad data.
MSA helps prevent that by:
✅ Identifying errors – showing if variation comes from people, tools, or the environment.
✅ Improving reliability – ensuring that repeated measurements give consistent results.
✅ Building confidence – in data used for reporting, quality control, and analysis.

Components of a Measurement System:
An MSA typically evaluates several components:
🎯 Accuracy – Closeness of measurements to the true value.
Example: A thermometer should read 100°C when measuring boiling water. If it shows 98°C, it’s not accurate — even if it gives the same result every time.
📏 Precision – Consistency of measurements.
Example: A bathroom scale always shows 70.5 kg for a person who actually weighs 71 kg. It’s not accurate (off by 0.5 kg), but it’s precise because results are consistent.
⚖️ Bias – Systematic error or deviation from the true value.
Example: A blood pressure monitor that always shows 5 units higher than the real pressure has a bias. The error is predictable and repeatable.
⏳ Stability – Consistency of measurements over time.
Example: A digital weighing scale that shows correct readings today but drifts by 1–2 grams after a month lacks stability. Calibration may be needed regularly.
📉 Linearity – Accuracy across the entire measurement range.
Example: A speed sensor might be accurate at 30 km/h and 60 km/h but show larger errors at 120 km/h. It means the system’s accuracy changes depending on the range.
🔁 Repeatability – Variation when the same operator measures the same item multiple times using the same equipment.
Example: A QA inspector measures a metal rod’s length three times using the same caliper and gets 100.1 mm, 100.0 mm, and 100.1 mm — good repeatability.
👥 Reproducibility – Variation when different operators measure the same item using the same equipment.
Example: Two analysts measure the same product sample. One records 100.1 mm, the other 99.8 mm. The difference shows an issue with reproducibility (possibly due to technique or interpretation).

By performing MSA, you:
*Detect and correct measurement errors early.
*Make decisions based on facts, not assumptions.
*Improve trust in reports and analysis.

👉 Takeaway: Before optimizing your process, make sure your measurements are trustworthy. Because if your data lies, your decisions will too.

#BusinessAnalysis #SystemAnalysis