📊 Survey: What Topics Interest You the Most?

Hi, analysts! 👋

As we continue to grow and learn together in our BA community, it's crucial to ensure that the content we share is valuable and interesting to you. Please take a moment to participate in this quick survey and let us know which topics you would like to see more of in our channel.
Your input is invaluable! If you have any other topics you'd like to learn about, please comment below.💛

Thank you for your participation! Your feedback helps us make this channel a better resource for everyone.

Hi, analysts!👋

🚀 Mitigating Risk with Gap Analysis, Risk Assessment, and Feasibility Analysis 🚀

When creating a change strategy, it's crucial to ensure your solution fits the organization’s culture, works with existing technology, and has future support. Here’s how to mitigate risks effectively:

🔍 Perform a Gap Analysis
Identify the current and future states of the organization.
Document and compare these states to find gaps.
Prioritize gaps to tackle the most critical issues first.

⚠️ Identify and Assess Risks
Identify internal and external risks during the transition.
Assess risks by probability, impact, and mitigation measures.
Prioritize risks based on their criticality and the organization’s risk tolerance.

🛠 Conduct a Feasibility Analysis
Check project constraints, assumptions, product risks, dependencies, culture fit, technology availability, support capability, schedule, and organizational readiness.
Ensure the proposed solution is realistic and implementable within constraints.
Conducting these analyses helps mitigate risks and ensures successful project implementation.

Happy learning! 📚

Hi, dear analysts! 🖖

Today I propose to review 📈The Evolution of Data Architectures: From Data Warehouse to Data Mesh 📈

Over the past few decades, data architectures have undergone a remarkable transformation to keep pace with the ever-increasing volume, variety, and velocity of data.

Let's take a closer look at this journey:

1️⃣ Data Warehouse (1980s):
- Designed for structured data storage with ETL processes.
- Data Mart: A subset of a Data Warehouse for specific business needs.
- Schema-on-Write: Data is transformed before storage.

2️⃣ Data Lake (2010s):
- Stores structured, semi-structured, and unstructured data.
- Schema-on-Read: Schema is identified when data is read.
- Popular for Data Science and Machine Learning.

3️⃣ Data Lake House (Late 2010s):
- Combines the structure of Data Warehouses with the flexibility of Data Lakes.
- Retains Schema-on-Read while ensuring structured querying.
- Metadata and Governance Layer ensures data integrity and accessibility.

4️⃣ Data Mesh (2020s):
- Treats Data as a Product with a product-centric lifecycle.
- Addresses scalability and complexity challenges in monolithic data architectures.
- Marks a shift towards decentralizing data infrastructure.
#cheatsheet

Hi, analysts!👋

It is extremely important to share your professional experience with colleagues in order to build a strong community of analysts! 🤝
Therefore, today I am sharing with you the thoughts of our colleague Emil Abazov, Senior IT Business Analyst at Andersen Lab 🔆

Hi, analysts!👋

Let's start this week by looking at a topic like Data Mapping.

📊 What is Data Mapping?

As a business analyst, mastering data mapping is essential to ensure smooth data flow between systems, avoiding inconsistencies and mapping issues.

🔍 Why Data Mapping?

Seamless Operations: Ensure data flows smoothly between systems.
Project Control: Start projects on the right foot for successful implementation.
No Coding Needed: Create data maps without coding or SQL knowledge.

📺 Watch the Video

In this video, Laura guides you through creating a data map step-by-step. Understand data mapping to confidently handle data migration and system integration projects.

📌 Key Components of a Data Map

- Source Attributes: List of original data attributes.
- Target Attributes: Corresponding attributes in the target system.
- Translation Rules: Define any data manipulations needed.

🛠 Resolving Issues with Data Mapping

Data mapping helps discover and resolve potential issues before implementation, preventing data loss or misrepresentation.
For example:
HTML in Titles: Ensure HTML in source data is handled properly.
Multiple Categories: Map multiple categories to a single text field.
Character Limits: Handle field length differences between systems.

Happy learning! 📚

Hi, analysts! 👋

Let's not forget the importance of such a document as Definition of Done (DoD)!

🔍 Why Is the Definition of Done So Important?

The Definition of Done (DoD) is crucial in guiding teams towards their goals. It ensures that each product backlog item delivers value while maintaining quality standards.

✅ Key Benefits of the Definition of Done:

- Ensuring Alignment:
Aligns every item, feature, and increment with broader business objectives.
Ensures team efforts contribute to long-term goals like market expansion and customer satisfaction.

- Stakeholder Satisfaction:
Manages expectations with a transparent agreement.
Builds trust and fosters positive relationships with stakeholders.

- Shared Understanding:
Clearly defines what it means to complete a work item.
Prevents misunderstandings and misalignments, ensuring unified team objectives.

- Quality Assurance:
Sets a quality benchmark for deliverables.
Ensures both functional and non-functional requirements, like performance and security, are met.

- Reputation and Trust:
Enhances the organization's market reputation.
Builds a loyal customer base and attracts valuable partnerships.

The DoD is the backbone of successful product development, weaving together stakeholder aspirations and execution realities. By embracing this definition, teams can confidently navigate challenges and deliver impactful products.🚀

Hi, dear analysts!👋

🌱 Junior Analysts: How Not to Burn Out After the First Project 🌱

Today I want to share experience and tips for Junior systems\business analysts.💛
The role of a systems analyst requires not only technical knowledge, but also the ability to communicate effectively with the team and clients.
Here's what you should keep in mind to avoid "burnout" after the first project.

Challenges of starting a career 🎈

The IT industry often requires professionals with 3-6 years of experience, which can create a barrier for beginners. Courses and self-study are important, but without real-world experience on projects, it's difficult to progress further.

What to do if you are a junior? 🧑‍🎓

1️⃣ Immerse yourself in the subject area: Getting up to speed quickly on the project topic and adjusting to the team will help you get up to speed faster. Don't hesitate to ask questions and seek support from your colleagues.

2️⃣ Stress management: The first project can be a daunting experience. Accept the fact that the initial stages are a time of active learning and adapting to new tasks.

3️⃣ Continuous learning: Even after starting a job, it's important to keep learning. Learn new technologies and techniques to stay on top of trends and complete tasks efficiently.

4️⃣Communication and collaboration: A systems\business analyst often works as an intermediary between the business and developers. Being able to clearly and concisely explain requirements and interact with different interests is important for project success.

*️⃣ Conclusion
The initial period of working as a systems\business analyst can be a challenge, but it also opens doors to new opportunities and growth. It is important to remember that every project is a chance to learn and gain experience that will help you become a true professional in your field in the future.

Don't be afraid of challenges and always strive for self-development - this is the key to success in IT🚀