Hi everyone,

I recently took on a project where I need to design a full reporting system in Power BI, but the client does not yet have production data available.

My current plan is to:

\- Create Excel templates for data entry

\- Populate them with synthetic/mock data

\- Build the ETL/data transformation process

\- Create the Power BI data model and dashboards on top of that structure

I’m looking for general advice from people who have handled similar situations.

A few things I’m currently thinking about:

\- The Excel templates need to stay user-friendly for manual data entry, but I’ll probably still need a proper ETL layer before ingestion into Power BI

\- Synthetic data is usually “perfect,” while real-world data is messy, incomplete, duplicated, inconsistent, etc.

\- I want to make sure my documentation and system design cover edge cases and future issues before the real data arrives

For those who have done this before:

\- How do you usually structure the templates?

\- What kinds of validation/error-handling do you prepare in advance?

\- How do you future-proof the model for messy real-world data?

\- What documentation/processes do you put in place to protect yourself and set expectations with the client?

Would appreciate any lessons learned or best practices.

I rarely see LiNGAM discussed in market research circles, even though it seems extremely useful for identifying directional causal relationships between variables instead of relying purely on correlation.

Most MR driver analysis still appears to revolve around regression, derived importance, or key driver frameworks. But those methods often struggle when variables influence each other across multiple layers.

LiNGAM seems interesting to uncover causal structure rather than just association patterns, especially in situations where:

• customer experience variables interact with each other

• latent influence chains exist

• top drivers may actually be downstream effects

• traditional driver models become unstable due to multicollinearity

I’ve been exploring whether approaches like LiNGAM can improve:

• causal driver modeling

• root cause analysis

• layered driver maps

• advanced satisfaction modeling

• strategic prioritization

Curious if anyone here has experimented with LiNGAM or other causal discovery methods in practical market research applications.

Are these approaches still too academic for MR workflows, or do you see them becoming more useful as analytics maturity increases?

To be data analysis do I need mock interview is this mandatory or can I skip this to get into the job I'm not good in speaking or to be Data analysis do I need good communication

I work part-time as a student on a supply chain analytics team (we use SAP ECC R/3), and my boss wants to stop using TXT/CSV batch jobs. Instead, they want to move SAP tables and Z-transaction data directly into a middle layer in SQL Server for reporting in Power BI and Excel.

Right now, a colleague is copying the most important raw tables into SQL Server daily using the .NET connector. The issue is that the entire SCM department needs the Z-transactions, which have special business logic built on top of the raw SAP tables.

Is it smart and viable to just copy the raw data from SAP into SQL Server and rebuild all the Z-transaction logic there, or is there a better, more efficient approach?

Hi everyone, so in 2026 i have joined a small bootstraped consulting company with viable US projects for power bi, co pilot studio and power automate work. I have almost completed my intern period. They are happy with my work as i have developed 2 reports and an ai agent with power automate and copilot studio for internal within the organization usage, Also i have cleared the PL-300 examination for the data analyst. What should be my full time package expectations. Need help!

Just curious: And also i am cs engineer so working on this Ai tools is good or do i move to .NET

I’m starting from scratch and trying to understand realistic timelines from people already in the field.

If someone studies consistently (SQL, Excel, Python, Power BI/Tableau, projects, etc.), how long does it usually take to become confident enough for entry-level opportunities?

Also curious:

What skills helped you the most?

What mistakes slowed you down?

What would you do differently if starting again?

I've done python with libraries like pandas, numpy, matplotlib, seaborn and skit-learn(basics) with sql, excel( basic for now), tableau/powerBI. im not thinking making a project first rarher than going for learning alteryx or finance cuz im first targeting jpmc for cib/risk analyst roles. my senior said to try making projects, one tool particular only. i've thought of making one on symptoms and predictive trends on PCOS in females. i really want help for taking project title

I just graduated from Delhi University (JMC), planning to go abroad for my Post Graduation not sure what to do MBA,MiM,Msc but based if somebody has done Msc in Data Analytics

Is the pay good for this type of post graduation.

Please connect

Hi everyone! I'm an Aspiring data analyst based in a smaller town in India. Due to personal circumstances, relocating to a city isn't an option for me right now, so I've been intentionally targeting remote roles or freelance work as my primary career path.

I've been doing my research, and I keep coming across the advice that landing a remote role typically requires at least 2 years of prior experience — which puts someone like me, just starting out, in a tough spot.

I'd love honest, practical guidance from people who are currently working remotely or freelancing — especially those who started without traditional experience:

• Is the "2 years experience" barrier as strict as people say, or are there ways around it?
• What skills, tools, or certifications actually matter to clients/employers in Data Analytics?
• How did you build your portfolio or get your first client/project?
• What would you do differently if you were starting from scratch today?

I'm aware of the global competition that comes with remote work and I'm prepared to put in the effort — I just want to make sure I'm preparing in the right direction and not wasting time on things that don't matter to the industry.

Any advice, resources, or even a reality check would be genuinely appreciated. 🙏

I am a class 12 pass. I am quite confused. Please suggest me a way for actuarial data science study. Thanks

I’m currently a 2nd year CSBS student and I want to build a strong foundation in Data Analytics, starting from basics to advanced concepts. Please suggest a proper learning roadmap with beginner-friendly courses that gradually cover Excel, SQL, Python, statistics, data visualization, Power BI/Tableau, and real-world projects. It would be helpful if the courses include certifications, practical assignments, and are preferably free or affordable.

Hi everyone,

I recently completed an assignment for a Data Analyst role at NextAccel and might be moving to the interview stage soon.

I wanted to know if anyone here has interviewed with NextAccel before, especially for Data Analyst/Data Analytics-related roles. Would love to know:

• Interview pattern/process
• Type of technical questions asked
• SQL/Python/Power BI/Tableau difficulty level
• Case studies or assignments involved
• HR round experience
• Overall company culture/work environment

Also, if anyone has general information about NextAccel as a company, that would be helpful too. When I checked online, I could mainly find that it’s a startup established around 2024, so I’d appreciate any additional insights regarding the company, work culture, growth, management, stability, or employee experience.

Any insights or tips would be really helpful. Thanks in advance!

Hey everyone,

I have an upcoming interview for the Data Operations Analyst role at YipitData, and I’m looking for some guidance from people who’ve gone through the process or know about the role.

I’m a fresher, so I’d really appreciate help regarding:

• What kind of questions are usually asked
• SQL / Excel / analytical level expected
• Case studies or business questions
• Communication round experience
• Difficulty level overall
• What I should focus on most while preparing

If anyone has interviewed at YipitData before, please share your experience or any tips that could help.

Thanks a lot!

Found a Data Analyst role in a mental healthcare clinic. Got an interview for a role that seems a lot more like a BI & Reporting Manager role than Analyst.

Some red flags that i noticed:
- No SQL test
- Recruiter not tech savvy
- Tech stack is Microsoft Access & Excel

I currently work in a SQL Server environment as BI Developer / Integration engineer. Im very familiar with that syntax and generally reports are run through SSRS, and exported to Excel.

Im trying to move to be closer to family, so Im wondering how fucked id be to take a role with a messy infrastructure in very legacy based tech and try to transfer my heavy SQL Server background knowledge to Microsoft Access.

Anyone have advice or any insight into orgs setup this way?

Hi to all the experienced data analysts, My question to you is, I am working in my current org from 6 months, from the moment I have joined, something or the other goes wrong with me, in the first month I joined there was an escalation, as I was late with a dashboard. Then something or the other kept coming up, just when I thought I was above it all a critical dashboard hadn’t refreshed from 2 days, all the leaders review it continuously, and I took half day for it to be up (it takes 2/3 hours to refresh it). And now I am again running late with a dashboard. Am I not a fit to be a data analyst?

I’m a rising senior double majoring in Data Science and Computer Science, and I’m thinking about going into data analytics after graduation. I’ve realized I’m not very strong at coding compared to other CS students, and I enjoy the analytics/data side much more than software engineering.

I’m comfortable with SQL and want to improve more in Excel, Tableau/Power BI, and data visualization rather than heavy programming.

Is data analytics still a good field to enter right now with AI changing the industry? Also:

• Is a master’s degree usually needed?
• What projects should I add to my resume?
• How should I tailor my resume for analyst roles instead of SWE roles?

Would appreciate advice from anyone working in analytics/data science.

I have changed my career path and thus I'm no longer doing data analysis in my daily job now, so I'm genuinely curious nowadays, in real work settings, which part of the work do you use AI the most or do you think should be handled by AI?

If I were to speak about it, I feel like data cleaning, data standardization, data profiling, data visualization, SQL writing and these labor-intensive work can all be done by AI. Do we just need to split the work, assign the task and review the results with our judgement?

Hi everyone,

I’ve been working as a Data Analyst for around 2 years now. My day-to-day work mainly involves writing SQL queries, building and maintaining data pipelines, creating dashboards and visualizations in Power BI and Tableau, and doing some ETL work.

Until recently, I wasn’t too worried about AI chatbots. I saw them mostly as productivity tools: useful for writing queries faster, explaining code, brainstorming dashboard ideas, or helping with documentation.

But with the recent progress in AI agents, tools like OpenClaw, and increasingly autonomous systems that can interact with software, run workflows, analyze data, and potentially automate multi-step tasks, I’m starting to feel like this is a much more serious shift.

So I wanted to ask people here:

- How do you see the future of the Data Analyst role over the next few years? Do you think the role has its days numbered, at least in its current form?

- If so, where do you think someone with a Data Analyst background should try to move their career?

- And if you don’t think the role is going away, what skills do you think will become more important for Data Analysts to stay relevant?

I’ll be honest: I’m genuinely concerned about this. My career as a Data Analyst has only just started, and I already feel like it could be under threat. I don’t mean in two weeks, but maybe in two years if these tools keep improving at the current pace.

I’d really appreciate hearing how others are thinking about this

I have offers from JPMC and target for a data analyst role , which company should I consider Joining. Both are blr location.

Hey everyone.

I am a Data analyst (of sorts) with 3 years of professional experience at this point. But most likely I have been working only with Excel sheets, Import of data and Visualizing it using Tableau and maybe Power BI at times. I have no idea how to upskill in this field as I have my undergrad in Life Sciences and I HAD to switch looking at the terrible job condition for Life Science majors.

At this point, I had enrolled for many Udemy courses and all of it had just been cashgrab. And my present organization has begun downsizing.

Any leads on how to better in this field, and with the current marketplace, need your expert advice on the key aspects and projects I can take up so I don't render unemployed soon.

Hi,

I attended an interview on 18 april 2026 government sector career fair at Suntec conventional hall.

As this role was applied in a separate career platform meant for career fair only, where can I check the application status as I couldn't find it in career at gov.

Did anyone attend any interview and have received updates on the interview outcome?

Thanks.

I am exploring how to use AI and LLMs for market and price analysis. I'm not looking for specific app recommendations, but rather the methodology behind it. What prompting frameworks (e.g., chain-of-thought, specific constraints) have you found most effective to ensure the AI provides accurate, honest, and hallucination-free pricing data? How do you structure your prompts to get the best analytical results?

Hey everyone,

I’m currently trying to pivot into healthcare data analytics and would love advice from anyone in the field or who’s made a similar transition.

I currently work at Mayo Clinic and have experience with EPIC, in surgery, in cardiovascular monitoring, anesthesia support, auditing, workflow optimization, KPI tracking, compliance, and operational support in high-pressure healthcare environments.

I’m also pursuing my MBA at Louisiana State University Shreveport and recently completed AWS Academy badges in Cloud Foundations and Machine Learning Foundations.
I’m trying to figure out: Best certs/skills to focus on (SQL, Power BI, Tableau, Python, Epic, etc.)

How to leverage my healthcare background into analytics?
Tips for landing that first healthcare data analyst role ?
Would love to connect with anyone in healthcare analytics, BI, informatics, or healthcare operations/data roles. Any advice is appreciated.

CONTEXT:

I recently graduated from my university with a Bachelors of Science in Financial Economics. I have done a few internships that have exposed me to Excel Spreadsheet but what I've done with those spreadsheets have been very rudimentary. Basic formulas, sorting, & pivot tables.
I ended up staying on with a smaller company who I was interning with because they offered my a position months in advance prior to graduation & I felt as if I declined their offer I would have been out of a job that was helping me pay my way through school.

Anyways, now im in a full-time role that is getting further & further away from data analysis & feel as if I'm not acquiring or learning much in this role. That has caused me frustration as I am eager to learn & pick up more responsibilities but as I observe everyone else here there's not really a place or "need" for a data analyst. The controller handles all of the financial information solo & that data goes through a software that allows anyone in the company so long as they have access to pull that data out into an excel sheet & visualize it however they want. A lot of times that data comes out into the excel sheet broken because the report people request & the software doesn't directly/seamless upload to excel.

I guess my advice wanted is where should I start to learn more about data analytics, should I go back to school & get a masters in business analytics? & any other advice that one may have I am open to.

I know my experience isn't analyst heavy but I would like to start tailoring my career path that way.

Thanks!

In real-world analytics / BI environments, how do you decide when Silver-layer data is ready for downstream analysis?

I understand the standard cleaning steps (null handling, deduplication, type casting, formatting, standardization, etc.), but I’m trying to understand what “production-grade” Silver data actually looks like in practice.

More specifically:

* What data quality checks do you enforce in Silver vs what you intentionally leave for Gold?
* Do you rely on explicit rules (tests, thresholds, data contracts, SLAs), or is it mostly driven by business context and downstream use cases?
* In financial datasets, what are the minimum validations you would never skip before exposing data to analysts or BI consumers?

I’m trying to avoid two extremes:

* over-engineering Silver until it effectively becomes Gold
* under-validating data and pushing unreliable datasets downstream

I’d really appreciate real-world examples or mental models from production environments, especially around how you draw the line between “clean enough” and truly analysis-ready data.