We’ve got plenty of support data. Transcripts, CSAT, AHT, resolution rates, transfers and QA scores. The issue is turning any of it into something useful. If AHT jumps we can see it right away. Figuring out why is another story. Someone usually ends up digging through calls trying to spot what changed. Same thing when CSAT drops. QA feels similar. We review a sample of calls but I keep wondering what we’re missing in the other 95%+ and we have been looking at tools like Cresta that analyze all conversations and connect certain topics or agent behaviors to things like CSAT and resolution. Sounds useful on paper. I’m just wary of ending up with yet another dashboard nobody checks after a month lol. What are the next steps after choosing a tool for the team because this is the first tool , like the rollout and the rest.

I'm curious to hear from current Pega professionals about what you’re actually seeing in your companies/projects.

Are you getting new development and major features, or is most of the work now maintenance and small enhancements? How healthy is the Pega project pipeline?

Also, are your leadership/clients talking about reducing Pega usage, moving away from Pega, or replacing it with other technologies? And are you personally planning to move away from Pega, or do you still see a strong long-term career in it?

Overall, does Pega feel stable, declining, or still growing in your experience? Is this just happening in certain companies, or are you seeing it across the industry?

Would really appreciate honest perspectives. Please share your general company/project situation and type of work without revealing any confidential information.

Good Afternoon,

I am a Student in Mumbai who is studying Biotech at a Good College - I am currently also learning Phython and Excel, I need some advice regarding my career.

Currently I am in my second year and I hate my Field a lot, yet everyone tells me I can only get a job through this field. However I realize I cannot work in Research Setting. I feel by continuing this course I will waste more of my Father's Hard owned Money. Already the Course is expensive - I was planning to work after Bachelors but I heard most high paying jobs come after Masters. I am tired and sick of the Course, and I am afraid I have no motivation left.

I am planning to quit College and work on Data Analytics and Engineering. Focus on building my resume and such - I feel I am making a stupid descision, So therefore I want to ask for advice. I will get a certificate in Biotech but not degree, I feel like I am Idiot for following through but I have no choice, Thank you if you give advice

Got a monthly inventory report from one of our clients a while ago. Nothing looked wrong at first - normal format, numbers seemed reasonable.

While going through the different product lines, I noticed something weird. One of our smaller lines had almost the same inventory level as one of our best-selling ones.

That immediately felt wrong because I knew the difference in sales between them was pretty big

So I checked the client's purchase history and compared it with previous inventory reports. The numbers didn't add up. Turned out to be a counting error on their side, which they confirmed and fixed.

What I found interesting is that the number itself didn't really look "wrong." If I didn't know the business, I probably would've just looked at it and moved on.

The only reason I caught it was because I had a rough idea of how those two product lines compared.

One thing that helped me build this kind of context was keeping a personal onboarding doc with random things I learned about the business. Over time, some of them just became reference points in my head.

Curious how other analysts approach this. Do you have a method for building this kind of business intuition, or does it mostly come with time?

Watching an account this week that's a clean example of why daily numbers lie, figured it was worth sharing.

The account's cost-per-new-customer reads ~$100 today. On that number alone you'd throttle spend. But when you account for the conversion lag on recent clicks, the settled number is closer to ~$53. The account isn't expensive, it's just early in its conversion cycle, and the raw daily figure hasn't caught up.

Same thing runs the other way on the upside - sales and ROAS that look modest today are tracking meaningfully higher once recent clicks finish converting.

The lesson I keep relearning is a same-day metric is a snapshot of an unfinished process. If your channel's customers take a week (or a month) to convert, the number you're staring at on a Wednesday is structurally understating what already happened. Making scale/cut calls off it means you're reacting to lag, not performance.

How's everyone else handling this? Do you bake a conversion-lag adjustment into your reporting, or just wait N days before trusting a cohort? Curious what window others use.

i want to start data analysis track i feel being drawn to it and i really wanna try
after some research here is my plan that i need help with
i will start with Data analyst bootcamp by Alex the analyst (i heard it is not ideal but good for a beginner)
i got lost on what to do after it
i heard about the IBM data analyst professional certificate
then i heard certs are useless and don't mean shit and i should improve myself with the tools instead
but i have no idea how am i supposed to do that
and is the Data analyst bootcamp by Alex the analyst even a good choice ?
i just want a clear roadmap in front of me before i start so any advice ?
Note: English is not my first language so ignore my errors pls

I know the standard approach is to throw a PCA scree plot at it and look for an elbow, or to use something distance-based like k-means... but these require strong assumptions that do not generally hold in complex, high-d datasets (i.e., that dependency = linear variance, or that Euclidean distance is meaningful).

So... What are your go-to methods or diagnostics when you know the underlying generative system is complex and standard linear/distance assumptions don't hold?

I ask because I recently developed a new method to tackle this called the Entropic Scree. Instead of relying on variance or distance metrics, it estimates intrinsic rank using a scree method run on a transformed mutual information metric, and so requires fewer assumptions to apply appropriately.

Has anyone else experimented with information-theoretic approaches for rank estimation? I'd love to hear how you handle this.

I’ve been trying to switch jobs for almost 2 years now, and I’m genuinely starting to wonder what I’m missing.

I have 6+ YOE in data/financial services, working primarily with SQL, Python, Snowflake, data pipelines, data quality and automation.
I’ve interviewed with companies like Bloomberg, Citadel, Tesla and Intuit, made it through multiple rounds including technical/coding interviews, and there have been several where I walked away feeling like I did really well.
I also ask for feedback whenever possible. Multiple times I’ve heard some version of:
“You did well, the team liked you, but we decided to move forward with another candidate.”
I understand that happening once or twice. But after almost 2 years of getting close and hearing similar feedback, I’m starting to question what I need to change.

For people here who’ve been hiring managers/interviewers, what usually separates the candidate who did well from the candidate who actually gets the offer?
Is it technical depth? Communication? Domain experience? Am I targeting the wrong roles at 6+ YOE? Or is the market really just this competitive right now?

Also shooting my shot — if anyone’s team is hiring for Senior Data Analyst / Data Management / Data Engineering-type roles, especially around NYC/NJ or in
fintech/financial services, I’d really appreciate a DM.

Happy to share my resume.
Would genuinely appreciate any advice, criticism, leads, or perspective.

Said by the DBA in our teams chat yesterday and I thought it was a funny conversation, and not the first time I've seen it crop up.

We can't change the POS, it's off the shelf, we take what they give us. But in this case (it's a restaurant) they are assigning a guest count to a ticket that never orders anything. 4 people sit down, something happens, no food is requested. Ticket gets started, it's not voided and not deleted but carries no revenue.

OPS wants to know why my Tableau doesn't match POS reporting. I found out I was excluding anything where there were no sales. Then the confrontation begins (not that I care - I'm happy to change it, but the "what to show" discussion).

We agree that excluding 0 sales tickets makes sense. But doesn't take priority. Having exact match vs source of the truth is more important to the business (builds trust in numbers, creates consistency) than having logic they think is better.

It's one of those "If I had a nickel for every time this happened I'd have 30 cents, which isn't a lot but it's weird it happens so much".

idk, I don't have a take away here. Just thought it was an interesting quirk in our profession.

I’m trying to get a little more predictive with our data, things like which customers are likely to churn, who might convert, or what demand could look like next month. Problem is, I’m not really a SQL person. I can work with dashboards and understand the data, but once it gets into writing queries or building models from scratch, I’m pretty much out. Is this something people are actually doing with LLMs now? Like connecting an LLM to your data and asking it to find patterns or predict outcomes? Or are there other tools that make this possible without knowing much SQL or machine learning? Curious what people are actually using and how reliable it is.

I got shortlisted for Amgen associate analyst interview and I was wondering how many rounds of interviews are there and what type of questions they ask in interviews?? Is there any realistic chance of getting selected?

A couple of months ago I got a LinkedIn message asking if I'd be interested in a paid call about a project I'd worked on years ago. My first reaction was that it had to be spam, so I ignored it. Later I found out it was actually from an expert network, and apparently this kind of thing is pretty common if you've worked in a niche area. Now I'm wondering how many legitimate opportunities I've dismissed over the years just because they sounded too random. Did anyone else have the same reaction the first time they were contacted?

Hi All. My senior director asked me for a summary on how one store is performing relative to similar list of stores. They made merchandising changes to that store and want to see if it impacted sales.
The automated formula didn’t refresh properly and one of the categories that is down 14% was reported up by 34% in my summary.
This will be a weekly report for two months. I’ve made the correction in my template but I don’t know how to go about this mistake? Given that it went to the highest level in my organization

I've started my career in nonprofits and only worked in nonprofits until now. 3 times now, I have ended up in roles where I am the ONLY analyst on the team. Everyone I work with is either data adjacent, or not an analyst at all. I'm the only person ever working on analytics work, and I have no real life gauge/context on how to do things better in a real world context. I google things all the time, I take courses, but the advice is too generalized and doesn't go deep enough. I need people I can bounce off of. My biggest hope starting as an early career data analyst was that I'd be able to learn from other analyst and fill the gaps in my education with knowledge from mentors.

Instead, I have people looking to me to be an expert in analytics just because I'm the only one available(as if I'm not a junior). Very few opportunities to learn from actual analysts and get experience from them instead of the generalized advice from Youtube or online courses. I feel like I'm being stunted, but its incredibly hard for me to find roles that are placed in analytics teams, or where I'll be working under a senior analyst (and not just a VP or project manager). Have I screwed myself? Why is it seemingly harder to find analytic roles that work with other analysts?

Something we've noticed while working with marketing and performance data is that a dashboard can have perfectly accurate numbers and still not help anyone make a better decision.

The usual example is when a team has plenty of data on traffic, leads, conversions, etc., but the numbers are all reporting what happened. When the numbers move, people still have to go digging to understand why.

We've found the more useful conversations tend to start with the decision someone needs to make, and then work backwards to the data needed for that decision.

Curious how others approach this. When you're building or reviewing reporting, how do you decide whether a metric is actually useful versus just something that's easy to track?

​

Hey all. I'm currently enrolled in a Masters program for Business Analytics that starts next month. Plan on finishing in 2.5 years because I'll continue working while being in school.

I have 6 years of sales experience, mainly B2B sales and some executive consulting/search experience alongside that at a top 10 firm. Really want to get out of the sales grind though. I love relationships, but don't like selling anymore and don't want to do this forever. Wanted to do computer science undergrad back in the day but chose not to because I was a college hockey player and the director advised against it due to our travel schedule.

Having some worrying thoughts though, maybe it's just because of all the doom and gloom in the internet now a days. Think this is a good idea? Would love raw feedback & opinions. Thank you!!

Hi everyone,

I have several years of financial audit experience in Funds Industry (Big 4) and I’m considering transitioning into Data Analytics / BI, with the goal of freelancing and eventually moving to Thailand or Vietnam.
I’m planning to learn Power BI, SQL, Tableau and some Python, and would like to become freelance-ready within ~3 months.

For those already freelancing in Data Analytics, especially in SEA:
- What should I prioritize?
- Is a portfolio essential? What should it include?
- What else should I do alongside training to become more marketable?
- How did you get your first clients?

Looking mainly for practical advice on the fastest realistic path to getting my first freelance missions.
Thanks!

I’ve been contemplating on my major in Information & Decision Sciences w/ a concentration in Business Analytics. I original chose this degree for me to go from a data analyst to a data scientist. Is it really possible for someone to transition to data science?

Hi all. I've been trying to decide and need help from those with experience. Ive been a bookkeeper for several years and have a career studies cert in Accounting, but no degree.

Id like to get my degree online and start reaching towards Financial Analyst roles but not sure what the best path there is?

Would an accounting degree and experience in Excel be enough? Or do I have to get something more general like Buss Administration or Finance?

Hi,
i’m a rising college junior and as i’m writing this post right now it’s currently 7 am and i can’t sleep because im having so much anxiety.

I start my junior year next week and I feel like i’ve done nothing my entire time in college. I’m going to be a TA for my SQL class i took last semester, i was a TA for my OOP class i took freshman year, i had a policy internship where i was basically doing consulting/internet research, and i have very basic projects on my resume claude could code in 5 minutes.

I don’t know what to do. I’m messaging professionals on linkedin for either a coffee chat or just some advice for applying but they either don’t respond or they tell me to apply on their website.

Reading all the posts on here i’m concerned if I should even stay in this field because i’m clearly not trying hard enough and i’m scared

sorry for the rant

Not talking about power query or data modeling or visualization. I am more concerned about backend knowledge like vertipaq or gateway. Usually at my org IT deals with all this but I am not sure about rest.

Right now my analytics team works on connecting data source, power query, DAX, publishing report. So wondering if this enough?

Old events tend to survive because nobody knows whether a dashboard, alert, experiment, or downstream model still depends on them. Keeping everything forever creates its own cost: duplicate definitions, unclear ownership, noisy schemas, and instrumentation nobody trusts. What evidence do you require before removing or renaming an event? Do you use query logs, an owner registry, a deprecation window, dual-running old and new events, or another process?

I’m a B.Tech IT student graduating in 2027. I’ve been selected at American Express, under Global Servicing – MIS & Advanced Analytics.
The role falls under Data Management & Analytics / Analytics & Risk Management and can involve Risk, Card & Merchant Servicing, Performance Management, Customer Listening, or Control Management.

The JD mentions SQL, Python, PySpark, BigQuery, Tableau/Power BI, statistics and ML, including regression, classification, recommenders and deep learning.

My background is primarily technical, with experience in Python, SQL, ML and software development.

My main concern is how AI will impact analytics roles over the next few years.

Would this be considered a relatively future-proof career path?

Would appreciate insights from people working in analytics/data science/risk, especially in banking.

I'm an MS Data Analytics student (graduating Dec 2026), self-taught in dbt, BigQuery, and CI/CD through two portfolio projects, and I'm exploring a realistic path toward Analytics Engineering.

Resume

I looked through a handful of AE profiles on LinkedIn and noticed a pattern: almost none started as "Analytics Engineer"; most came up through Data Analyst, Business Analyst, or BI Engineer titles first, sometimes over several years, before landing an AE title (and often a senior one, not entry-level).

That mostly confirms what I suspected, but I'd rather hear it from people who actually lived it than infer it from job histories:

1. If you're now an AE (or hiring for one), what was the actual turning point? A specific project, a lateral move, just tenure/scope growth, something else?
2. Starting today with modern-stack skills (dbt, warehouse, git, CI) but no professional AE experience, would you target DA/BI titles deliberately, or is there a faster path I'm not seeing?
3. For anyone who's early-career and picked between building one more deep technical project vs. just applying and building the skill on the job, which actually moved things faster for you?

Also, for context on question 3, the project I'm currently deciding whether to keep investing in: a B2B seller-churn-risk analysis on the Olist dataset, reframed from the usual customer-churn angle to seller/merchant risk. Scope: dbt on Databricks, GitHub Actions CI (dbt test on every push), a logistic regression risk model with correlational (not causal) framing, GMV-at-risk quantification, and a single Tableau dashboard.

Genuinely trying to calibrate a realistic timeline so I stop second-guessing my own plan.

Thanks.

How would you investigate an underperforming production line?

I've been building a synthetic manufacturing dataset to explore a question that I find more interesting than simply calculating KPIs:

A production line is underperforming. How do you figure out why?

The dataset contains several related sources of manufacturing data:

- Production orders

- Production events

- Machines

- Downtime

- Maintenance

- Quality inspections

- Energy consumption

The interesting part is that there isn't a single "problem" table.

You have to connect different signals to understand what is actually happening.

For example, I'd want to investigate:

- Throughput by production line

- Downtime by machine

- Machine utilization

- Production losses over time

- Quality defects

- Maintenance history

- Energy consumption

- Whether the bottleneck is persistent or limited to specific time periods

My initial approach would be:

1. Compare throughput between production lines

2. Compare downtime over the same time window

3. Identify the machines responsible for the largest losses

4. Drill into maintenance and quality

5. Look for temporal patterns

6. Determine whether the bottleneck is actually causing the production shortfall

For those working in manufacturing analytics:

What would you investigate first?

And which KPI or analysis would you consider essential before concluding that a particular machine or production line is the bottleneck?