As AI companies like ChatGPT and Anthropic release new, increasingly intelligent AI models that could potentially replace numerous human roles, what is the anticipated impact on analytics jobs? Given the imminent prospect of AGI-level AI within the next few years, it raises concerns about widespread job displacement.

Do you:

• Pick a domain you’re genuinely interested in and build projects around it?
• Choose a domain that matches your educational background?
• Or simply take the first Data Analyst opportunity you get and specialize later?

Would love to hear from experienced Data Analysts: does domain specialization actually matter when applying for your first DA job, or is getting that first job more important?

Ok so, our outbound is kind of on fire in the worst way. SDRs are grinding out touches and it feels like half the accounts we hit have zero real buyer intent, then leadership wonders why reply rates are trash.

We have a crm score plus some intent feeds plugged in, but in practice it just bumps random accounts because someone clicked a webinar page once. Meanwhile the ones that actually end up closing look nothing like what the score said was hot. Trying to figure out how people are combining buyer data, product usage, and all the random gtm signals into something that does not waste 30 touches on dead logos.

If you have a setup where account scoring is not a total guessing game and reps trust it enough to live in it, would love any tips or examples from your stack, even just rough ideas on what signals you prioritized. thanks in advance

So I've been working with a startup on a web app. It was developed using spec-driven development, and my role was mostly focused on project management (planning meetings, generating reports, coordination...) and helping with other things such as testing.

Soon, I'll start applying for roles in data engineering and data analytics, but I'm not sure which parts of this work are related to data and could help me later on when putting together my resume.

I appreciate your help and advice in advance!

Sat down last week to redo a client update deck that had gotten messy over four quarters of copy paste. Every slide had a vague label like "Q3 Results" or "Next Steps" sitting on top of a chart nobody could read without me talking over it.

Tried something different this time. Before opening the deck I wrote out what each slide needed to say as an actual sentence. Not a topic. A claim. "Churn dropped because we fixed onboarding" instead of "Retention Update." Once the sentence existed the layout basically built itself. Fewer bullets survived. Charts got simpler because they only had to support one claim instead of hosting a data dump.

Took about half the review cycles this time. Client caught fewer things because there was less to misread.

Do people write the sentence first or build the slide and caption it after the fact?

This is a data reliability story rather than a finance one, and I doubt the shape of it is unique to this source.

The SEC's submissions JSON at data.sec.gov gives every filing an acceptanceDateTime ending in "Z". For a large minority of companies that Z is wrong: the clock in the string is already New York time, not UTC. Read as UTC it puts the filing 4 or 5 hours later than it happened, which in my case was enough to move a row across the 16:00 market close and flip it from intraday to after hours.

So I stopped trusting the JSON and re-read the raw SGML header of each submission instead, where the field ACCEPTANCE-DATETIME is always Eastern, and compared the two record by record: 64,827 filings, every 8-K item 2.02 I could pull for 808 companies, 2003 to 2026. The gap between the two stamps is always exactly 0, 4 or 5 hours. No partial offsets, no noise, nothing in between.

What surprised me was the grain. It is not per record:

• 624 companies converted in every single one of their filings
• 181 in none of them
• 3 mixed, and each of those three differs in exactly one filing, its most recent

The unit is the entity, not the row. That is the difference between re-ingesting everything and building a small lookup table, because a handful of records per entity classifies it.

I want to flag how I got that wrong first, because the mistake is the reusable part. I had already published the opposite conclusion, per record, off a sample of 120 entities. The sample was internally clean and the rule it produced was false, because the counterexamples were entities I had not drawn. The fix was not a better test on the sample, it was counting how many cases would have to exist to break the rule and then going looking for them on purpose.

Two caveats worth being precise about.

I cannot find this field documented anywhere on the SEC's API or developer pages, so this is undocumented behaviour that is inconsistent with itself, not a broken contract. "The API lies" is a stronger claim than the evidence carries.

And the source repairs entries after the fact. Entities that came back unconverted last week come back converted now. Any snapshot is dated, mine is 2026-09-06, and the three mixed entities differing only in their newest filing is that same lag showing up as a fingerprint rather than as a contradiction. It is also why I am not naming a company as an example: a named example expires the moment the source touches that record, and then whoever checks it concludes the rest is wrong too. The method is the part that reproduces.

To check it against whatever you are pulling: take three or four records for one entity, read ACCEPTANCE-DATETIME out of the SGML header, and compare the wall clock against the JSON. If they match, the Z is decoration.

One more, independent of timezones, in case anyone is doing an event study off this field. The acceptance stamp is a ceiling, not the event. Allowing 15 minutes between the press release and acceptance, 8,756 of the 27,227 rows that look like they landed after the close were most likely intraday, about a third of them. The error only runs one way, so a correct time based split survives it, but it is not zero.

I've been experimenting with a small JavaScript-compatible language called JojoScript, mainly around a problem I find interesting in data processing: what happens when an analysis pipeline takes hours to run?

The idea is to treat pipelines as something the runtime can understand, rather than just a chain of function calls. That makes it possible to inspect the execution plan, control concurrency, profile stages, and checkpoint long-running pipelines.

For example:

loadData()
  |> filter(...)
  |> map(...)
  |> parallel(8)
  |> aggregate(...)
  |> checkpoint()

The interesting part for me isn't the syntax itself, but whether this approach can make large-data analysis easier to debug, optimize, and recover when something fails halfway through.

I'm curious how people here handle long-running or large-data analysis pipelines today, especially when a job fails near the end.

[ Removed by Reddit on account of violating the content policy. ]

Almost every online tutorial or course I look at these days makes it seem like a data analyst needs to have a solid proficiency in Python and Pandas to survive in the current job market.

The thing is, I’m actually quite comfortable with Python, but in my day to day work, I barely use it. Whenever I’m preparing data for the dashboards I build, I usually just write a few complex CTEs in our database, clean the result, and connect that directly to PowerBI.

I really only use Python for hitting an external API or for advanced text manipulation. Otherwise, it’s just pure SQL to get the data ready for the stakeholders.

Does anyone else operate like this in the real world, or am I building a bad habit that’s going to hurt my career in the long run? Where exactly in this kind of workflow would you plug Python in? Would love to hear your thoughts on this!

Hi everyone,

I needed some advice on programs and schools. I recently finished my undergrad and have no jobs so I am planing to go back to school. My cum gpa is 3.5 but my major gpa is higher. I wanna go to a good name school because I believe that does matter when it comes to landing a job offer. Please advise what are some good schools preferably online but I am open to moving as well.

Hello, I am a Business Analytics student and is working on a capstone project and I'm currently working on what system shoud I do. To be able to innovate one must have some dissatisfaction and I've tried different softwares and I just can't find a gap or think of stuff that needs automating or innovate other software to improve the work of others 🥲 Maybe it's because I haven't had much experience to find things I wish I could improve. My professors told us that inventory, booking, and e-commerce was not allowed because it's common. So I am struggling with finding gaps, I tried searching online but everything I think of is either already made, or it's not on par with the current standards my college is upholding. So I am asking for help for some of the things you guys, who have more experience than me if you would like to share what process you think should be automated or you think should be improved. Anything is much appreciated 🥹

I’ve been thinking about the distinction between attribution accuracy and event accuracy.

If GA4 says 950 purchases, an ad platform claims 1,020 attributed purchases, and the transactional database contains 1,000 actual orders, the attribution disagreement makes sense.

But determining whether the underlying events themselves are complete and correct seems like a different problem.
For people responsible for analytics/measurement, what do you consider the ground truth?

And do you have automated reconciliation between analytics events and backend transactions, or is that usually something investigated manually when discrepancies appear?

Let's say I have a genetic algorithm with two different selection methods. I've gathered the data from testing these selection methods (gene values ranging 1-6 from every individual as well as fitness scores). What metric/test/method could I use to compare these two algorithms, especially in terms of exploitation vs exploration?

I'm currently evaluating ClickHouse vs Trino for a new analytical data platform and would like to get some opinions on how people would structure the technical evaluation.

There are several types of workloads involved:

\- Frequent ad-hoc analytical queries

\- Automated/AI-assisted analysis, where queries can be generated dynamically

\- Larger analytical/reporting queries involving aggregations and joins

\- Some interactive queries where predictable latency is important

The data is stored in object storage as Parquet, and the analytical system would be kept separate from production workloads.

I'm currently considering these evaluation criteria:

1. Scalability: handling large datasets and future growth

2. Fault tolerance: reliability, persistence, redundancy and failure handling

3. Query performance: latency for interactive workloads and execution time for heavier analytical queries

4. Concurrency: behavior when different workloads run at the same time

5. Operational complexity: deployment, upgrades, scaling, monitoring, troubleshooting, etc.

6. Cost: infrastructure and operational cost

For the PoC, I'd probably benchmark representative queries from each workload and look at things like p50/p95 latency, throughput, resource consumption, failure rate and performance degradation under concurrency.

My main question is: would you use these criteria, or would you evaluate ClickHouse vs Trino differently?

Are there important criteria I'm missing?

In particular, I'm wondering whether things like these should be first-class evaluation criteria:

\- Object storage / Parquet integration

\- SQL capabilities, especially complex joins

\- Workload isolation

\- Ingestion performance

\- Ease of scaling

\- Operational maturity

\- Behavior with dynamically generated SQL

\- Predictability under mixed workloads

And would you use one common scoring framework, or define different criteria/weights for each workload?

I'd be particularly interested in experiences from people who have actually run PoCs or production workloads with both systems.

I'm working in a company which primarily use tableau. Recently C-level suggest why don't we use AI to build dashboard ourselves and save thousands of dollars.

We've built few of them in html format via AI. However, I feel like it's not going on the roght track

1. I don't know anything about html, I can't debug if there is something wrong

2. The UI UX is kind of ugly

3. The way we build is having a pre-aggregated file which store all the possible combinations of the tables used in the dashboard, which kind of not scalable imo.

Want to know how you guys dealing with the dashboard in the AI era.

Looking for some genuinely useful AI tools that can actually help with landing a job, not just the usual ChatGPT/Gemini stuff 😭 Also open to any advice, certifications, or resources that you guys feel are actually worth doing.

1. "Can we make this more actionable" = Translation: I read the title
2. "Is this the most recent data" = I do not like the number
3. "Can you add a bit more context" = I do not know what any of these columns are
4. "Quick question on the dashboard" = I have not opened the dashboard
5. "Lets take this offline" = I want you to say the bad number in a smaller room
6. "Can we turn this into a one pager" = I want to forward it without reading it first
7. "I trust your judgment on the analysis" = I am not going to defend this if it is wrong
8. "Just want to make sure we are aligned on methodology" = The number is lower than I told my boss it would be)

What's missing?

There's a metric called Revenue_FINAL_v3_USE_THIS, two years old. Nobody remembers v1 or v2, too scared to delete it.

"Can we make this more actionable" means "I don't like what it says."

"Directionally correct" means I know it's wrong and I'm not fixing it today.

Every AI strategy convo is two people who watched the same YouTube video pretending they didn't.

"Let's align on definitions" means this meeting is dying, and it's taking next week's with it.

Any thoughts the week brought?

For those running Marketing Mix Models in companies doing $10M+ in annual revenue with reasonably complex media setups:

What evaluation metrics/benchmarks do you see, e.g. for 4-week or 8-week out-of-sample validation?

Specifically interested in acceptable ranges for things like MAPE, NMSE or other metrics you rely on before considering an MMM actionable-ready.

Would be great to hear actual benchmarks from models used in practice.

Shipped a feature in June. Built a dashboard to track adoption - DAU, activation rate, time-to-first-action, the works.

Week one: checked it daily. Week two: checked it twice. Week three: didn't open it.

The dashboard answered "how are the numbers." It didn't answer "what should I do next." Those are different questions and I only built for the first one.

Curious - how many of your dashboards actually changed a decision?

trying to move into game data analyst roles. I’ve had two interviews recently — one rejected at the case study, the other at the interview itself — and the common thread was that I clearly hadn’t spent enough time with real player data.

Problem is I keep running into the same wall: there’s nothing to practice on. The public datasets are either synthetic, tiny, or from 2016, and none of them look like what Firebase or GameAnalytics actually spits out.

Would appreciate any pointers, thanks

Some free advice to consider for college students considering a career in data. Don’t worry about learning data in college, instead study economics, finance, business, etc and THEN:

Economic consulting firms like NERA, Cornerstone Research, Analysis Group, Compass Lexacon, etc will all hire analysts straight from undergraduate, no prior coding skills needed. The bar to get in isn’t “can you code” like other industries but rather “can you think critically and logically. And then you learn good data practices on the job while working on high profile commercial litigation work. Basically calculating the numbers you see in the newspaper “X company agrees to pay Y dollars in damages” using big data

Spend 2-4 years sharpening your data chops and then these firms have outstanding brand reputation at places like FAANG etc.

There’s lots of ways to get into data but this is one I don’t see talked about all that much

Edit to add the why tech companies like to hire from these shops

- data skills: from day 1, daily use of SQL, Python, R, Stata, Excel, etc. after a couple years, full E2E ownership of analytics products from data import, cleaning, and analysis (including causal regression analysis, optimization, etc)
- the clients are attorneys and the audience for your work is a judge or jury: you need to package complex analysis in a way that’s digestible for non-technical people
- the quality bar is litigation stakes: your work has to be so good that it’s admissible as scientific evidence in court. It also has to be so good that an analytics team on the the other side of the case can’t poke holes in it. You get really good at poking holes in your own work and understanding what your analysis can actually say or not say
- agility: fast paced, deadlines, competing and changing priorities on multiple projects at once. Tech is fast paced but feels chill in comparison
- framework for approaching problems: by approaching questions as an economic problem, you get a deeper understanding of incentives and human behavior that are driving metrics and telling the storyi

Hi everyone I'm looking for a job in analytics, before applying for the jobs I want to build a strong resume. So I need to add some good projects which could make my resume shortlisted at least. Plz tell me what projects need to be added in the resume in today's AI world.

And also where can I find the dataset for the project you have recommended.

The tools i know are powerbi, Excel, SQL, python, r.

My qualifications are MBA in analytics.

Hey, my job has literally turned into prompting all day. Wanted to know what you all are doing (if anything) to ensure your analytics skills don't atrophy?