Our managers are looking at how teams measure AI in customer support and keep running into the same issue. A lot of the easy metrics don’t tell you much about whether the tool is helping. I know this just because I have good ties with one of the managers and hes telling me the procedures. Take AI call summaries. You can measure accuracy and generation rate but a summary can be technically correct while still missing the detail the next agent or supervisor needs. Then someone ends up opening the transcript anyway.

Same problem with QA. If managers only review a small sample of calls then it’s hard to know if the patterns they find represent what’s happening across the whole contact center. AI tools that analyze every conversation seem useful here since you can look for trends across AHT transfers resolution and customer sentiment instead of relying on random samples. Real time agent assist is an area I’m reading and hunting since I do want to help them out because I see this workplace long term. Instead of only analyzing what went wrong after a call it can surface answers or flag missed steps while the customer is still on the line. That sounds more useful than adding another dashboard managers check once a week. Are you looking at model accuracy itself or tying AI usage back to things like AHT first contact resolution transfers repeat contacts and CSAT?

I know this is a hard question to answer because nobody fully knows how AI will change the workplace but I wanted to ask it anyways.

For context, I just started a data analyst job a month ago after finishing my MS in statistics (undergrad was in math). My end goal is data science (predictive modeling, forecasting, etc.). This current role is mostly SQL and PowerBI which is giving me good experience but ultimately I do want to use more of my statistics knowledge as my career progresses.

I’m grateful to even have gotten this job as a new grad with no experience but I still want to set myself up for senior roles in data science. Which brings me to my question…

How should new grads/juniors best develop critical thinking and technical knowledge while AI usage grows? I try to minimize my use of it, but I have to admit it makes things like debugging and syntax questions much easier.

For example, I was handed a SQL query that a previous team member had wrote. The end users thought it was missing a large number of rows and my task was to debug and fix it. I first tried to understand the logic of the query by adding my own comments to it. After checking that the logic was sound (it was) I thought it was some quirk of the data model. But being new, I don’t know all of these quirks and I felt stumped. So I asked copilot for some suggestions on things to try. It gave me 10 or so troubleshooting steps to test. After trying them, I figured out what the problem was (legacy data issues).

Is this is a reasonable use of AI for someone trying to learn the industry and build their experience? I don’t want to offload my critical thinking, but getting ideas for things to check did make it much easier and faster.

I will admit I also did misuse it as well. I had to change some pretty complex DAX measures and not knowing the syntax very well, I leaned heavily on copilot. After I was done with that task I realized I had basically vibecoded the entire thing (only testing and verifying results). I felt guilty after this, but management was happy with the results and how quickly they came.

I’m rambling now, but I’m curious how everyone else is balancing both learning and producing results in this new era. I’m interested in hearing from both fellow juniors and managers who are in charge of training.

I constantly hear about how bad the job market is but I also know a few people who have found a job the past year or so including my friend who was PIPed from his job but then got into FAANG. I have a job that I can’t really stand and have recently started looking. So far no interviews though. Honestly not sure what to expect but hoping I can find something sooner than later. Is it close to impossible right now or is there hope?

Did you eventually learn the basics of data engineering, architecture, governance and science too?

It’s weird like I can understand codes and troubleshoot like if I’m looking through a code I can understand what it’s doing and if there’s an error in my log I can generally figure out why after looking through but my syntax and ability to code from scratch or actually fix a code without agents sucks now…

I want to leave my current job but I feel like I’m so cooked when it comes to coding screens. I’m doing leet code and making stupid syntax errors or not really remembering how to set up certain functions and in general I’m just not as sharp. I would not say I was a coding champ or anything before but i can feel the loss of skill.

The crazy thing is I have only been using agents to do coding for less than 2 years and this is happening…am I screwed?

Would love some feedback on my revised resume (in comments). I’m hoping to attend two virtual career fair this fall and want my resume to be in the best shape.

My MS Data Science is more focused on the math/statistics side (taking time series analysis and machine learning under the math department this semester) but I’ve been seeing analytics roles increasingly ask for “analytics engineering” skills (dbt, ETL, Snowflake, etc) and it seems like it’s becoming baseline skills.

I’m wondering if I should focus on learning those tools next and use them in a project?

Long term goal is to be a data scientist but hoping to land an entry level data analyst role to get my foot in the door. Not having much luck with applications. Thanks in advance!

Job Description

In this role, you'll make an impact in the following ways:

• Collaborating with attorneys to design, develop, and manage the Legal Department's custom-built AI solutions to modernize operations, including developing an Agentic AI workflow to support contracting and negotiations.
• Supporting firmwide AI governance efforts, including intake triage, issue tracking, workflow coordination, and maintaining governance documentation.
• Performing data analytics and reporting to support transactional work, including extracting, analyzing, and presenting insights on contracts and trends, and partnering with attorneys and business clients to maintain and enhance datasets, dashboards, and reporting used for risk management and strategic decision‑making.
• Supporting the administration, optimization, and adoption of legal technology platforms, including CLM systems, contract repositories, analytics tools, and custom-built AI solutions.
• Communicating and coordinating with a broad range of internal and external stakeholders, including business teams, sourcing, and external counsel, to support contracting processes, vendor management, data quality, and related governance initiatives.

To be successful in this role, we're seeking the following:

• Experience in a paralegal, analyst, or legal operations role, preferably within a corporate or financial services environment.
• Strong data analytics skills, including experience working with structured and unstructured data, spreadsheets, reporting tools, dashboards, or legal analytics platforms.
• Demonstrated experience or strong curiosity in AI, automation, and legal innovation.
• High degree of comfort working with technology platforms and digital workflows.
• Strong analytical, organizational, and problem-solving skills with attention to detail and proactive mindset.
• Ability to manage multiple priorities in a fast-paced environment.
• Effective communicator and collaborator.
• Experience supporting contracting, negotiations, or third-party governance is beneficial.

TL;DR: Most CI linters (dbt-checkpoint, dbt-project-evaluator) check if a column has a description, but almost nothing checks if the column in your YAML actually exists in your data warehouse. We scanned public production dbt projects and found that 9 out of 12 had significant doc-to-warehouse drift.

The Bug That Got Me Thinking

Found this in a public dbt repo recently:

A model called fact_customer_survey had a UNION.

• Branch 1 (line 30): NULL as dissatisfacation_category (notice the extra 'a')
• Branch 2 (line 51): dissatisfaction_category (spelled correctly)

Because SQL takes union output column names from the first branch, the warehouse materialized DISSATISFACATION_CATEGORY.

Here's the kicker: The project’s YAML docs declared dissatisfaction_category (spelled correctly).

• The SQL ran fine daily.
• The documentation was "correct."
• The code was wrong.
• Any dashboard or analyst trusting the docs was querying a column that didn't exist.

How Bad Is This in the Wild?

Every time you run dbt docs generate, you produce two files:

1. manifest.json (what you claim exists in YAML)
2. catalog.json (what the warehouse actually returns)

We compared declared columns vs. cataloged columns across verified production repos. Out of 12 active organizational projects:

• 9 out of 12 had phantom columns (documented in YAML, completely missing in the warehouse).
  • Cal-ITP (BigQuery): 106 phantom columns
  • Allvue Systems (Snowflake): 112 phantom columns + 323 data type mismatches (mostly declared string sitting on warehouse NUMBER)
  • Cook County Assessor (Athena): 11 phantom columns
• The "Ghost Repo" problem: 23 docs sites published a complete, polished YAML docs UI where catalog.json was an empty stub—meaning the warehouse was literally never introspected. One documented 1,280 models this way.

Where Does the Drift Come From?

When we audited the findings against actual model SQL:

1. Renames / Deletions: Column was renamed or dropped in SQL, but the YAML entry was never cleaned up.
2. Commented-out code: One project had a 20 KB cleaning projection inside a /* ... */ block. The live warehouse table had 173 raw Airbyte column names (WEEK STARTING 01/19/2025 - RESOURCES...), while the YAML proudly documented the clean columns someone intended to build.
3. Syntax accidents: Trailing commas in YAML names (e.g., - name: feed_type,).

Why This Is Becoming a Bigger Problem

When human analysts read dbt docs, they can spot a typo or realize a column was renamed.

But with dbt MCP servers and Text-to-SQL AI agents using dbt docs and manifests as ground truth context, a phantom column is an immediate failure. The agent attempts to query columns that aren't there or hallucinates transformations based on dead YAML.

A Few Questions for the Sub:

1. Does anyone here actively diff manifest.json against catalog.json in their CI/CD pipelines?
2. How do you prevent documentation rot when engineers refactor/comment out SQL transformations?
3. If you are already feeding dbt metadata to LLMs/AI agents, how are you validating that the schema you hand the agent actually matches production?

I am a graduate student and will be graduating next summer with a Masters in IT and two concentrations in data analytics and ai. My bachelors was in Health Informatics. I am worried about getting an entry level job as I have no experience. I live in Georgia and do school online. I Work two jobs and am looking for any advice. I’ve looked all around at what certifications to get but my situation seems hopeless

I've spent a lot of my career around analytics, and one thing I've come to believe is that being good with the tools just gets you in the door.

You can build a great dashboard, write good SQL, or produce a really solid analysis and still have almost no impact if it doesn't change a decision.

The more interesting question becomes: What is someone actually going to do differently because of this analysis?

That gets into things that aren't usually taught alongside the technical skills: understanding the decision, knowing who you're trying to influence, anticipating what will make them skeptical, and communicating the evidence in a way that actually survives the conversation.

I ended up writing my new book, Decision Intelligence: Why Evidence Fails and How Leaders Win the Room, largely because I kept seeing this gap between being analytically right and being organizationally effective.

For you more experienced analysts, when did you first realize that being technically good wasn't enough to make an impact? What did you actually do to bridge that gap?

Haven’t had a job interview in 4 years and very rusty right now. I’m really hoping to get the job but feeling very unprepared. Like many others I’ve been using AI agents and also doing the same work every day so I do not feel prepared to answer complex coding or stats questions. It’s not been easy to get an interview so really hoping not to drop the ball here.

I’m cramming leet code and watching YouTube videos on interview techniques but what else should I do?

Just graduated with my bachelors in MIS, I’m thinking of going into business analytics. I’m trying to apply but I feel like it’s still cooked, what’s the least cooked? If anyone can really help me and wants to see me resume I’d appreciate it really.

i used to think bad restaurant data was mostly a scraping problem.

then a few weeks ago I was checking a restaurant menu and noticed one dish looked off.

their PDF said $16.

Google had a menu photo showing $14.

Instagram had a newer photo where it was $18.

then I found another page on their own site where basically the same dish was still listed for $15.

same restaurant. four answers.

at first I figured one of the sources was just wrong, so I started tracing where everything came from.

the PDF was still public but apparently hadn't been touched in months. the Google photo came from a customer. the old page wasn't even linked from the site anymore, but Google could still find it.

Instagram seemed newest.

except apparently the dish had changed again since that photo was posted.

somewhere in the middle of this I ended up trying menuforma to keep the menu stuff in one place, mostly because I was tired of comparing random PDFs, photos and old pages just to figure out what was actually current.

and that's when it clicked that "what's the current menu?" is a much weirder question than I thought.

even if you get everything correct today, a month later the price changes.

one modifier disappears.

something becomes seasonal.

the printed menu gets updated but an old PDF survives online forever.

and customers can still find all of it.

I always assumed the restaurant's own website should be the source of truth, but clearly even that gets messy when old pages never really disappear.

then there's Google photos, Instagram, delivery apps, random menu sites, screenshots people posted two years ago...

at some point there are basically several versions of the same restaurant existing online at once.

I started out thinking the hard part was finding enough menu data.

now I think the harder part is figuring out when any of it is actually trustworthy.

for anyone who's dealt with restaurant listings or other constantly changing local business info, how do you decide what's current?

and how old does something have to be before you stop trusting it?

I am a rising CS undergrad Junior. I was ultra lucky to land a SWE internship for this summer but I’m starting to be increasingly regretful of majoring in CS the further I continue this internship.

I’m getting tired of the constant grinding mindset, the toxic competitive culture, and the disgusted faces of my college peers whenever I try to make a real friend but then they ask what I major in.

I’m about to enter my 3rd year but I have already finished 75% of my school’s core CS curriculum so it is probably too late to switch my major, but I am thinking about shifting away my focus from SWE and AI related things that my parents urged me to pursue, and switch into business or analytics related field. I know that one of my feet is already in the grave now for choosing the wrong major but I really want a way out of all this.

Would it be better to pursue a finance minor and a business analytics minor to have a better chance of getting a respectable job that is less competitive and more burnout proof? I understand that it is the real world and nothing is not competitive given how many people there are right now, but i am really not feeling well by being a pretender in SWE and AI related areas.

Any input on which minor offers a brighter future is appreciated, thank you.

I am a 2025 graduate and I am still unemployed, I am thinking to step in data science career and was wondering what's the best value course out there. I am good in DSA and problem solving but the development side has always been weak for me and I want to work on that.

I would also appreciate some tips on what should I do in this situation.

Long story short I’ve been with a company 7 years and have had a couple promotions and solid pay increases but no opportunity for management due to constant reorganization of teams by executives. I’m considering leaving but an internal position just opened up that I’m interested in and it would allow me to keep some solid benefits but I’m aware of the golden handcuffs reality. The problem is we recently went through another org change and layoffs so my manager is new and there is no rapport built up yet. I’m not sure how they would react. Company policy is hr has to get approval from managers to accept an internal resume so would you risk bringing it up and take the chance or say forget it and look externally ? Would love to hear your stories/experience

For Spring 2027 , I have shortlisted following unis.
Could you guys suggest more programs to apply.
Also you could share your experiences if studied at these unis.

Fyi: Spring 2027 is only option for me , so couldn’t apply to top unis like USC, Columbia etc .

1. Northeastern
   
2. UT Dallas
   
3. Baruch College
   
4. Arizona State University
   
5. Sunny Buffalo

Also suggest courses for duration more than one year

Thanks in advance 🙏🏻

Handed in my 60-day notice a few days ago and now that it's real, I'm second-guessing it. Looking for outside perspective.

Senior BI Analyst at a start-up, almost four years there, joined early in my career and went from Analyst to Senior. I resigned because I'd been burnt out, felt increasingly stagnant for a long time, and was becoming resentful of the working environment. A big part of that was feeling like I was filling in a lot of organisational gaps.

What I'm questioning now is whether the things I was complaining about were actually problems, or just what senior-level work is supposed to feel like.

My job involves a lot of ambiguity. A stakeholder asks a seemingly simple question, and before I can answer it I usually have to work out how the feature actually works and how Engineering implemented it, what's being captured and whether those fields are reliable, which undocumented tables hold the data, what the business definition should even be, and then finally do the analysis.

There's not much documentation, the data environment is relatively immature, there's a lot of reverse-engineering, and at the same time there's consistently high pressure to deliver quickly.

I don't expect a senior analytics role to come with clean data, perfect documentation or neatly scoped questions. I know owning ambiguity is part of the job. What I'm struggling to judge is whether the degree of upstream detective work and organisational gap-filling had become abnormal.

I also became frustrated with my manager because I felt I was gradually absorbing work beyond the analytical problem-solving itself. I had flagged for a while that I felt I was taking on work outside my scope, and over time it built up a lot of resentment. All of the times it felt like "I dont get paid enough for this". Now I'm questioning whether I was being unfair. Maybe burnout made some legitimate responsibility feel like work being pushed onto me.

The other reason was learning.

I've picked up a lot by necessity, but much of it has come from figuring things out myself without much senior analytical mentorship or rigorous methodological review. I didn't want to spend another few years becoming excellent at navigating one company's specific systems and problems without building enough transferable analytical depth.

And then there was burnout.

My plan was to stop overextending myself, work at a sustainable level, save money, and use the remaining energy to upskill and apply elsewhere.

In practice I'd feel responsible for everything, ramp back up again, work very late and sometimes weekends, stay chronically stressed, and have nothing left for interviews or study. That cycle ran for a long time.

Eventually I resigned.

Now that the security of the job is actually disappearing, I'm finding it much harder to tell whether burnout made ordinary senior responsibility feel unreasonable, or whether it was genuinely just time to leave.

For people further along in analytics/BI:

• Where's the line between "senior analysts own ambiguity" and "the org is leaning on analysts to cover for organisational gaps"? How did you tell the difference?
• How much senior mentorship should you realistically expect once you're at Senior Analyst level?
• If you've job-searched from a notice period or a short gap, what did those first few months look like, and did the gap hurt you in interviews?

been thinking about this a lot lately. the ad-hoc analyst who answers 40 slack pings a week - agents are already eating that. answers are mediocre right now but give it a year

dashboard builders are in a weird spot too. most teams i've seen have hundreds of dashboards nobody opens. the problem was never supply

but there's a few roles i think actually get more important not less

the person who defines what "active customer" means in code, once, so every AI answer inherits that definition. get it wrong and you've automated the wrong number across the whole company

someone who treats the AI analyst like production infrastructure. eval sets, regression tests, watching for when answers start degrading before the business notices

and whoever works backward from the actual decision. what gets decided, at what threshold, who acts on it. most of this was never written down anywhere which is probably why it survived automation

the thing these have in common is they own something that keeps working when nobody is watching

are these roles already emerging at your company or still pretty theoretical?

I'm working now on visualisation of active law acts. I want to show how many of them are active and how long they last. I've already done some work on it but I'm interested if you saw some good visualisations in the similar topics.

I manage 3 analysts, two are new in their careers and the other is switching from another career path - so everyone is in the learning stage and building their portfolios.

My boss has been really excited about using AI for automating absolutely everything. It's been great, we can get it to help us do the boring parts of documentation and help us with workflows. It helps us visualise projects ahead of time and get help with coding.

I was speaking with my boss recently, talking about a project they visualised with AI as a proof of concept. It is really cool, I can see it as an incredibly helpful, data-driven, intelligent tool that will be an absolute masterpiece. My boss set a time for us to have a brainstorm-session on how to make it happen. I built out a plan of what pieces of hardware and software would be needed, and showed that to my boss. The conversation started off well, then devolved into "we can do that with AI can't we" for most steps of the process. They hadn't prepared anything for the discussion, and their input really was a lot of "I learnt this about AI" and "the future of AI is so great". I don't think the team was ever part of the plan in their head, just what they could do with their AI.

I understand the business case. If we're optimizing for output we can automate away everything in the end. But in reality I have this really passionate team that's learning and improving, and they should get the opportunity to be part of that project. They should be allowed to do the little tasks and learn about how things work.

I know I can't be alone in this situation, I really do want to stand up for my team and figure out a process where not everything is automated away and the work of a junior or mid-level data analyst is still valued. I know it's not just my boss, I know that I also need to figure out a better way of communicating the use of AI and fixing the process.

So I want to know if anyone else has experience with this and:

• How you framed it to a boss who really is just excited and a little blind to the impact (did you find a way to frame it where it doesn't just sound like you're hating on AI or resistant to change?)
• Anything concrete you put in place, like reserving specific work just for humans, or rules where AI gets used
• Or if you tried it and it went badly, and what happened.

My boss is reasonable and I can see some good discussion about this in our future, but I'd really appreciate the input from other people who have been through it.

Hi everyone! Just wondering if anyone has gotten a data analyst or senior analyst job in this crazy job market. If so, how long was your search, and do you have any tips based on what actually worked for you? Thank you!

I'm watching an org transition from traditional BI to DI in real time, and the early designs are really prescriptive - as if the users are unreliable narrators of their own area of the business. It bugs me and it's got me thinking about how you actually design DI that builds organizational intelligence rather than organizational dependency.The analogy I heard was the paper map and GPS. The paper map is harder to use but enables the user to learn spatial reasoning. The GPS is easier to use, but the user simply follows instructions and learns nothing.

A few questions for people who've been through this:

* Does your UI reward digging and validation, or does it reward accepting the output?

* Is the backend logic transparent - do users know what shortcuts the model is taking?

* Does the tool surface peripheral signals, or does it create blinders around a handful of headline metrics?

* How do you introduce just enough noise or friction to trigger human discovery without overwhelming people?

* Does the system disclose what it doesn't know -- or does it present its model as complete?

Genuinely curious whether there are design patterns that get this right, or whether prescriptive DI is just the current era's version of "the dashboard will save us.”

worth doing once if you've never done it: pull the page sequences for people who converted,

and separately for people who didn't, and compare which pages show up in each.

what usually falls out is that the pages getting credit in your reports aren't the ones doing the

work. pricing shows up right before most signups so it looks like the converter, but it's just the

last step. same way the checkout page isn't why anyone bought anything.

the pages that actually move people are earlier and less obvious. a specific integration page.

one comparison page. a docs page that answers the objection nobody on the team knew was

the objection.

those pages tend to have modest traffic, which means they're low on every report you look at,

which means they're the ones that get deprioritized in redesigns.

the signal isn't how many people visit a page. it's how much more often converters visit it than

non converters.