I understand data analytics and data science can both be predictive or descriptive, but in general, in the industry this is mostly how the jobs are split correct ?

Data science is more predictive like what will happen

Data/Business Analytics is more descriptive and diagnostic like what happened, whats happening, and why

Data engineering moves the data

Business analysis is analysing processes and prescribing solutions

AI is being added to almost every tool now, but what would you genuinely feel comfortable handing over to it?

For analytics apps that do need fresh data , would u use Lakebase for serving results or query the warehouse directly ? Or some other solution similar to it? What has worked better for you in tour org/experience ?

The last time I looked for a job was 4 years ago. I received 2 jobs offer after 4 weeks of looking and very good callback rate for jobs I was qualified for. I started looking again this year, tailoring my resume and only applying for jobs I qualify for.

Took me 1 month just to get a single interview and very poor overall callback rate despite using AI to only apply for jobs that I was a match for.

I heard people tell me how bad it is out there and now that I’ve tried it myself it really is that bad! I think I will have better luck doing an internal transfer but this job market really is garbage 😂

I'm a Data Analyst planning to build my first Databricks project for my portfolio. I have experience with SQL, Excel and Power BI, but I'm new to Databricks. Could you suggest some good project ideas that would help me demonstrate Databricks skills from a Data Analyst perspective?

I'm trying to decide what to put first in my learning: SQL for getting data into shape, or Python for doing more with it. For someone early in analytics, which gives the better foundation?

Recently applied for a position and heard back from a recruiter for a technical screen for an R&D analyst role! Does anyone have advice on what questions to expect for a 30-minute interview?

I had a job in analytics and am starting to realize that most things can be broken down into smaller sections regions - states - locations or brand - category - item. Is this what everyone realizes or is this just me?

hey everyone, sharing this because it’s directly relevant to analytics teams that are being asked to put AI agents on top of company data.

packt publishing is running a hands on workshop on Oct 31 on harness engineering, building an AI data analyst you can actually trust, with bruno goncalves.

what gets covered:

• timeouts, retries, re planning and execution budgets, so an agent run can’t loop or burn spend
• structured traces, so every run ends in a state you can inspect afterwards
• permissioned tool access, so risky queries are blocked before they reach your data
• a fault injected test suite, where you break tools on purpose and check the agent stops safely instead of inventing an answer

4 hours, online, Sat Oct 31, 9:30 AM to 1:30 PM ET. runs on a laptop with a mock model, so no GPU or API key is needed.

Full details and the workshop link is in comments

happy to answer any questions about what gets covered

Overstock, stockouts, ordering too early, ordering too late...curious what others have learned the hard way.

Advice on advocating for data warehouse vs vibe coded data infrastructure

At a medium-small software company in data analytics. The company has been around for a while but has no data infrastructure. To do all my projects I have to download reports from our different platforms.

We have a 3rd party data warehouse for product data. Recently I’ve been starting to quietly advocate to move our analytics data there. Admittedly we don’t have much data: one platform that measures consultant output, sales force, and billing pretty much covers it.

But I think it would be very useful to be able to query refreshing data sets, create updating reports, dashboards with that data.

Now I’ve already got some light objection, because the company uses ai heavily. we have most our apps (not the workload app) “connected” with some sort of AI infrastructure. (Guy responsible is not confident for using it as data storage, but still people are googly over ai) So the natural response is why can’t we do it with ai?

And I’m struggling to come up with a convincing answer. It would be convenient for me to query the data for EDA, it would give more people easy access to do their own investigations, and having solid safe data infrastructure is important for any company.

I just don’t know if these are compelling enough reasons to sell it as a business value. I’ve begun to work a bit with data engineering (who are only product focused thus far) and they seem to share the sentiment that it would be important.

But I don’t know how to convincingly present it to management. Keeping data safe, ease of reporting,

Any advice greatly appreciated.

Working in enterprise data and analytics at Polestar Analytics has made me interested in a recurring challenge: getting the technology working is often only one part of delivering useful analytics.

A data platform can be running, pipelines can be healthy, and dashboards can be available, yet business teams may still struggle to use the outputs confidently.

Sometimes the issue is inconsistent metric definitions. Sometimes it's unclear data ownership, a lack of trust in the numbers, or analytics that doesn't fit naturally into how decisions are made.

I'm curious how others have seen this play out in practice.

When an analytics initiative fails to deliver the expected business value, what tends to be the biggest obstacle in your experience?

• Data quality and integration
• Inconsistent business definitions
• Lack of stakeholder adoption or trust
• Poor alignment between analytics and business decisions
• Something else entirely

I'd be interested in real examples of what helped teams overcome the problem.

Hi there, currently I’m keen to get something up and running in my team and be one of the first to build up infrastructure to get some text to query going over my teams data. I think my team will be a great use case as we get many small adhoc questions coming our way and many questions can connect similar sources.
Right now I think I understand trying to build a semantic layer over things give a good description of our data maybe outlining all tables with data types examples of outputs and then get good descriptions on the metrics we want as well as example queries maybe.

My question is where are you putting all this and getting an agent to run over it? We have snowflake and I know this can all be done in there and have a semantic layer but my business is slow moving and other teams deal with that.
I however right now have vs code can connect my git repo into that and can run an agent over it.
Has anyone down this with something like that and just put info and context into your read me/.md files??

Would love to know where to start and hear peoples experiences of getting this going how useful it was and how much it’s changed there work.

Thanks

We tend to fall into this trap where we look at our KPI and it's hitting its target, so everything must be good, right?

Maybe you have a hospital and people are waiting an average of 28 minutes. The target is 30 minutes.

Great, you're hitting your target. What if last month they were waiting 18 minutes on average? Or maybe one area of the hospital is higher than others.

The KPI isn't bad. It's just not giving you the whole picture.

Same goes for dashboards. You may have your KPI over or under its target. But maybe the trend line is going in the wrong direction.

You can always segment your KPIs by area or time to see what's going on.

What do you use on your dashboards to give more information?

AI can summarize earnings reports, explain financial ratios, compare companies, and turn market data into readable reports.

The harder question is whether the analysis is reliable enough to use repeatedly.

Imagine a financial research workflow that combines company filings, earnings releases, historical prices, and market news.

What would you require before trusting its output?

My initial checklist would include:

• Every important claim should link back to its source.
• Numbers should include reporting periods, units, and timestamps.
• Reported facts should be separated from estimates and model-generated interpretations.
• Missing or conflicting data should be flagged rather than silently filled in.
• Risk scenarios should be presented alongside the main thesis.
• The system should explain what it cannot verify.

I'd also keep research and decision-making separate. Automating information gathering is very different from allowing an agent to execute trades.

For people working in finance, analytics, or investment research: what are the biggest failure modes you've encountered with AI-assisted analysis?

Say you have Google ads pointing to your site with all the UTM’s for tracking but when they hit your site, they are redirected to another subdomain on your site and all of a sudden the utm’s are stripped and the redirect happened too fast for the info to be recorded. What do you do?

When a spreadsheet looks wrong, the instinct is to delete duplicates first. I've started doing that last, because duplicates are usually the symptom of something earlier that was never fixed.

The order I use now is: data types, then whitespace, then structure, then duplicates.

Data types first because it's the cheapest check and the most expensive to miss. A column of numbers stored as text will sum to zero or be skipped entirely by aggregation functions, and nothing on screen tells you why . The values look like numbers. They are not. Filtering or summing a text column can return a plausible-looking result that is simply wrong.

Whitespace second. A trailing space makes "New York" and "New York " two different categories in a pivot table, and neither the filter nor the pivot will warn you . Same for nonprinting characters carried in from an export. This is invisible until you count distinct values and the count doesn't match what you see.

Structure third. Blank rows, subtotal lines, merged headers. If the range isn't a flat table, formulas and pivots behave unpredictably. This is a layout problem, not a data problem, and it's easier to fix before touching individual values.

Duplicates last. Removing duplicates before fixing types and whitespace can delete records that only looked duplicated because of a formatting difference, or keep records that are actually different but got normalized later . Once types and whitespace are clean, the duplicate removal is working against a consistent definition of "same."

This isn't a universal order. If a file has an obvious duplicate explosion from a bad join, duplicates jump to the front. But for the ordinary messy export, the duplicate rows are usually downstream of something simpler.

The part I'm less sure about: how much of this is worth automating versus checking by eye. Power Query can record the cleanup steps and refresh them next month, which handles repetition well . But the first pass on an unfamiliar file is still manual, and I haven't found a shortcut that doesn't just move the guesswork somewhere else.

Im really overwhelmed with the sheer amount of conflict on the future of business analytics/analysis roles.

In uni, I'm currently working on SQL, Python, Power Bi and getting exposure to ERM systems now as well

I need a genuine, non sugarcoated advice on what career path I can ACTUALLY aim for with AI slowly commoditizing entry level BA stuff.

• Is it worth specializing in data science, data engineering, or AI/ML instead?
• Would I be better off pursuing an adjacent field, such as risk analytics, analytics engineering, or a more business-focused role?

I'm not expecting any career to be completely AI-proof. I just want to make an informed decision about where to invest my time and what skills will actually give me an edge in the job market.

Any advice would be so really appreciated, Thank you so much!

I'm a data engineer researching recurring pain points on analytics and BI teams (may build something eventually, no links, not selling).

1. What do you still do by hand every week or month: reconciling numbers, pulling data from an ERP, fixing a broken dashboard, prepping something for audit?
   
2. Why hasn't it been automated: no time, too many edge cases, nobody owns it?
   
3. Have you paid for a tool or consultant to fix it? Did it work?

A manager tells HR the sales team feels shaky and they might start losing people. The HRBP narrows that into something analytics can work with: attrition by department. The team builds a lovely dashboard with every slice and date range you could want.

Every handoff did its job. Nobody screwed up. But the manager's real question was how to spend a retention bonus budget, and that never made it down the pipeline. Repeat that enough times and you get dashboard hell... a great wall of data and a team that spends its days firefighting.

The fix is one sentence the requester completes before sending anything:

"If it comes back X, I'll do A.

If it comes back Y, I'll do B."

In the sales case: if attrition in critical roles hits X, we fund a retention programme. If it's Y, we do nothing.

Now the analyst knows what matters and which threshold to test.

If they can't fill that in, they're curious.

That's fine, but it isn't a request yet.

I also got into how I'd score an analytics team, because "we delivered 20,000 reports on time" is a reporting scorecard... not an analytics one.

Does anyone here actually track decisions influenced rather than deliverables? How?

I am an ML engineer and not selling anything. The thread a couple days ago about everyone having Claude wired to the warehouse hit close to home. I’m trying to understand how teams are actually dealing with it.

For those of you living this:

1. When someone outside the data team publishes a number or a dashboard built by an agent, how do you find out, and how do you check it against the official metric definitions?
2. What’s the thing that actually blocks you from letting people do this safely: row level security, refresh, permissions, metric drift, or something else?
3. Has a wrong agent-built number ever made it into a decision? What happened?

Not looking for “just use a semantic layer” unless you can say what still slips through with one.

Here's my mental model of the change happening in analytics.

• Ad hoc analytics are moving from spreadsheets to AI tools like Claude Dashboards.
• Good data and semantics were always important, still more so now. Semantics have a natual center of gravity close to the data
• Business Intelligence tools were losing the battle for the semantic layer . Now they're also losing the battle for analytics creation. So they are basically left with a deployment layer, but a slow, expensive one.
  
• Organizations still need to share analytics sometimes and have them refreshed, trusted and managed. Ideally they will also be fast and interactive and have some form of conversational analytics. Claude Dashboards doesn't do that.

First of all, is this how you're seeing the change happen?

Second: for those of you planning on using Claude dashboards or other AI builders, how are you planning to deploy analytics when needed? Like for in-product analytics for customers, or across your whole org? Are you using a BI tool that you already have, and how is that going?

My take is there's no way that legacy BI becomes the deployment layer for AI-based analytics. Would love to hear what you're doing or even just discussing at your shops.

Running into the situation where I report web visitors by such and such ad campaign and then PPC comes to me and it’s like wait wait we expect way more visitors or whatever. How do you guys deal with this? At the end of the day, I don’t really know how to say this is just what I see yours might be different because it’s a different platform. Do you all gain access to the reporting platforms to verify more? It doesn’t help that we are a small shop and so it’s not like we have a data warehouse where I can pull all extreme data together. It’s literally me looking at GA four reports and then they sent me slides that say different things.

1. Daily reporting

2. Inventory alerts

3. Customer support

4. Marketing reporting

What makes that task so painful today?