r/BusinessIntelligence

We’ve been noticing this a lot data today comes from multiple tools, systems, and platforms.

But when everything is connected, even small mismatches can create confusion.

Sometimes the challenge isn’t collecting data, but making sure it’s consistent and reliable across systems.

In your experience, do you fully trust your data or do you still end up double-checking it?

In your opinion, what are the options for entrepreneurship and business, related to data analysis and skills used in it? One would be education of course, but, what are others and does anyone from this community have that kind of business?

We keep running into a specific failure repeatedly and hence this post. I'm trying to figure out if it's just us or if others have this problem too.

We work on operations of large vehicle fleets. Our business users (ops managers) can get answers fine when the data is in one place, but the moment a question needs two systems — say, joining something from a database of vehicle arrivals, departure, with data from a third-party API or a CRM — they hit a wall.

Either they wait for the data team to write a one-off query, or someone starts exporting stuff to CSV files and starts stitching it together in Excel.

We've tried Metabase (single-database only, no cross-source joins) and pointing people at Looker requires a data engineer to update the LookML every time the question changes. What does this look like at your company?

• How often do requests like this come in, and what's the turnaround time?
• Have you found anything that lets a non-SQL person query across more than one live / data source without engineering involved?
• Or is the answer just "centralize everything into a warehouse first" — and if so, was it worth the project cost?

Every modern data stack tool now seems to have an "AI assistant" built-in. Honestly, I find them incredibly useful for boilerplate SQL or quickly drafting documentation, but they completely fall apart on complex, multi-table enterprise logic.

Has anyone found an AI tool that actually understands a messy company data model without needing massive hand-holding?

We've been on Qlik Sense for 6 years, 4 of those on their cloud platform, and I think it's time to re-evaluate where we should be heading.

Here are a few things that make me want to move elsewhere:

• Currently using SQL Server 2017 as a data warehouse. It works, but it's starting to be limiting for internal reasons outside my control. I'm aware you can build your own "data warehouse" in Qlik using load scripts, but it's not great.
• The Execs never bought into the tool, and they want to stick to Excel. It's been 6 years, and we've tried to accommodate them, but it just doesn't stick.
• Their prices are getting more and more bonkers every release, and they keep trying to justify their increases by selling us more shit we won't use.
• After 6 years, users still don't understand set analysis, and newcomers see the syntax and run away.
• It has poor email capabilities (note that I didn't say none) unless you buy third-party add-ons or pay a fuck-ton of money to Qlik for capacity.
• Our company policy prevents users from creating their own apps from scratch, which means we, the BI team, are fully in charge of making the data models for them. They can do self-service, but inside what we create. This has obvious pros and cons.
• We had an awesome buy-in for the first 3 years, coming from Cognos, where I started putting a lot of "never seen before" datasets in the tool to attract users on the platform.

Now the user count is dwindling — we're down to maybe 10 slightly active writers and hundreds of readers who go in, download to Excel, and get out.

Why not Power BI?

I think PBI is just a side upgrade vs Qlik. Also, we're using Google, not Microsoft, for our company tools. I'm aware it's in the right spot on the Gartner chart, but considering how it's basically "buy your spot on the chart", it's sort of meaningless.

Where I'd like to go

I'd like to move to a cloud warehouse for small data (we're at 1.5 TB before any compression), such as Motherduck, and leverage the power of the cloud tools to move to something like Sigma or Omni.

Why Sigma or Omni? My top 3 reasons:

1. Not having to mess around with reloading the warehouse, then reloading another proprietary data set in Qlik/PBI is a big win here, allowing for faster load times for some of our workloads.
2. They seem very Excel-like in the way they work.
3. Emailing capabilities to send reports to the older execs who refuse to use anything but Excel.

My questions for you:

• Do you know those tools, and have you used them?
• How has your experience been with them?
• What does the pricing structure look like?
• Am I wrong in saying PBI is not where we want to be heading?

Thanks!

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Most early-stage businesses don’t fail because of bad ideas.

They struggle because of execution patterns.

From what I’ve observed:

~60–70% keep switching strategies too early ~50% try to grow on multiple channels at once many never reach consistent output

The result:

No data → no learning → no growth

Growth usually starts when:

one channel is prioritized actions are repeated consistently decisions are based on data, not guesses

I am all in. Going to be taking the FP&A Wharton Business School and WallStreet Prep Certification (https://wallstreetprep.wharton.upenn.edu/financial-planning-and-analysis-certificate/). Price is not a issue as it will be reimbursed. Already have an Graduate Degree in Business and work in analytics. Looking to expand my skill set and advance my capabilities. Am curious 🤔 as to those who completed the course. What did you think? Did this course teach you anything new? How were the exams?

I'm getting more skeptical of green checkmarks.

In a normal BI or ops flow, "job completed" is usually enough to move on. In these newer agent workflows, that status can be almost useless. The run finishes, logs look clean, and then you find out the important side effect never happened.

One example from this week, the system wrote the internal note, updated the record, and marked the run complete. What it did not do was create the task the team was supposed to work from. So the dashboard stayed green while the actual work queue stayed empty.

That feels like the real headache with AI ops right now. Not generation quality. Verifying that the handoff actually created the next real thing.

Are you all checking for actual downstream artifacts now, like task IDs, row counts, message IDs, calendar events, before you trust the dashboard?

Lately, it feels like Business Intelligence (BI) is no longer just a “nice-to-have” but something every company needs to survive.

With so much data being generated—customer behavior, sales trends, product usage—the real advantage seems to come from how well you can interpret that data, not just collect it.

But here’s what I’m wondering:

• Are companies actually using BI tools effectively, or just scratching the surface?
• Is BI being replaced (or enhanced) by AI-driven analytics?
• For startups, is investing in BI early worth it, or should it come later?

It seems like the companies making smarter, data-driven decisions are pulling ahead faster than ever.

Would love to hear from analysts, founders, and anyone working with data— How important is BI in your workflow right now?

I am a financial controller. Recently learned power query and powerbi and trying to decide how it might support my internal reporting. I'm thinking powerbi might be not as helpful as I thought - the front end seems so rigid.

Currently I have a few large Excel files I'm pulling various charts and data for to make a dashboard and it just feels everything is everywhere. I do have some spare time now to build some kind of framework but I'm trying to decide what is best.

Reminder that pipelines still cost money even if you stop using them!

If there is a new conversational AI platform for your petabytes of data at various sources, what metrics do you evaluate before start using that?

Is it accuracy? Latency? (Real time and near real time updates to the data) Cost? or convinience that at any point of time you may ask any question and you should able to get the answer?

edit: security is considered as non-negotiable

We build an analytics tool aimed at non-technical users, and getting adoption beyond the data team is still our biggest challenge. We know executive sponsorship and embedding insights into existing platforms helps - but what else actually worked for your org? For those of you who've rolled out BI tools to non-technical teams, what got people to use them consistently?

We recently got hit with an unexpected bill from AI tools our employees have been signing up for on their own. Different teams are using different tools, some overlapping, some we had no idea even existed in our org. Finance flagged it and now IT and security are both being asked to fix it but honestly we dont even have a clear pic of what tools are being used, who is using them or what data is going into them!!!!!

The cost issue is just what surfaced it but the deeper problem is we have zero visibility into AI usage across the org. No policies, no controls, nothing.

Has anyone dealt with something similar? How did you get visibility into what AI tools are actually being used across your org? Is there something that sits at the browser level or network level that helps with this??

Has your startup ever made a wrong decision because your data was silently broken? I'm researching this problem.

I'm talking about things like:

• A dashboard showing wrong revenue because of a broken sync
• Duplicate leads flooding your CRM without anyone noticing
• A KPI that quietly changed definition after a schema update
• A report that was wrong for weeks before someone caught it manually

I'm not selling anything. I'm in the early research phase of building a tool that automatically monitors startup dashboards and data pipelines, catches these silent errors, and explains what went wrong in plain English — before leadership acts on bad numbers.

If this has happened to you — even once — I'd love to hear the story. What broke? How did you find out? What did it cost you in time or bad decisions?

I’m 23 years old, living in Hamilton Ontario (an hour from Toronto, Ontario) who just got laid off from my day job recently.

Last year in the summer I fell into the hype of starting my own ai business (as a side hustle at the time) selling AI receptionists, I got some response by cold emailing from my personal email account. I didn’t have any sales experience at the time so I lost the sale but it was a good learning experience for me.

Then I watched a few Nick Saraev videos and I followed what he showed in his video of how to start with $0 which I followed and got some responses and fulfilled them but then the worst thing of my life happened, which is.. my dad passed away and I gave up all the side hustles and stopped it for a few months. (Up until December)

This year I started back up and those few months that I was gone, the landscape shifted so dramatically and drastically.

No one wants ai receptionists anymore, my previous clients they themselves don’t want it anymore, I guess it’s still a bit gimmicky even if it is just for after hours booking, it gets the job done but it still isn’t gone to a level where local businesses especially in a place like Toronto are willing to adapt. (At least in my opinion)

I did some research with a few different ai models about the type of product or service based on my experience and my life would be better for me to sell as an entrepreneur and being in the locality in which I’m based, I got mixed results and some are saying to continue selling ai receptionists (which I already stated why not many businesses around me want it), another thing that I found was a service called “14 day cash clarity sprint” for contractors where in 2 weeks I clean up their books and find them undiscovered cash which they may have (basically a service that is not really an accountant or a bookkeeper but someone who categorizes and perfectly sorts documents and invoices and continuesly gives clients a dashboard of how much each job actually made them profit, basically helping them improve their margins and cash on hand)

Me as an inexperienced 23 year old found this business to be different enough to work in the contracting space as well as the location that I’m in, but then I had trouble with outreach. Most of these businesses don’t check their emails so cold email didn’t work. Then I tried Instagram DMs, which had a bit better response (which I interpreted as market validation) but horrible conversion (literally 0)

I couldn’t do cold calling cuz I had a job but I got laid off a couple of weeks ago because of budget cuts in the company so I don’t have a day job either. All I have is my side hustle, sort of slowly becoming my main hustle. After this had happened I started cold calling, but with contractors they’re very busy, they told me to F off, told me to send them an email (which they never read), told me to send them a text (which they probably deleted).

Obviously as I got laid off, I need money coming in and I need it asap. So I am getting abit desperate and that’s why I’m writing this post.

But I need help with direction from someone who started from scratch and build a business like this on

- should I start cold DMing again? (Requires low effort but relentless follow up; my follow up gave could use improvement)

- should I continue to cold call? (Requires high effort and high skill improvement; my cold calling skills could use improvement as well)

- how/when/at what point do I decide to alternate between the two methods of acquiring clients or switch methods entirely?

- a part of my brain thinks the early response I had with this product was not market validation but was a fluke and I mistook it as market validation, is that true?

- at what point do I switch my offer entirely and start thinking or looking at something else entirely that could make me some money faster?

(Even last night I was thinking of selling something else that A-already has a demand/market validation and is already a pain point or bottle neck in a certain industry or the other. B-is under threat because of AI and can also be solved by AI. C-doesn’t restrict me in local markets but is demandable world wide (especially in the US). D-doesnt require a lot of time being spent in upgrading my skills (though it is important) before making any money. E-Will have a much easier channel of client acquisition.)

In my free time I chat with different LLMs to see what they’re saying. Claude told me to keep going with this and try DMing for a week with persistence and a proper system and not to stop or pivot until I get to a 100 DMs sent and only then can we identify how effecting cold DMing is. If it’s effective, great. If not, switch to cold calling next week. Gpt told me to start selling AI receptionists again through cold calling local businesses again (which worked in the beginning, but through cold email, gpt is saying now try cold calling) Perplexity told me to try a new model entirely, Gemini told me to keep going (just like Claude did) but told me to start cold calling immediately and abandon DMs.

If you read this far, i genuinely appreciate you, thank you so much, God bless.

Every month I spend 30+ hours pulling data from qbo, harvest, hubspot, gusto, cleaning it, building reports in excel, making charts, pasting into slides. It's miserable. I'm a finance manager not a data engineer so building a warehouse isn't realistic.

How are other finance people automating this?

I’m researching CRM tools in the medspa industry.

Most platforms focus on bookings, but fewer emphasize retention analytics and loyalty ecosystems.

What systems are you using?

What’s missing from them?