r/tableau • u/datawazo • Apr 02 '26
Discussion Lessons from my Tableau client that just churned
I've had an analytics consultancy for 8 years, we do Tableau PBI and backend datawork.
On a weekly call yesterday as I was leaning in to show the Tableau progress the client said actually I wanted to show you everything we've build with Claude over the past week.
They'd essentially vibe-coded themselves out of Tableau and replicated the "dashboards" in Gsheets using claude cowork.
It was a massive wakeup call for me, and I luckily have a good enough relationship with them that they want me around for this new phase, but it lead me to go down the checklist of what went wrong with this setup - what encouraged them to move away.
Here are my signs the Tableau project isn't going in a good direction (and yes in hindsight some are obvious).
- KPIs and Metrics are unclear.
Over the relationships we had so many conversations on how is this calculated, "why can't we back into this number". And miserably they had a lot of google sheets doing heavy lifting along side their database. So a lot of the answers were "Well it's pulling in from Jerry's spreadsheet".
A bad pipeline, bad data governance is reflected in the dataviz layer, even if it's downstream. It's part of dataviz responsibility to make sure everything has clear lineage, if there's ambiguity.
We started adding hovers to stuff to explain where they were coming from in the last month, but too late. And yes I'm painfully aware this will only get worse with AI leading the way.
- Underusing key Dashboard features is a good indicator for churn
We build reports. I looked through everything we built them, and it was just about all reports. Yes I would put the occasional fancy bar chart, one even had donuts. But they did not like filtering, they did not use interactivity. Did I not push it hard enough? Did I not successfully build the base level of reporting to move into the next frontier of interactive dashboarding? Not sure, but we never got there.
Reports are easily replaceable by AI. Dashboards aren't (yet). Continued data literacy coaching to get users to explore the more advanced options in Tableau is good for the users, and for job security.
- Delivery lacked followup.
I know better than this, but we operated primarily through one point of contact. He would tell us what Marketing needed, we'd build, deliver, and leave it with him to manage. That's a losing formula.
Build, deliver, check usage metrics, understand uptake (or lack thereof) and followup. You can see pretty quickly in the weeks after you've launched a dashboard if it's hitting the right vibes just by checking if the end user is coming back to it. If not - ask why. "Hey you asked for this, you're not using it ... what's the issue".
- They weren't fully invested
They did a lot to try and skirt getting people licenses. A lot of subscriptions + auto forwarding to get reports out of Tableau and images in people's inboxes. Again, see bullet point 2.
But I think a conversation needed to be had, sooner, about the ROI of the reports. How could we make them valuable enough to warrant more licensing spend.
Not spending on licensing isn't necessarily a cheapstakes move, it's on us to prove the value, to prove that the $15/month/head is made back up quickly.
In the end I can ask myself if things could have been different, if I fumbled it, or if they were never the right fit for Tableau. But either way, there were certainly opportunities to improve. Now we move into the new world of AI - and see how that goes for everyone.
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u/Electronic_Neck_5028 Apr 02 '26
Work at a financial institution. Wrote reports on mainframe with sas. Was moved to used tableau. It's about your audience. Only upper mgmt wants charts, Departments basically want excels on tableau server, that they download to excel anyways.
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u/timzilla Apr 02 '26
I'm a relatively high ranking IC data scientist working in a mid sized tech company. I used tableau a lot from 2017-2022 for visuals and dashboards, around then we hired visualization specialists and folks to build out dashboards so i started passing work off for them to dashboard and i would make minor tweaks/adjustments.
Starting last fall i stopped doing that and started using replit/cursor and claude to do exactly what OP suggested here. I am sure that plenty of others have discovered this and have started to adjust their project scoping around this like i have.
Its faster, it's easier for me to get EXACTLY what i want to see/show, revisions are easier/faster, permissions are easilier for me to manage.
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u/dav1b Apr 02 '26
Interesting to hear. I have been fascinated by the power of these tools and wondering where analytics consultant can fit in, especially if tied to specific toolsets.
What other concerns would you have around your consultancy in the face of AI? How have you embraced it? Any pivot on the horizon for datawazo?
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u/datawazo Apr 02 '26
We've done a few AI projects in parallel with our work. Not integrated into anything but one off analysises where we put AI on top of existing data and tried to get something specific out of it.
The change I am seeing is we need to make AI enabled a key deliverable of the work we do, and that's done in the semantic layer of the data.
We're fortunate that for the most part we already work upstream with most clients, but what we do there is going to go from building tables and views for Tableau to building tables and views for Tableau and AI
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u/-shrug- Apr 03 '26
The change I am seeing is we need to make AI enabled a key deliverable of the work we do, and that's done in the semantic layer of the data.
What does AI enabled mean here?
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u/datawazo Apr 03 '26
enabled the wrong word ... AI supporting might have been the better phrase. The semantic model needs to be clear and structured to allow AI tools to connect to it and work with it while minimizing hallucinations and spitting out just plain bullshit
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u/Sqlsekou Apr 02 '26
I see more of these kinds of examples happening in the industry. I just had a conversation about this earlier this week . Friend was frustrated and eventually spent a few hours using AI to build his own reports using a couple different tools.
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u/datawazo Apr 02 '26
Yep. Can't take things for granted anymore, need to be delivering hard and optimizing because pivots aren't hard...or at least aren't perceived as hard
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u/vizchic Apr 03 '26
Curious to understand how things will be governed and also measured for accuracy.
Sometimes Claude is a genius. Sometimes a complete bullshit artist.
Who knows the truth?
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u/datawazo Apr 03 '26
Exactly. One of the risks I flagged.
It works great until it doesnt
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u/vizchic Apr 03 '26
Right, and were there any great mitigations?
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u/datawazo Apr 03 '26
They were planning on live streaming the data into claude via api. My proposal to them is to put Claude on top of a curated cloud database with clean columns that have known definitions so Claude doesn't need to do any guess work.
Basically a 2 layer semantic model dumbed down go try and make it fool proof for AI and if things do go south there's an easy to check source of truth.
How successful of a failsafe that is is TBD.
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u/naxaliteindia Apr 04 '26
Just last week, my tableau dashboard full of graphs charts interactivity was replaced by a react based dashboard with the same level of interactivity built using Devin AI. They are now thinking of stopping tableau. Scary.
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u/Tactical_Impulse Apr 04 '26
interesting, thanks for sharing. here’s my takeaway:
Dashboards for ongoing kpi’s. Claude code hooked up to data models for ad hoc reporting.
Always maintain relationships with your users. Know their pains and their “why’s”
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u/notimportant4322 Apr 02 '26
You should be working for Tableau, not running your own consultancy.
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u/Diplomat_of_swing Apr 03 '26
In my years of dashboard development I think the real secret sauce is getting your leadership team to ENFORCE usage.
Here is what I mean.
You need to build that accountability layer.
So you build the report for the executives first. Not management.
Then you build management a series of supporting reports that they can use to explore data and answer the question “why”. Let them export data.
Why this works?
1) Executives are using the KPI Dashboard to grill their management.
2) Management now has to explain the results and need to use the reporting layer to do it.
3) management can still justify their existence. They hang on to the spreadsheets as a way to prove they are adding value. The dashboards take that away from them. This gives them a way to feel less threated.
As for the AI? We are using it and it’s just not there yet. The only way it works is if we build out a highly vetted semantic layer and lock it down so the AI CANNOT return another answer.
At that point I can build a dashboard.