Data Panda lands the data the rest of your business already produces in a warehouse and curates one model your Tableau workbooks build on. Dashboards, scheduled reports and ad-hoc exploration come off the same numbers, not three competing extract files.
Tableau was founded in 2003 out of Stanford research on visual analytics, went public on the NYSE in 2013, and was acquired by Salesforce in 2019 for $15.7 billion. The platform spans Tableau Desktop for authoring, Tableau Server and Tableau Cloud for sharing workbooks, Tableau Prep for in-tool data preparation, and Tableau Pulse for metric-driven alerts. Its strength has always been visual exploration: drag-and-drop authoring against a schema, with a calculation language built for analysts.
That strength is also where Tableau pages go wrong. Every workbook can connect to a different source, save its own extract as a .hyper file, and define its own version of revenue. After a couple of years a typical Tableau site has hundreds of extracts on overlapping refresh schedules and three published data sources for the same fact table. Pulling Tableau onto a curated warehouse model puts the calculations, joins and grain in one place, so the workbooks become a presentation layer on top of one definition rather than the place where the definitions are invented.
Curated warehouse models feed every Tableau workbook from one source of truth.
The warehouse refresh feeds Tableau, not hundreds of overlapping extract jobs.
Forecasting, anomaly detection and Pulse insights run on warehouse models, not extracts.
Internal tools and embedded views read the same warehouse Tableau reads.
A list of concrete reports, automations and AI features we have built on Tableau data. Pick the one that matches your situation.
Which Tableau workbooks would change their numbers if the same logic were applied to all of them?
A scan of the published workbooks against a single warehouse model shows where revenue, margin, churn or pipeline are calculated three different ways. Reconciling those three to one definition turns the next steering committee into a discussion about the business, not about whose dashboard is right.
How many of our Tableau extracts are still refreshing for nobody?
Server and Cloud usage logs joined to the extract refresh schedule reveal extracts that haven't been opened in months but still cost a refresh slot every night. Pruning that list cuts refresh load and surfaces which workbooks really steer decisions.
Where are we paying for Creator licences that should be Explorer or Viewer?
Cross-referencing the licence tier with actual authoring activity shows accounts paying for Creator who only consume content. Once shared models exist, fewer people need to author from scratch, and the licence mix shifts toward Explorer and Viewer at material savings.
One revenue and margin definition feeding every Tableau workbook, reconciled to the ledger. The board pack and the analyst's exploration view show the same number, and the monthly close stops getting questioned by a workbook that calculated things differently.
Salesforce opportunity data joined to billing, product and ops data inside one Tableau model, not stitched per workbook. Pipeline coverage, win-rate and forecast accuracy come from the same data the finance team trusts, so the forecast meeting stops being a debate about whose extract is fresher.
Workbook count, extract refresh schedules and licence-tier mix become observable on the warehouse side. Server or Cloud admins manage one model layer instead of debugging hundreds of one-off extracts authored across the business.
You keep the reporting tool you already have. We connect it to the warehouse where your Tableau data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
Both, by design. The warehouse model is the source of truth, and workbooks read live where the warehouse is fast enough or via one shared published extract per model where that performs better. The change is that an extract is per model and refreshed once, not per workbook with overlapping schedules.
Salesforce Data Cloud is one possible source the warehouse can pull from, alongside SFDC core, the ERP, web events and the rest. The warehouse remains the curated layer feeding Tableau, so you are not locked into one Salesforce-owned data path even if Tableau itself sits inside the Salesforce stack.
Usually yes, downward on Creator. When shared warehouse models exist, fewer accounts need to author from scratch and more can consume on Explorer or Viewer tiers. The licence-mix saving often pays for the warehouse work within the first renewal cycle.
We review your Tableau setup and the systems around it. Together we pick the first thing worth building.