Duration: 25:40
PART 1 — Analytical Summary 🚀
Title: From raw data to insight: hands-on guide to Graphs in Odoo Spreadsheet
Speaker: Marco, Business Analyst (Odoo Italy)
Context: A practical, live walkthrough showing how to turn ERP data into insights using Odoo Spreadsheet, Graphs, and Dashboards. Why it matters: many companies collect large volumes of ERP data that remain underused. The session demonstrates how Odoo’s native spreadsheet and visualization layer can make that data actionable — quickly, visually, and in real time.
Core ideas & innovations 🧠
Marco frames the challenge: raw ERP tables aren’t decision-friendly, but Odoo Spreadsheet with Graphs and Dashboards can transform them into clear, dynamic visuals. Using a realistic, fictional steel foundry called “Steelboarding,” he builds an end-to-end analytics view without leaving Odoo.
The dataset spans Sales Orders across Europe with strong product variability (categories, alloy families, and casting weights). The dashboard shows month-to-date performance with “trick-based” comparisons to prior month and prior year, a Europe map of sales distribution, and deeper product and sales analyses. He emphasizes clarity, usefulness, and dynamism: every chart should be self-explanatory, tied directly to ERP data, and refresh in real time.
Newer capabilities shine through. The Combo Chart (new in v19) merges revenue (left axis, €) and shipped weight (right axis, kg) by salesperson to reveal efficiency patterns (e.g., high revenue with low weight = fewer small shipments, less logistics friction). Chart expansion is now one-click, making detailed data exploration easier. The Treemap/Hierarchy quickly highlights underperforming alloy families, while a product-category pie shows a healthy balance. Crucially, many charts support drill-down to the underlying records.
Marco then demonstrates a second path: starting from Accounting > Invoice Analysis to build Pivot Tables, inserting them into Odoo Spreadsheet, and using the spreadsheet’s “pivo” function to reference pivots dynamically (including top N truncation). He duplicates a pivot to switch the dimension from Partner to Industry, links Filters (e.g., invoice date) to keep everything synchronized, and inserts bar charts. The result: live views of top customers and top industries that auto-refresh with ERP changes.
Throughout, he highlights practical tactics: duplicating datasets when different filters are needed per chart, preferring pivots over lists for performance on large data, and keeping the dashboard’s purpose clear to avoid chart sprawl.
Impact & takeaways 💼
This session shows that Odoo Spreadsheet can function as a native BI-lite layer:
- It keeps analytics close to where data lives — no exports, no external BI tools, no data warehouse required.
- It supports real-time decisioning with live ERP data and intuitive Graphs, Maps, Treemaps, and the new Combo Chart.
- It enables operationally relevant insights: seasonal comparisons, geographic distribution, product mix, alloy performance, logistics trade-offs (revenue per kilo), and sales efficiency by rep.
- It scales better when you use Pivot Tables, link Filters correctly, and avoid monolithic lists.
Limits remain clear and healthy: charts don’t write back to the ERP, and complex forecasting or cross-domain modeling is possible but manual. Exporting is indirect (drill into the chart to a list/pivot, then export CSV). Some drill-downs vary by chart type, and specific period-offset comparisons require spreadsheet tricks rather than out-of-the-box magic.
Bottom line: Odoo’s integrated approach simplifies analytics for business teams. With a disciplined dashboard purpose, smart use of pivots, and a few v19 enhancements, companies can turn dormant ERP data into reliable, day-to-day decisions. ⚙️💬
PART 2 — Viewpoint: Odoo Perspective
Disclaimer: AI-generated creative perspective inspired by Odoo's vision.
Our philosophy has always been simple: keep everything in one place and make it easy. Bringing graphs, pivots, and dashboards inside the spreadsheet means teams don’t have to jump across tools to understand their business. When data stays in Odoo, you get context, speed, and trust — the three pillars of good decisions.
I like how this session started from a business question, not a chart type. That’s the mindset we design for: start with the decision you need to make, then let the tools get out of your way. With v19 we focused on richer visuals, better performance with pivots, and little touches — like chart expansion — that make analysis feel natural. The community’s feedback keeps pushing us toward simpler, more integrated analytics.
PART 3 — Viewpoint: Competitors (SAP / Microsoft / Others)
Disclaimer: AI-generated fictional commentary. Not an official corporate statement.
Odoo’s native spreadsheet analytics are compelling for SMBs and mid-market teams. The unified UX and real-time connection to operational data reduce friction and speed up insight. The new combo chart and map visuals align well with day-to-day questions sales and operations leaders ask.
At enterprise scale, the conversation shifts to governed models, data lineage, auditability, and cross-system consolidation. Heavy scenarios (tens of millions of records, strict compliance, multi-source harmonization) still favor dedicated analytics stacks — think Power BI with semantic models or SAP Analytics Cloud with enterprise governance. Odoo’s approach is differentiating in simplicity and speed; the trade-off is depth in advanced modeling, fine-grained security, and formal lifecycle management of analytics assets.
PART 4 — Blog Footer Disclaimer
Disclaimer: This article contains AI-generated summaries and fictionalized commentaries for illustrative purposes. Viewpoints labeled as "Odoo Perspective" or "Competitors" are simulated and do not represent any real statements or positions. All product names and trademarks belong to their respective owners.