Lean on DataFoodSight: the missing benchmark for F&B
An intelligence and benchmarking platform for restaurant operators. Hotels have STR, HOTSTATS and revenue management. Restaurants have nothing. FoodSight closes that gap, starting in Dubai.
Lean on DataHotel operators run on shared intelligence: STR for the market, HOTSTATS for the P&L, revenue management for pricing. Restaurant operators fly blind. There is no equivalent in F&B, in Dubai or anywhere in the region.
The US has one reference point, Black Box Intelligence, and it took years of grind to accumulate the data. The UAE market is younger and more disparate, which is exactly why the door is still open. Whoever does the hard work of signing the first operators owns the category.
Guido validated the thesis without hesitation and pressure-tested it in the right places: is anyone already here, why has no one built it, and is there enough comparable density for a benchmark to mean something. The answers held. His one caution is that this is a long, high-touch sell, covered under Risks below.
Where it came from. The idea started with Sudhin Siva, who saw the gap and shaped the give-to-get commercial model. Anindo Banerjee and Daryanand Shetty then blessed the architecture and set the Microsoft Fabric direction. Guido de Wilde validated the thesis, and Harry Johnson sharpened the go-to-market. Their input runs through the sections below.
Become the shared intelligence layer for F&B operators. Give each operator revenue-grade insight on their own data first, then build the industry benchmark the market has never had.
Operators will not hand over data for a benchmark on day one. So we do not ask them to. We lead with a dashboard that gives them insight on their own data they have never had, then accumulate toward the benchmark. Every major benchmarking player did the same, entering as a POS or aggregator first.
A strong, standardised template with real presence: infographics, league tables, an executive summary, and budget and prior-period comparisons. Revenue-management models, menu engineering, predictive prompts, and a plain-English "ask your data" layer on Fabric-connected data.
Built on Microsoft Fabric, connected to the operator's own Fabric. An LLM never touches a source database: data flows source, to a curated data lake, to an MCP layer, to the AI and front end. Each operator is fully isolated, raw data is never exposed, and the benchmark surfaces only aggregated figures over a minimum basket.
Operators contribute their data to unlock benchmarks, rather than buy access without contributing. The dashboard is the reason to contribute in the first place.
The thesis is concentration. Land roughly six large operators and the long tail of 1,000-plus outlets follows, because the benchmark only becomes useful once the anchors are in. Dubai first, with micro-market granularity such as JLT versus Marina, then Abu Dhabi and Sharjah.
Sponsor and business development. Owns the operator relationships, the pitch and the founding-cohort push.
Product and AI/analytics lead. Builds and demos the platform, the data architecture and the dashboard.
Originator. Seeded the concept, shaped the give-to-get commercial model, and opens the Shamal channel.
Architecture advisor. Set the data-platform mandates and the anchor-cohort logic, from an enterprise IT lens.
Data-platform advisor. Microsoft Fabric SME. Steered the backend onto Fabric.
Champion. Validated the concept and is opening doors to F&B operators through his network.
Connector. Knows the operator landscape and can shape the target list. Meeting to be arranged by Guido.
First data partner. Bringing Shamal's F&B estate into the benchmarking play. Sudhin already warm.
Strategist and connector. Sharpening the go-to-market and opening doors, including Stefan Breg and Kevin. Deep operator and mall-market knowledge.
Advisor. Ex-Starwood F&B, now consulting. High-level read on the concept and the market.
Commercial connector. Industry-embedded across Thailand and the Far East, introduced by Harry. The commercial overlay to the data and industry expertise, and a bridge to phase-two expansion.
| Risk | Mitigation |
|---|---|
| Operators reluctant to share data | Lead with the dashboard value on their own data. Secure Microsoft architecture, raw data never exposed, give-to-get contributory model. |
| Long, high-touch sales cycle Guido's main caution | Qualify genuine interest in the room and set the next meeting date on the spot. Run a tight, sequenced cohort push rather than a broad spray. |
| "We will build it ourselves" operator IT teams | Position as the benchmark custodian and partner, not a report-builder. Anyone can query their own data; only we hold the cross-operator benchmark. Differentiate on think-with-AI: natural-language query and predictive insight an in-house BI team does not deliver. |
| Thin benchmark validity | Ensure enough comparable concepts per cohort before publishing. Release only aggregated numbers over a minimum basket. Keep the benchmark format stable over time, Gartner-style, so credibility compounds. |
| Credibility from a standing start | Secure a high-profile anchor early so the pitch can borrow its trust, per Guido's advice. |
| Confidentiality in the demo | Keep the line clear: first-party reports show only the operator's own data; the benchmark shows only aggregated figures. Never share commercially sensitive cross-operator detail. Each operator is isolated inside Microsoft. |
| Third-party data sources | Treat data sourcing as a commercial decision, not just a technical one. For any aggregator or provider, ask who owns it, is it free, how is it qualified, and what it costs us in commercial position. |
| Pricing pressure | Deferred by design. Prove concept and demand first, then package. |
WhatsApp Guido the reminder he asked for, to set up the session with his Head of F&B, Max, who can shape the target-operator list. Meeting: Nitin, Ritta, Guido and Max.
Use the Wednesday session to connect FoodSight to Shamal's Fabric, without hosting their data, and use an industry dataset for day-one benchmarking.
Wednesday advisory call. Get his honest read on the concept, the design points to sharpen, and a first set of introductions and targets.
Lift the dashboard to Harry's bar: a strong standardised template with infographics, an executive summary, and budget and prior-period comparisons. Add the revenue-management models, lock the FoodSight identity, and polish the ask-your-data and predictive views before approaching the anchor cohort.
Land one high-profile operator willing to be named as backing the concept, so every subsequent pitch carries borrowed trust.
Firm up the six anchor targets, warm-first, and work them one by one toward a signed data contribution.
Once v1 is where Nitin is happy, Harry shares the stack with Kevin for his commercial read and introduction, then set a proper joint meeting. Also a bridge to phase-two Thailand.