Overview
Vizlake Analytics is a data analytics company focused on enabling users to convert their data into actionable insights. The company operates in the information services sector.
In the news
- One KPI, three definitions: why your board meeting turns into a debate about numbers Picture a monthly finance review. Sales reports revenue of €4.2M. Finance reports €3.9M. The Power BI dashboard says €4.05M. Nobody is wrong; they are simply using three different definitions. Forty minutes later, the meeting is about reconciling figures instead of making decisions. This is a KPI governance problem, not a dashboard problem. No amount of visual polish fixes it. Here is how we approach it at Vizlake: 🔸 Write a one-page KPI
- Your finance team is drowning in year-end reports. What if they didn't have to be? October is wrapping up. Q4 is in full swing. And if your finance department looks anything like most mid-sized companies, the next 8 weeks will be a marathon of closing the books, reconciling numbers, and pulling together board-ready reports — all while planning next year's budget. Here's what we see too often: 🔸 Finance teams spending 60-70% of their time gathering and cleaning data instead of analyzing it 🔸 Reports that take days to compile
- Your Power BI report takes 40 seconds to load. Your users have already stopped opening it. Slow dashboards are rarely a "Power BI problem". In most cases we audit, they are a design problem — and a fixable one. Here are the 4 culprits we find most often in finance reports across Belgium and the Benelux: 🔸 DirectQuery everywhere. It feels "real-time", but every click fires a query at the source. Import mode (or a composite model) is usually 10x faster for month-end and management reporting. 🔸 No incremental refresh.
- Your next data hire shouldn't be a hire at all. Most mid-sized companies hit the same wall: you need better dashboards, cleaner data pipelines, and real-time reporting — but building an in-house BI team from scratch takes 6-9 months, costs a fortune, and might not even work out. What if you could plug in a complete data team — without a single recruitment round? That's exactly what our Data Team-as-a-Service model delivers: ✅ Full-stack BI coverage from day one. You get a dedicated team of Power BI developers, data engineers,
- Your CFO dashboard is only as reliable as the layer underneath it. Many finance teams we meet in Belgium and the Netherlands build Power BI reports directly on top of raw ERP exports. It works at first. Then month-end arrives, a source system changes a column, and three versions of "revenue" appear across three reports. The fix is not another dashboard. It is a clear data architecture. We use a medallion approach on the Azure data stack: 🔸 Bronze – raw data, landed as-is from ERP, CRM and Excel sources. Nothing is altered,
- Your finance team deserves better data — without the overhead of building a full BI department. Here's a reality most mid-sized companies face: you need enterprise-grade reporting and analytics, but hiring a full in-house data team — engineers, analysts, BI developers — is expensive, slow, and risky. That's exactly why the Data Team-as-a-Service model exists. At Vizlake Analytics, we embed as your dedicated external data team. Not a one-off project. Not a generic tool implementation. A partnership that delivers continuous
- 𝗪𝗵𝗶𝗰𝗵 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗻𝘂𝗺𝗯𝗲𝗿 𝗶𝘀 𝗿𝗶𝗴𝗵𝘁? — 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗶𝗻 𝗮 𝗳𝗶𝗻𝗮𝗻𝗰𝗲 𝗺𝗲𝗲𝘁𝗶𝗻𝗴. Sales reports €4.2M. Finance reports €3.9M. Both numbers come from the same ERP. Both are "correct". And the first 30 minutes of every monthly review are spent arguing about definitions instead of making decisions. This is not a Power BI problem. It is a KPI governance problem. Here is what we put in place when a Belgian mid-sized manufacturer came to us with exactly this issue:
- A manufacturing CFO told us: "We spend 12 days closing the quarter. Half of that is just arguing about whose numbers are right." This wasn't a small operation. 200+ employees, plants in two countries, SAP running the financials. On paper, they had everything they needed. In practice, every quarter-end looked the same: 🔸 Three departments pulling revenue numbers from three different exports — none of them matching. The controller spent her first week just reconciling figures before analysis could even begin. 🔸 Intercompany
Something wrong or missing? Send an update. Fixed within 24 hours.