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
- Q3 is closing — is your finance team still chasing spreadsheets? September means one thing for finance leaders: the Q3 close is here. And for too many teams, that means weeks of manual data wrangling before a single insight reaches the boardroom. We see it constantly. A CFO calls us because: 🔸 Their team pulls data from 4-5 systems manually — every single quarter 🔸 Reports take 10+ days to compile, and leadership still questions the numbers 🔸 One key person owns the "master spreadsheet," and if they're on holiday, the close
- Most finance teams don't know how healthy their data actually is — until a board deck falls apart. You've got dashboards. You've got reports. But when the CFO asks "why do these two numbers disagree?", nobody has a fast answer. That's not a Power BI problem — it's a data maturity problem, and most companies never diagnose it until it costs them credibility in a meeting that matters. A data maturity assessment is a structured 1-2 week engagement where we map your data landscape across five dimensions before touching a single
- A flat table isn't a data model — it's a bottleneck waiting to happen. We still see finance teams building Power BI reports on single wide tables pulled straight from ERP exports. It works... until it doesn't. The problem: 🔸 Flat tables force DAX to do the work joins should be doing — every measure recalculates relationships on the fly 🔸 Refresh times balloon as row counts grow, because there's no separation between facts and dimensions 🔸 One "add a column" request breaks five reports, because everything lives in one giant
- A manufacturing CFO in the Netherlands came to us with a familiar problem: three plants, three different Excel reporting formats, and a finance team spending the first week of every month just reconciling numbers before anyone could even look at performance. Sound familiar? Most mid-sized manufacturers hit this wall once they scale past a single site — and it's rarely a headcount problem. It's a data foundation problem. Here's what we did: 🔸 Data Assessment: mapped every source system across the three plants (ERP, shift logs,
- Your dashboards aren't the problem. Your data foundation is. We keep meeting finance teams who've invested in Power BI Premium, built a dozen polished dashboards, and still don't fully trust the numbers on them. Nine times out of ten, the issue isn't the visualization layer — it's what's (or isn't) happening underneath it. This is where medallion architecture earns its keep. A simple, three-layer approach to structuring data as it moves through your pipeline: 🔸 Bronze: raw data landed exactly as it arrives from source systems —
- Most finance teams are flying blind on their own data — and don't know it. We often meet CFOs who assume their reporting is fine, until we run a data maturity assessment and find the cracks: three versions of the same revenue number, dashboards nobody trusts, and an FP&A team spending more time reconciling spreadsheets than analyzing them. A BI audit isn't about criticism, it's about visibility. Before we touch a single dashboard, we assess: ✅ Data sources — where does the data actually live, and how many systems does it pass
- Your KPI dashboard is lying to you — and it's not the data's fault. We see this constantly with finance teams: three departments, three different numbers for "revenue growth." Everyone is technically right. Everyone is also arguing over a metric that was never actually defined. The root cause isn't bad data. It's the absence of KPI governance. 🔸 Finance calculates gross margin one way, Sales calculates it another — same name, different formula, different result. 🔸 A metric gets redefined quietly in a spreadsheet, and six months
- A manufacturing CFO told us their month-end close took 12 days. Here's what changed. Last year, a mid-sized manufacturer in the Netherlands came to us with a familiar problem: finance was spending more time reconciling spreadsheets than analyzing the business. Data lived in five different systems — ERP, a legacy inventory tool, three regional Excel files — and nobody fully trusted the numbers by the time they reached the board. Sound familiar? Here's what we did: ✅ Data Assessment — mapped every data source and flagged
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