Team

Meet us.

Technical guy Vojtěch Šíma

Vojtěch Šíma

"What I'd highlight about myself is a no-bullshit approach to problems. If something doesn't make sense or doesn't deliver real value, we steer clear of it. Power BI is my passion, and I love creating content about it, speaking at conferences and community events, and sharing practical experience that helps people work with data simply and effectively."

Microsoft MVP
Business guy David Polleč

David Polleč

"At Daatlers I own the business side. I have experience leading innovation and process teams, building data products and driving changes that actually make sense. I believe in open communication, accountability and an environment where people know where we're heading and why."

"We connect data that doesn't talk to each other."

We turn scattered data from your systems into tailored reports, thanks to which you have a clear view of operations.

Data Visualization · Expertise

Data Visualization –
POWER BI

The report is a tool. The outcome is that you know.

Every report is born from a specific need. For instance, knowing that a project is running three percent below average margin. Or that material for a new order is missing from the warehouse. And knowing it before the problems surface on their own.

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Monthly Overview Updated: today 06:00
Sources: CRM ✓ Invoicing ✓ ERP ✓
Revenue
12.4M
Margin
31%
Orders
87
Revenue by Month
Jan Feb Mar Apr May Jun
In Stock
1,248 pcs
Avg. Due
14 days
18:09Margin dropped by 3%
Data Integration · Data Engineering

Data Integration –
MICROSOFT FABRIC

This is the moment when it makes sense to build a data platform.

There comes a moment when a simple data structure is no longer enough. The number of sources and data volume grows, requests for more and more metrics keep coming, but the foundation, the data platform, isn't ready. How do we recognize it? Maintenance starts taking more time than actual work with data, changes are very demanding and often break the entire report.

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Lakehouse Tables
sales_pipeline.ipynb Polars 1.x Run All
[1] Python 1.8s
# Load 3 sources into one model import polars as pl sales = pl.scan_delta("sales_data") outlook = pl.scan_delta("outlook_3m") plan = pl.scan_delta("year_plan") lf = sales.join(outlook, on="region")\ .join(plan, on="region")
[2] Python 0.3s
lf.select("region","sales","outlook","plan","status").collect()
Output
regionsalesoutlookplanstatus
CZ-Praha1 2401 3801 500✓ OK
CZ-Brno8709101 000✓ OK
SK-BA620580700⚠ Watch
Ingest
Notebook
Lakehouse
Report
Advisory · Management

Advisory and Management

Your colleagues came to you wanting to use Power BI or Fabric, and you want to make sure it won't be just another costly technology that goes unused. Or you already have the platform but aren't sure you're getting what you should from it.

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Scan & Super Audit 4 / 6
Workspace & license scan
Capacities, access, cost allocation
Governance & security check
RLS, sharing, deployment pipelines
Super Audit: DAX & data model
Measures, relationships, cardinality, star schema
Super Audit: Power Query / M code
Query folding, unnecessary steps, refresh time
Optimization proposal & roadmap
Concrete plan, priorities, quick wins
Implementation & benchmark
Joint execution, before/after measurement
66 %
Recommendation Rewrite 4 CALCULATE measures, enable query folding on 2 sources
Training · Community

Training and community

Even in the age of AI, education is more important than before. We don't offer boxed courses. We teach people, and after the training, we stay available to help implement the new material directly in your environment.

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Training and community
Power BI DAX Power Query Fabric Data Journey