AI Analytics & Reporting

AI Analytics & Reporting The two-day seminar for everyone who receives reports and has to make decisions: you learn to choose metrics, to automate evaluations and to recognise when a number only looks like insight. By the end you have reporting that gets read — and you know where AI helps in it and where it deceives.

AI Analytics & Reporting at a glance

• Format: 2 days on site + self-learning track
• Level: basic
• Participants: max. 16 people
• Language: German (English on request)
• Certificate: AEI certificate of attendance
• Investment: €199.00 plus VAT

Most companies have too many reports and too few decisions. Every week a new dashboard appears, nobody looks into it, and the important questions are still answered by feeling in the meeting. That is not a data problem but a selection problem.

AI makes this worse when used wrongly. A model that builds a forecast from existing numbers always delivers a result — even when the data carries nothing. Whoever cannot tell that apart gets certainty that is none.

This seminar works on both: reporting oriented towards decisions, and the question of where automation helps and where it creates false precision. You work with your own metrics, not with textbook examples.

What you take away

• A metric set that fits your decisions instead of a standard report.
• A framework for spotting spurious correlations and data errors.
• A dashboard concept reduced to one question per page.
• An assessment of which evaluations can be automated.
• A reporting rhythm that does not turn into busywork.

Your learning objectives

• You choose metrics by the decision they are meant to support.
• You recognise spurious correlations and check data quality before reporting.
• You build dashboards that answer a question instead of showing everything.
• You assess what AI can do in an analysis and what it cannot.
• You derive a measure from a report instead of forwarding it.

The levers at a glance

Every metric needs a decision. If no action can follow from a number, it does not belong in the report. That sounds strict and clears half the dashboard within an hour.

Two figures that rise together have nothing to do with each other. Correlation is cheap to find and expensive to mistake. Whoever claims causes needs a test or an experiment, not a chart.

Forecasts are probabilities with a spread. A point value without a range is an assertion. Whoever reads the spread decides better than someone who stares at the number.

A dashboard that shows everything is not used. One question per page, three numbers, one trend. Everything else is reference material and belongs elsewhere.

A model that predicts will predict, even on noise. Give it random numbers and it will find a pattern and report a confidence. The only defence is to ask what the baseline is and whether the forecast was ever tested against it.

Who is this seminar for?

For people who create or check reports, or decide on the basis of them.

• E-commerce and marketing managers with reporting responsibility.
• Controllers and analysts who want to automate evaluations.
• Managing directors who no longer want to just note reports.
• Team leads who have to explain their own numbers.

Basic knowledge of spreadsheets and metrics helps. The statistical terms are explained during the seminar; no mathematics is required.

The seminar is not suitable for data scientists building models. We work on selecting, checking and using evaluations.

Investment and dates

Participation costs €199.00 plus VAT, including the self-learning track, materials and certificate of attendance. There are six dates per year in Berlin, Hamburg, Cologne, Munich and live online, plus an in-house variant for teams. You will find all dates in the list on this page.

You do not need live access for the exercises. Anyone who brings their own report can reduce it to one question during the seminar — that is the most effective exercise of the day.

After the seminar

You keep the materials, a template for a metric set and a checklist with which you assess an existing report in twenty minutes. With that you can reorganise your reporting within a month.

The checklist asks three things per metric: which decision it supports, who reads it and what happens when it changes. Where an answer is missing, the metric is ballast.

Automation is worth it where the question repeats and the answer is unambiguous. Where the question changes every month, a person reading three numbers is faster than any pipeline.

Two weeks after the seminar you receive a short follow-up: the report you rebuilt and the reaction of its readers.

For teams the seminar is available as an in-house event with your own reports. The procedure stays the same, the examples come from your company.

Programme: 2 days

Day 1 – From data to statements

• Choosing metrics correctly
• Automated evaluation
• Spurious correlations
• Checking data quality
• Dashboards that get used
• From report to measure

Day 2 – Reporting in daily work

• What AI can do in analyses
• Reading and assessing forecasts
• Responsibilities and rhythm
• From gut feeling to evidence

Both days run from 09:00 to 16:15 with breaks in the morning, at midday and in the afternoon. Short self-checks sit between the blocks so you can test where you stand before moving on.

Each day has three parts: an introduction to the pattern, an exercise on a real report and a review in the group. At the end of day one your metric set is reduced; on day two you build one page from it.

The seminar includes a self-learning track. It contains the patterns from both days as short units with examples and one self-check each. You can work through it before the date to clarify terms, or afterwards to repeat what stuck and what did not.

Our approach

We work on your reports, not on a collection of best practices. At the start of each exercise you decide which decision you want to prepare and which number is needed for it; at the end stands a page somebody can read.

The exercises are short and repeat in variations. That is deliberate: an eye for reliable numbers does not come from watching but from repeatedly deleting metrics for which nobody can name a decision.

Every block ends with a sentence you could show a colleague: which decision, which number, and what you would do differently if it moved.

We work in pairs, in small groups and in plenary, with changing partners. Every participant gets feedback on their metric set, not just on their chart. Group level and participant count are deliberately limited: with 16 people no anonymous lecture is possible.