Your Challenge: Complexity

When asked about the biggest challenges in your company, you might not immediately say “complexity.” Instead, you probably recognize several of the following symptoms:

• Unexpected outcomes: Established processes suddenly produce surprising results or side effects under otherwise stable conditions.
• Knowledge silos: Expertise exists within the company but fails to reach all the places where it is needed, leading to duplicated work and declining quality.
• Documentation graveyards: Manuals, reports, and meeting notes are written and archived properly — but rarely read or used.
• Overcaution: Fear of potential mistakes leads to promising ideas being dismissed as too risky without proper expert evaluation.
• Loss of efficiency: Processes slow down, waiting times increase, and redundant loops become more frequent.
• Wasted effort: A growing share of work contributes little or nothing of real value.
• Powerlessness: Employees and management alike feel increasingly overwhelmed and unable to influence outcomes effectively.

If you are a manager in a corporation or mid-sized company and find yourself nodding at several of these points, it might be time for us to talk.

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What we do: Services

We simplify complex processes, make them "digital-ready" and, if requested, implement them in software. We build a technical view of product and process landscapes, apply data analytics to create transparency, identify optimisation points, prototype solutions and support complete implementation, including managing external IT vendors.

• Vision & Objectives
• Effort & Benefit
• Transparency & Control
• Scenarios & Simulation
• Experimentation & Iterative feedback
• Change management & Growth

Use case: Data analytics is complexity reduction

An exemplary data analysis pipeline

The sketch below shows a simplified flow for a typical data analysis project. On the left are your source systems (often optimised for transactional tasks, not analysis). On the right is the dashboard you want.

Source systems		Local database		Analytic datasets		Dashboard
TB	hours	GB	seconds	MB	instantly	KB

Large databases are rarely fast for ad-hoc analysis: terabytes of data are unnecessary for most use cases. Effective dashboards are usually based on sensible data slices (selections) and aggregations. The trick is to precompute these slices for many use cases — e.g. overnight — and cache them in local databases or in-memory structures. From there, analyses can be produced in seconds (for example using Python and pandas) and fed into visual dashboards.

Example solutions

Open-source visualisations for d3

We publish several example visualisations as open source code. These include interactive map visualisations (by postal region), a treemap for hierarchical data, and filterable bar and pie charts. Some of these visualisations are used at the Statistikportal of German Health Data Lab.

The examples provide JavaScript, CSS and a minimal page structure to help you implement similar visualisations on your data.

Variant trees in self-service

Tabular data often contains hierarchical relationships. A variant tree reveals hidden dependencies and helps understanding. We published a lightweight webapp that runs entirely in the browser: upload a CSV locally and explore tree views, aggregate or expand individual columns and download the resulting SVG.

From Vision to Implementation in Three Steps

Our structured framework provides clear guidance on how we turn complex challenges into practical solutions.

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