Foundation

Data and Information

By Redaktion techport.ai, IT-Beratung · Last updated on 21 August 2026

Every digital initiative ends in the same place: at the data. The dashboard needs a metric that is calculated the same way everywhere. Automation needs master data that is complete. The AI assistant needs documents that are current and findable. Where that foundation is missing, every initiative turns into a project with a manual attachment.

Data work is unspectacular and affects everything. It is the reason why two companies with the same software achieve completely different results.

Typical problems

Two reports on the same question produce different numbers, and the discussion turns to the calculation instead of the issue.
Article or customer master data is maintained in several systems without one being authoritative.
Reports are built from exports that someone assembles by hand. Every report is a small project.
Documents sit on drives, in mailboxes and in three cloud services. Nobody knows the retention periods.
AI tools get tested and give poor answers because they work on outdated files.

What we do in this field

We define with you which data is business critical, where it originates and who is responsible for it. We clean up master data and put rules in place that stop the situation repeating. We build reporting on definitions that apply across the company. We move documents into storage that meets retention and evidence obligations. And we create the data basis without which no AI initiative delivers reliable results.

Topics in this field

Reporting that holdsFrom spreadsheet exports to solid reporting: settle the questions before the tools, fix definitions, connect sources, automate reports and enable self-service sensibly.Creating the data basis for AIWhy AI initiatives fail on data and what helps: identify knowledge sources, remove duplicates and outdated versions, mirror permissions, keep content current and make answers verifiable.Data strategy and ownershipWhich data is business critical, where it originates, who owns it and which rules apply. The basis for reporting, automation and AI, without a large governance programme.Documents and archivingStore documents so they can be found and stand up to audits: define the filing structure, implement retention periods, write the process documentation, order permissions and enable search.Cleansing master data and keeping it cleanHow master data becomes and stays clean: measure quality, cleanse where it matters, regulate creation and maintenance, prevent duplicates and keep quality metrics in view.

Häufige Fragen

Should we clean the data first or change the system first?

The data, but not as a year long pre-project. Cleansing runs partly in parallel with the selection, because the target structure depends on the new system. What has to happen first in any case is the analysis: how many records really exist, how many duplicates, how many mandatory fields are empty. Those numbers regularly change the project plan.

Do we need a data warehouse?

In the mid-market, often not immediately. The first step is a shared definition of the twenty most important metrics and one system that counts as the source. A separate database for analysis pays off once you regularly combine data from several systems or need historical states that the source systems overwrite.

Let us talk about your situation

In a thirty minute first call we work out where your biggest lever sits and whether we are the right people for it.

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