{"id":155468,"date":"2026-04-28T08:00:00","date_gmt":"2026-04-28T06:00:00","guid":{"rendered":"https:\/\/www.immo-invest.ch\/?p=155468"},"modified":"2026-04-27T16:50:22","modified_gmt":"2026-04-27T14:50:22","slug":"mehr-ki-loest-keine-datenprobleme","status":"publish","type":"post","link":"https:\/\/www.immo-invest.ch\/en\/mehr-ki-loest-keine-datenprobleme\/","title":{"rendered":"More AI does not solve data problems"},"content":{"rendered":"\n\n

The mistake begins with the investment<\/strong>
It’s a familiar pattern: a company recognizes the potential of artificial intelligence, looks at solutions, chooses a tool – and gets started. The expectation is that the new technology will somehow solve existing data problems. The reality: It doesn’t. It makes them more visible.<\/p>\n\n\n\n\n\n

This is no coincidence. It is the consequence of a wrong sequence.<\/p>\n\n\n\n\n\n

Data is collected – but not made usable<\/strong>
Data is available in most real estate companies. Property data, tenant data, operating figures, maintenance histories – they exist. The problem is not its absence, but its condition. They are scattered across systems, inconsistently maintained, inconsistently defined or simply cannot be linked to one another. There are sometimes three different versions of the same key figure – in three different systems.<\/p>\n\n\n\n\n\n

Anyone who sets up an AI model under these conditions will not get any answers. What you get is output that reinforces existing uncertainties – automatically and at high speed. AI recognizes patterns in data. If the data is inconsistent, the model learns from the inconsistency. If it is incomplete, it operates on an incomplete basis.<\/p>\n\n\n\n\n\n

A new layer of complexity<\/strong>
What is created in practice is not a gain in efficiency. It is a new layer of complexity: AI outputs that nobody trusts. Departments that manually check results. Projects that come to a standstill. A lot of effort, little effect, growing frustration.<\/p>\n\n\n\n\n\n

The fatal thing is that many companies react to this with the next tool upgrade. The cycle starts all over again.<\/p>\n\n\n\n\n\n

A data hub is not a tool – it is a structure<\/strong>
The solution does not lie in better models. It lies in a structural decision: the creation of a common, harmonized database. A data hub is not another system that is added to the existing IT landscape. It is the opposite – it replaces fragmentation with central availability. It integrates distributed data sources, breaks down silos and inconsistencies and creates the basis for scalable AI applications and automated reporting.<\/p>\n\n\n\n\n\n

The decisive factor is not where the data is stored. What matters is how it can be used: uniformly defined, quality-assured, accessible for different use cases. Only on this basis can AI deliver what it promises.<\/p>\n\n\n\n\n\n

Data quality is not preliminary work – it is an ongoing task<\/strong>
Even with a data hub, a central challenge remains: Data quality is not a one-off cleansing project before go-live. It is a continuous process. Anyone who sees data quality as a preliminary project will realize after the launch that the real problem is only just beginning.<\/p>\n\n\n\n\n\n

The database is supplemented by a data catalog: It transparently documents which data exists, where it comes from and how reliable it is. It creates a common language that connects specialist departments and technology – and gives control back to the organization.<\/p>\n\n\n\n\n\n

In the webinar: From the database to scalable AI<\/strong>
In our free webinar “The optimal AI architecture: How data hub, data quality and data catalog make the difference”, we show how real estate companies can tackle this transformation in concrete terms – from data architecture and quality assurance to the productive use of AI. With practical insights, concrete solutions and time for your questions.<\/p>\n\n\n\n\n\n

Register now for free<\/a><\/em><\/p>\n\n","protected":false},"excerpt":{"rendered":"

Real estate companies invest in AI tools – and reap complexity instead of clarity. The reason rarely lies in the technology. It lies in the database, which is not approached correctly.<\/p>\n","protected":false},"author":2,"featured_media":155469,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","_surecart_dashboard_logo_width":"180px","_surecart_dashboard_show_logo":true,"_surecart_dashboard_navigation_orders":true,"_surecart_dashboard_navigation_invoices":true,"_surecart_dashboard_navigation_subscriptions":true,"_surecart_dashboard_navigation_downloads":true,"_surecart_dashboard_navigation_billing":true,"_surecart_dashboard_navigation_account":true,"inline_featured_image":false,"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[22697,56005,12486,56020,12483],"tags":[58637,58638,12525,12526,55794,12528],"class_list":["post-155468","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-company","category-international","category-promotion","category-property-management","category-proptech-en","tag-data-hub","tag-datenqualitaet","tag-immoinvest-en","tag-immonews-en","tag-kuenstliche-intelligenz-3","tag-news-en","pmpro-has-access"],"acf":{"bild_mit_ki_erstellt_worden":"","lead":"Real estate companies invest in AI tools - and reap complexity instead of clarity. The reason rarely lies in the technology. It lies in the database, which is not approached correctly.","autor":"Quelle pom+","ort":"","bildunterschrift":"","zusammenfassung":"","bild_im_grid":false,"gallerie":"","newsim":""},"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t