Data Warehousing

Data warehousing brings together data from multiple systems into one trusted, consistent and well-governed source. It turns fragmented information into a unified foundation for reporting, analytics and decision-making.

A modern data warehouse ensures that data is accurate, up to date and structured for fast insights, whether the need is operational dashboards, strategic reporting or AI-driven analysis.

DSharp helps organizations design and automate data warehouses using metadata-driven models, ensuring clarity, governance and seamless scalability across complex environments.

Fragmented data is a leadership challenge – Here’s how to turn data into a business driver

12.2.2026

Fragmented data is a leadership challenge: when key concepts mean different things across systems, trust erodes and decisions slow down. Metsähallitus built a shared conceptual model (1,400+ concepts) with DSharp to make metrics consistent and reusable across teams. Next, they see potential in DSharp Scout to guide users to reliable, traceable answers.

Tiedon sirpaleisuus on johtamisen haaste – Näin datasta tulee liiketoiminnan ajuri

12.2.2026

Monessa organisaatiossa dataa on paljon, mutta numeroihin ei luoteta, kun käsitteet elävät eri järjestelmissä eri tavoin. Metsähallitus ratkaisee haastetta DSharpin käsitemallinnukseen pohjautuvalla data-alustalla, joka tekee tiedosta yhtenäistä ja päätöksentekoa tukevaa. Yhteiset käsitteet ja mittarit nopeuttavat arjen työtä, vähentävät tulkintaa ja parantavat raportoinnin laatua.

The City of Turku Expanded Its Data Warehouse with Library Data

3.6.2025

“We wanted to take control of our data so that we can modify it ourselves and decide what information we want to use and combine in reporting. In the future, the data stored in our own data warehouses can be integrated with other city datasets, such as financial data. This allows us to compile valuable insights to support decision-makers.”

Making Data Management Easier with Automation

27.5.2025

Comprehensive reporting solutions are always technically complex. Among various construction methods, Data Vault, realistically involving thousands of tables, is often perceived as laborious and expensive, simply because traditionally implemented, it is. However, the requirement specifications for reporting solutions often list as mandatory exactly those aspects that Data Vault addresses. This makes Data Vault a reasonable and functional solution model instead of inventing a completely new implementation approach.