Data Modeling

Data modeling is the practice of defining and organizing information so it can be understood, used and governed consistently across an organization. It creates a shared language between business, data and technology teams, making complex information clear, structured and reusable.

A good data model explains what your data means, how it relates, and how it should be used in reporting, analytics, automation and AI. It improves data quality, speeds up development and ensures that decisions are based on trusted, well-defined information.

Data modeling is essential for:

  • Public sector organizations needing clarity, interoperability and governance

  • Companies wanting scalable analytics and high-quality reporting

  • Data and architecture teams managing complex environments

  • Any organization preparing for automation or AI

At DSharp, we help teams build strong semantic foundations through intuitive modeling, consistent definitions and metadata-driven automation, ensuring data is meaningful, reliable and ready for intelligent use.

On this page a selection od data modeling articles.

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

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

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.
DSharp and Varha in collaboration

Quarterly Execution Brought Agility to Varha’s Project Management

Varha, the wellbeing services county of Southwest Finland, set out to build a unified data foundation and develop its data leadership capabilities. Agile collaboration was essential in the project. Planning and executing the work in quarterly cycles proved effective for advancing this large-scale initiative. DSharp's DSharp Studio was the chosen software for the automation of the data warehouse.
DSharp customer stories Turku

The City of Turku Expanded Its Data Warehouse with Library Data

“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.”
DSharp Studio

Making Data Management Easier with Automation

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.
dsharp-blog_conceptual-model-shows-information-needs

Data Warehouse concepts and data models

Each of us needs information in our work. Information is acquired,…