Enterprises have no single, structured source of data that is consistent across formats and attributes
DT.Storage — a reference enterprise data warehouse
A digital twin software platform for storing and normalizing data: it brings together the data warehouse, master data and metadata, supports digitalization and runs on PostgreSQL and ClickHouse.
Request a proposalAbout the product
DT.Storage is the product that builds a centralized and strictly normalized data foundation for an enterprise, a region or an industry. It brings documents, factual data, reference information, master data and metadata together into a single structured system, and it eliminates duplication, fragmentation and inconsistency of information across departments and sources. DT.Storage solves the key problem of having no single, consistent source of information. Instead of a multitude of Excel files, folders and local systems, you get a full-fledged enterprise database in which all data is brought to a common format and structure. This approach enables the correct digitalization of management processes and creates a solid foundation for analytics, reporting and the construction of a digital twin.
Learn more
The product is designed to create a digital foundation for data management: it is the very base on which the analytical, balance and forecasting models run. The warehouse is deployed on modern database management systems, including PostgreSQL and ClickHouse, and supports integration with the company's other platform products — DT.ETL, DT.Balance, DT.Marts.
As part of the implementation of DT.Storage, the following are performed:
As part of the implementation of DT.Storage, the following are performed:
- Loading and consolidation of data from files, local and external information systems;
- Cleansing, verification and bringing data to agreed formats;
- Normalization of existing data and elimination of duplication;
- Building unified reference data, master data and codification systems;
- Establishing relationships between objects, indicators and time slices;
- Creating aggregated data representations for analytics and reporting.
Challenges
No data normalization, which makes it impossible to build aggregated analytical models
No centralized management of the data and metadata lifecycle
Duplication of information and inconsistency of reference data and indicators
Chaotic storage of documents and data across unconnected systems and folders
Get a personalized proposal and consultation
Describe your task and leave your contact details. We will get in touch, clarify the specifics and prepare an implementation proposal.
info@dtwin.city
+381 67 776 0421
Capabilities
Building a reference data warehouse
Bringing documents, factual data and reference data together into a single structured model that eliminates fragmentation and duplication of information.
Creating and maintaining master data and metadata in a normalized structure.
Building consistent master data, classifiers and metadata, kept up to date and free of contradictions.
A full data processing cycle
Loading, verification, transformation, updating and archiving of data with quality and consistency control at every stage of the lifecycle.
Development of highly normalized data models (up to 6NF)
This approach ensures the accuracy and consistency of queries, eliminates data duplication and preserves flexibility when building analytical models.
Codification and classification mechanisms
Implementation of unified codes for indicators, territories, objects, legal entities and time scenarios that make data comparable.
Methodology
The DT.Storage methodology is based on the principles of strict data normalization and standardization, which ensure data compatibility, integrity and transparency. This approach makes it possible to build a consistent and contradiction-free data structure suitable for integration with the platform's other products and with external systems. At the core of the methodology is a unified system of codification, attribution and normalization, thanks to which data is brought into a logical and comparable form, duplication is eliminated and the warehouse structure remains manageable. This approach ensures high data quality, data consistency and readiness for further use in analytical, balance and forecasting models. The methodology for building and populating the warehouse includes the following stages:
Defining subject areas, data structure and key entities
At this stage we build the enterprise data map: object types, indicators, documents and the relationships between them are defined. This creates the foundation for the future warehouse architecture.
Designing the database schema and the coding system
The data model is designed and codes are set for objects, indicators, territories and legal entities. A unified coding system ensures unambiguous interpretation and eliminates confusion between data from different departments.
Importing existing data from files, local systems and external sources
All available data is collected in one place. Excel files, documents, exports from accounting systems and industry sources are loaded — forming the initial dataset for cleansing and normalization.
Cleansing, verification and bringing data to a single format (normalization up to 6NF)
We visit the sites, compare the actual condition of the equipment against the documentation, refine specifications, record missing information and bring the data up to date.
Building and validating reference information, master data and metadata
Unified reference data, classifiers and metadata are created. At this stage the fragmentation of definitions is eliminated, and data is given consistent attributes and descriptions.
Creating relationships between entities and building aggregated representations for analytics and reporting
Logical relationships needed for analytics are established between objects. Aggregated tables and data marts are created for reports, forecasting models and dashboards.
Results
A reference enterprise data warehouse that includes documents, reference data, master data and metadata
A normalized data model that supports analytical and forecasting systems
Data maintenance and update regulations that ensure the reliability and completeness of information
An interface for integration with other systems, including the DT.ETL and DT.Marts modules
A databank of public and industry sources, available for use for analytical and research purposes
Get a tailored solution
Request a proposal
Describe your task and leave a contact — we will clarify the specifics and prepare a proposal for implementing DT.Storage at your enterprise. You can also reach us at info@dtwin.city.