Why is data harmonization and quality crucial?
In industry, data is the fuel for production efficiency – from short-term operational decisions to predictive AI models and reporting. The challenge is managing data from dispersed sources: devices, OT systems, and business applications, which translates into problems with consistency and verification.
Smart RDM provides complete control over data – from acquisition, standardization, codification, and storage to reporting, complex analysis, and visualization of results in the form of configurable dashboards.
By combining a Central Data Repository (CDR), a time series layer, and a hierarchical object model (devices, machines, branches) in a single environment, the platform guarantees broad access to data across a variety of operational and business processes within the organization and at the same time it eliminates data silos.
Central Data Repository (CDR) – a single, consistent data environment for the entire organization
The CDR is a place where all data, e.g., from SCADA, PLC, AVEVA PI, meters, IoT sensors, stock exchanges, or weather forecasts, is collected, organized, and standardized. It is resistant to changes in source systems and forms the foundation for all analyses, KPIs, and AI projects.
CDR provides:
- Data consistency – common nomenclature, structures, and metadata
- Versioning – every change in the data is tracked and reversible
- Universal access – data available to users at various levels within the organization
- Resilience to changes in source systems – CDR protects the continuity of analyses
- Security – access restricted by user roles
- Scalability – virtually unlimited performance for business analytics
This environment effectively eliminates data silos and information chaos.
Data validation, standardization, and quality control
Smart RDM monitors data quality in real time. The system automatically detects anomalies, gaps, and deviations, and ensures the correctness of entities, tags, and structures.
- Data validation
Detection of incorrect readings, inconsistent deviations, and erroneous ranges. - Data normalization
Consistent naming, hierarchies, units, and metadata across the entire enterprise. - Identification of data incompleteness
Checking whether data arrives regularly and completely, according to the expected schedule (e.g., every minute, every hour). - Data ready for analysis and reporting
Change history and data lineage available to quality departments, IT, and auditors.
The platform ensures that data flows:
On time, in full, without interruptions, and with complete content.
Data prepared for analysis and AI
(AI-ready data)
Data needs to be prepared before it can be used in predictive models, anomaly detection, or advanced analytics. Smart RDM performs most of these operations automatically, so analytics teams don’t have to start with data cleaning and validation and can focus on deploying AI models in a controlled, production-ready manner.
Intelligent support
The platform harmonizes data (i.e., it unifies and standardizes data from different sources to make it consistent and compatible), fills in missing data, marks operational events (failures, downtime, alarms), integrates process data with business data, and prepares governed datasets ready for training, deployment, and long-term operation of AI models. Smart RDM embeds MLOps principles directly into the data layer by continuously monitoring data quality, signal stability, and operational context, enabling early detection of data drift and context changes. This provides a foundation for model versioning, traceable training datasets, controlled retraining, and safe rollout of new model versions without disrupting operations. As a result, predictive analytics can be scaled across assets and sites while remaining reliable, auditable, and aligned with real production conditions over the entire AI lifecycle.
Why becoming a Smart RDM Partner is worth it?
Smart RDM is designed to grow your business, not just add another product to your portfolio.
AI-ready data – data prepared for algorithms
Verification of data completeness and supplementation
Aggregation and harmonization
Marking of operational statuses (failures, downtime, alarms)
The ability to codify and hierarchize
Automatic and manual data validation and cleaning
In many companies, the same indicators (KPIs) are calculated differently in different systems, each with different device codifications, which leads to problems with the correct interpretation of information and, for example, incorrect reporting or analysis.
Thanks to CDR and central standardization, Smart RDM eliminates silos and ensures:
One organization = One coding standard
All departments in the organization, from operators to controlling, should use the same data and a single, common set of definitions and algorithms. This approach reduces the risk of errors and ensures full comparability of data across lines, plants, and regions.
Operational data is increasingly subject to regulations – from NIS2 and ISO 27001 to environmental reporting and ESG/CSRD.
Smart RDM ensures transparency by documenting every value change, recording data provenance, and providing controlled access based on SSO/IAM.
This approach facilitates audit and report preparation, reduces errors, and minimizes the risk of data breaches.
High data quality in Smart RDM means:
Faster decisions and fewer errors
Reliable KPIs and reporting
Reduced time spent on data cleaning
Reliable AI performance
Effective data quality management across the entire organization
Correct operation of optimization and prediction algorithms
Smart RDM – a single source of truth for operational,
manual, and analytical data
Smart RDM is an operational decision-support system for industrial organizations.
It connects operational data with business objectives, enabling management to control performance,
costs, energy usage, and risk – based on real-time facts rather than delayed reports.
What are Data Forms in Smart RDM?
Forms in Smart RDM are built-in, interactive views for working with operational data. They are used to enter, edit, and approve information that does not always enter the system automatically – both information entered manually by users (e.g., operator entries, manual readings) and information from other IT/OT systems. This makes Smart RDM not only a platform for viewing data from systems and automation, but also a place where data can be completed, validated, and organized, with full control over its quality.
What are the forms in Smart RDM used for?
They are most often used for:
- manually entering measurement data, e.g., readings from devices, meter readings, in the absence of integration possibilities,
- supplementing technological parameters that are not recorded automatically,
- entering report data (e.g., environmental, operational),
- handling operational events, e.g., operator entries, failure descriptions,
- data acceptance and validation processes,
- supplementing data for calculation models that require declarative values.
How do they work?
- they have a defined schema of fields, data types, and validation rules,
- they support versioning and a full edit history,
- they can be connected to business logic, alerts, and data pipelines,
- They automate the creation of visualizations and reports.
Build your data structure from scratch
From the administrator level, you build a hierarchical model of objects –
from plants and production lines, through installations and devices, to individual measuring points.
From the administrator level, you can create a complete hierarchy of industrial objects:
- plants and locations,
- lines and departments,
- installations and devices,
- individual measuring points and sensors.
For each level, you can define:
- sets of attributes,
- units and dictionaries,
- required fields,
- related forms,
- object and measurement codification schemes that ensure consistent naming and unambiguous identification throughout the organization.
As a result, Smart RDM maps the actual layout of the factory, creating a logical and standardized data model that becomes a common language between departments and is the basis for building a digital twin and advanced analytics.
Enter, validate, and complete data – in one place
Smart RDM forms allow you to enter and correct data directly in the platform – without
exports, intermediate files, or manual transformations.
The system automatically checks the correctness of the information entered:
- checks ranges and acceptable values,
- enforces correct formats,
- validates mandatory fields,
- maintains the consistency of data units and types.
Each change creates a new version of the record with a full history and audit trail
(i.e., a chronological record of all actions, decisions, and operations performed in the system).
This makes manual data an equivalent, systemic source of information that immediately
feeds into the central data repository (CDR).
One place instead of many spreadsheets and tables
No more Excel files in maintenance, quality, and production
Smart RDM eliminates the need to work in scattered Excel spreadsheets. All operational data – both automatic and manual – is collected in a single, consistent, and secure data environment. The platform makes it available to operators, maintenance, production, and quality teams, as well as managers.
The system ensures data quality control, full versioning, and logging of every change, which eliminates common errors resulting from working in spreadsheets.
In Smart RDM, data is stored:
- without duplicates,
- without the risk of uncontrolled overwriting through the data acceptance process,
- with a complete history of changes,
- with data permissions consistent with user roles.
It is a single data environment for the entire organization and a single source of truth.
What does this give the organization?
- Manual data becomes system data – it goes to a single platform, is versioned, and automatically linked to process data.
- The risk of entering data that reduces the reliability of KPIs is significantly reduced.
- The time needed to prepare analyses is reduced many times over, as the data is immediately ready for use.
- Transparency of information sources – it is possible to clearly identify who entered the data and when.
- Data quality increases measurably, and the entire organization works on a single, consistent resource.
Contact us
Do you have scattered operational data and lack a single source of truth?
Or maybe you need a tool that will organize manually entered data and technical data into a consistent structure? With Smart RDM, we help companies build stable data environments – from integration to quality management. If you want to see how we can support your organization, please contact us.
Gabriela Gic-Grusza
Unit & Product Manager Smart RDM