At a glance
- Integrity rules that prevent duplicate, faulty or contradictory entries
- Logs allow transparent and traceable data changes
- Indispensable for BI & controlling – error-free data as the key to precise analyses
What would you like to know about Data integrity?
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Data integrity definition
Data integrity refers to the correctness, consistency, and reliability of data throughout its entire lifecycle. It ensures that information remains unchanged, complete, and protected against unauthorized access – from capture through storage to analysis.
Data with integrity is therefore essential for companies to make well-founded decisions based on precise information and to meet regulatory requirements such as GDPR. In areas like business intelligence, IT security, and compliance, data integrity thus plays a central role, since erroneous or manipulated data can have serious economic and legal consequences.
„Since we started working with DeltaMaster, our reports have been based on validated and consistent data models. We trust the numbers without having to manually cross-check them every time.“Head of Controlling
Industrial company
How do you ensure data integrity?
Ensuring data integrity requires a consistent implementation of technical and organizational measures to protect data against errors, loss or manipulation:
- Audit trails and monitoring: Logging all accesses and data changes enables traceability and helps detect security incidents.
- Automatic backups and recovery strategies: Regular data backups protect against loss due to hardware failures, system outages or cyberattacks.
- Unique keys and constraints: Primary and foreign keys as well as rules such as “NOT NULL” or “UNIQUE” prevent faulty or duplicate entries.
- Employee training: Raising awareness of the importance of data integrity and training in the secure handling of data.
- Use of high-security data centers: Storing critical data in certified data centers with strict security measures.
- Validation and cleansing: Checking incoming data for correctness and consistency, as well as regularly removing redundant or faulty records.
- Access controls and authentication: Implementing the principle of least privilege and protecting physical server access with biometric scanners or access cards.
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What are the four types of logical data integrity?
Data integrity encompasses both physical and logical aspects: While physical integrity ensures protection against external influences such as hardware failures or cyberattacks, logical integrity prevents unauthorized or faulty data changes through validation mechanisms and access controls. With logical integrity, four main types are distinguished:
| Type | Description | Example |
|---|---|---|
| Entity integrity | Each data unit is uniquely identifiable – through primary keys that prevent duplicate or missing values | No record without a unique ID |
| Referential integrity | Consistency between linked records – foreign key rules prevent orphaned entries | Order line item references a valid customer |
| Domain integrity | Permissible values and formats for data fields are defined through data types, value ranges, or input restrictions | Invoice amount only in valid currency codes such as "EUR" or "USD" |
| User-defined integrity | Individual rules for specific business requirements that go beyond standard rules | An item can only be marked as "shipped" if a valid shipping number is present |
- Entity integrity ensures that each data unit is uniquely identifiable. In relational databases, this is often achieved through primary keys that prevent duplicate or missing values.
- Referential integrity ensures consistency between linked records. Foreign key rules ensure that references to other tables remain valid and that no orphaned entries or faulty links arise.
- Domain integrity defines permissible values and formats for data fields. Through rules such as data types, value ranges, or input restrictions, the correctness and consistency of the stored data is ensured. For example, in an ERP system it is required that the currency specification of an invoice amount may only be made in predefined currency codes such as "EUR", "USD", or "GBP", in order to prevent incorrect postings caused by invalid or inconsistent entries.
- User-defined integrity includes special rules that are defined individually for a system or an organization. These can encompass business-specific requirements that go beyond standardized integrity rules. For example, in a warehouse management system it may be stipulated that an item may only be marked as "shipped" if a goods issue posting with a valid shipping number has been made beforehand.
What is the difference between data integrity, authenticity, and data security?
Data integrity, authenticity, and data security are essential concepts in the management of corporate data, which, however, pursue different goals and focal points:
| Concept | Goal | Typical measures |
|---|---|---|
| Data integrity | Data remains correct, complete, and unchanged | Validation mechanisms, constraints, and access controls prevent errors, manipulation, or corruption. |
| Authenticity | The genuineness of data and its source is confirmed | Digital signatures, certificates, and multi-factor authentication ensure that information originates from a trustworthy entity and has not been forged. |
| Data security | Protection against unauthorized access, loss, or cyberattacks | Encryption, firewalls, and authentication procedures protect sensitive information from threats. |
Thus, while data integrity protects the consistency and correctness of the data, data authenticity guarantees that it originates from a legitimate source. Data security focuses on protecting the data from external and internal threats. All three concepts are indispensable for keeping corporate data reliable, trustworthy, and secure.
What does a company need for data integrity?
Data integrity requires an interplay of technical rules, organizational processes, and trained employees.
Use cases: Where is data integrity particularly relevant in companies?
Data integrity is relevant in nearly all data-driven areas of a company. The following examples show typical fields of application.
Where would you like to strengthen data integrity?
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Data integrity and Bissantz
Data integrity is not an abstract IT requirement – it determines whether controlling reports are trustworthy and whether decisions are based on a solid foundation.
DeltaMaster from Bissantz ensures that data is not only analyzed but interpreted on the basis of valid and consistent information. The software is based on a consistent key figure logic and accesses verified data models in the data warehouse that systematically avoid faulty, duplicate or contradictory entries.
Through standardized ETL processes, clearly defined access rights, automatic variance analyses and consistent presentation, DeltaMaster supports the implementation of high data integrity requirements – even in regulated or decision-critical areas. Bissantz also supports companies with the functional design: Which integrity rules must be anchored in the data model? How are source systems connected so that inconsistencies do not arise in the first place? These questions are answered together with the customer – as part of a consistent BI and data strategy.
| Function | Benefit |
|---|---|
| Verified data models in the data warehouse | systematically avoid erroneous, duplicate, or contradictory entries |
| Standardized ETL processes | ensure consistent and error-free data transfer from source systems |
| Clearly defined access rights | limit unauthorized changes to decision-critical data |
| Automatic variance analyses | uncover inconsistencies and conspicuous values early on |
| Consistent KPI logic | ensures a consistent and valid basis for interpretation |
„The standardized ETL processes and clearly defined access rights have noticeably improved our data quality. Inconsistencies from the source systems are now caught early and no longer end up in the analyses.“Head of IT
Food producer
Frequently asked questions
What is data integrity, simply explained?
Data integrity means: the data is right – it is complete, correct and free of contradictions. Like an accountant who ensures that every figure in the balance sheet is correct and that nothing is missing or duplicated. Without data integrity, analyses and reports cannot be trusted – no matter how good the BI tool is.
What is the difference between physical and logical data integrity?
Physical integrity protects data from external influences – hardware failures, cyberattacks, power outages. Logical integrity prevents content errors – duplicate entries, invalid values, contradictory links. Both are necessary: physical integrity secures the existence of the data, logical integrity its correctness.
Why is data integrity particularly important in controlling?
In controlling, budgeting, investment and management decisions are based on key figures. If these are faulty or inconsistent, they lead to false conclusions – with potentially significant economic consequences. Data integrity is therefore not an IT question but a question of decision quality.
How are data integrity and the GDPR connected?
Among other things, the GDPR requires that personal data be factually correct and up to date. Data integrity is therefore a legal requirement, not just a technical one. Companies must ensure that their data is managed correctly, completely and traceably.
How does Bissantz support ensuring data integrity?
DeltaMaster accesses validated data models in the data warehouse, avoids inconsistencies at the source through standardized ETL processes and enforces clear access rights. In addition, Bissantz supports the functional design of the data model – so that integrity rules are properly anchored from the outset.
Summary
Data integrity is the silent prerequisite for everything that works in controlling and business intelligence: only when data is correct, complete and consistent do analyses, reports and key figures provide a reliable basis for decisions. With DeltaMaster and its consulting expertise, Bissantz ensures that data integrity is not only technically safeguarded but also anchored from the ground up in data models, ETL processes and key figure logic.
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