Data integrity

Data integrity: the key to successful automation

Christopher Klee

Data is considered the "gold of the digital age". It is used as a currency, resource or bargaining chip and is collected almost everywhere: In both the private and business spheres. It is also the basis for large language models and AI solutions in companies. It is no coincidence that there is talk of the datafication of society and "big data" as a key term in current developments. However, the true value of data is determined by two factors in particular: Accurate analysis and data integrity.

For more and more companies, data is the basis for management processes and decisions. Data collected from customers, partners and employees is used to align the company's strategy, optimize processes, implement solutions and make small and large decisions. The basis for ensuring that these decisions are well-founded and therefore correct is the integrity of the underlying data. But what does data integrity mean and how can it be ensured in companies?

What is data integrity?

Definition - What is data integrity?

Data integrity is the decisive quality criterion for data. It describes the integrity of the data and includes the correctness, completeness and consistency of the data as well as its security. Integrity defines the trustworthiness of the database multidimensionally and is therefore more meaningful than the mere determination of data quality. It also relates to the fulfillment of regulatory requirements, such as the GDPR or CCPA. To ensure data integrity, it is essential that data is not processed, deleted or changed without authorization. There are various forms of integrity to consider:

Physical data integrity

Physical integrity represents the haptic integrity of the database and therefore the completeness and correctness of the stored elements - whether in the cloud or as an on-premise solution. Threats to physical data integrity can include natural disasters, power outages or hacker attacks that disrupt the database's functions. User errors, memory erosion and numerous other problems can also pose risks. Physical integrity can be compromised by georedundant storageThe system is guaranteed by a comprehensive security concept, backup copies of the entire database and a comprehensive security concept.

Logical data integrity

Logical integrity ensures that data remains unchanged during use in a relational database. Logical data integrity includes entity integrity and referential integrity. Entity integrity represents the definition of unique values. This ensures that all values are only present once in the database, thus avoiding distortions. Referential integrity describes the state in which data is recorded, stored and used in a uniform manner, thus ensuring the comparability of the data.

Data integrity is the decisive quality criterion for data. It is the key to the successful implementation of AI.

What is data quality, data security and data consistency?

A clear definition of data integrity is important for a clear definition of the term. It is crucial to distinguish it from related terms such as data quality, data consistency and data security. All are central components of data integrity.

Data quality
Data quality is subordinate to integrity and indicates whether the stored data is accurate and correctly depicts facts from the real world. The key factors of data quality are accuracy, completeness, consistency, reliability and timeliness.

Data consistency
Data is considered consistent if it is logically correct, uniform, up-to-date and free of contradictions. Data consistency is therefore a central component of data quality.

Data security
Data security is intended to ensure the integrity of data. It represents the protection of digital information against unauthorized access, damage or theft. Ideally, data should be available in an unmodified state. If they have been changed, the modifications should be recognized and taken into account during evaluation in order to ensure data quality and consistency in addition to security.

With the increased use of AI and the implementation of large language models, the importance of data integrity will continue to grow in the future.

Shaping the future with data integrity and a reliable partner.

Why is data integrity important?

Companies make their business decisions on the basis of data - entire management processes are based on the collection, storage and evaluation of data and digital information. However, if the data on which the decision is based is incorrect, the decision made may also be wrong. The same applies to the functioning of AI and large language models. These solutions also use data to automate processes and generate answers via a chatbot. If incorrect information is available, they cannot function reliably.

In addition, the responsible handling of data and information is also regulated by laws and regulations. GDPR-compliant handling of data is not possible without comprehensive solutions to ensure integrity. Compliance with data quality, data security and data integrity is therefore particularly important.

How can you ensure data integrity?

For these reasons, the integrity of data should be a high priority in the company - especially if the company is data-driven and management makes business decisions based on collected data or processes and workflows are to be reliably automated.

Risks

  • User errors and misconfigurations
  • Transmission error
  • Bugs, viruses and malware
  • Compromised hardware

These risks are real in almost all companies. It is therefore important to create rules and processes that permanently and sustainably ensure data integrity and thus provide management with a reliable basis for decision-making and automation. The following points should therefore definitely be taken into account.

Solution approaches

  • Restricting data access and defining rules for processing data
  • Data validation & ensuring the correctness and uniqueness of the data 
  • Regular data backups & log checks
  • Regular internal training and sensitization of employees
  • Error detection software

In addition, there are some professional procedures that protect the integrity of the data. These include, among others Data Cleaning or the creation of Data snapshots. Data Cleaning helps to identify and eliminate inconsistencies and irregularities in large data sets. Snapshots represent virtual images of systems and data carriers and, similar to a backup, enable the restoration of a previous state of the database. Both functionalities help to ensure data integrity, data quality and data security.

There is no way around a comprehensive strategy to ensure data integrity.

Professional data management with Konfuzio

In order to implement these measures and thus ensure data integrity in the long term, it is advisable to work with a professional partner. Konfuzio has extensive experience in the intelligent collection, processing, storage and management of data. Konfuzio's systems make it possible to define all the necessary rules and implement multi-layered control mechanisms to improve and maintain data integrity in companies in the long term. The company's own data never leaves the German jurisdiction throughout the entire process and is therefore protected by the GDPR and other European data protection regulations. 

In addition, functionalities such as the snapshot feature, data cleaning or the option of on-premise hosting provide further opportunities to take measures that ensure the integrity of the data while taking into account the individual requirements of companies. On this sound basis, intelligent solutions for document processing can be automated and complex processes can be sustainably optimized.  

Conclusion

So that data also provides a solid basis for your company's Automation and optimization of business processes as well as for management decision-making, there is no way around a comprehensive strategy for ensuring data integrity. With the increased use of AI and the implementation of large language models, this importance continues to grow. Large language models do not question the contents of their database, but form their conclusions, answers and decisions from the available data. 

As in management, a solid database is the foundation for good results. Companies should be aware of this relevance in order to avoid being left behind economically in the digital competition. In fact, reliable solutions to ensure data integrity should be implemented at an early stage. Konfuzio can be a competent and reliable partner in all of these areas, ensuring that data integrity is the key to the successful implementation of AI, sustainable automation and reliable decision-making in your company.

Are you interested in automating and optimizing your processes and looking for a reliable partner to collect, store and process data? Get in touch with our experts and find out about the many possibilities offered by Konfuzio software:

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