Data Warehouse

What is a Data Warehouse and What It is from the Protection Point of View

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Decision support systems have a long history in the business world. Companies have been using analytical techniques to extract the data they need for planning since the early 1960s. Data-driven reports, models, and forecasts help management strategically manage business processes.

Business intelligence (BI) is a broad category of analytical information systems that provide related functionality. These related but indistinguishable terms, used in companies since the 1990s, are MIS (Management Information Systems) and DSS (Decision Support Systems).

What Is a Data Warehouse?

A data warehouse is a database system, alongside business information systems, in which data from different, sometimes very different, sources is collected, combined, and archived for long-term use. To prepare historical data for later access and strategic analysis in the context of business intelligence (BI), many companies regularly transfer historical data from their business data systems to this data warehouse. Data from the operational domain is transformed into actionable data:

Operational data: administrative and accounting systems provide operational data, i.e., transactional information companies generate during their daily operations. Business data processing systems such as accounting software, warehouse management software, inventory management software, or information and purchasing systems are common data sources.

Disposable data: Disposable data refers to the collection, long-term storage, and preparation of business data in one place for analysis.

In online analytical processing (OLAP), a data warehouse provides analysts with a complete view of heterogeneous data sets and enables the collection of key operational metrics. The data warehouse supports the internal knowledge management of the company by acting as a central repository for all relevant corporate data. Users typically have read-only access. The data warehouse, which serves as a database of data mining techniques, is the basis for all considerations related to the company’s performance management and strategic direction.

Data Retention for Data Protection

The extensive collection of operational, business, and customer data in a data warehouse and the analysis of these data sets using data mining techniques or OLAP software allows companies to optimize their business processes in the long term. Data protection advocates point not only to the benefits of decision-making but also to the risks inherent in this kind of analysis of large data sets, particularly regarding the international right to self-determination and the fundamental right to privacy.

In particular, critics argue that analyses that allow for personality profiling and automatically generated predictions of behavior and actions are particularly dangerous. In particular, the possibility of manipulating information from data analysis is contested.

A formal statement on the subject of “Data warehouse, data mining and data protection” is contained in the Resolution of the 59th Conference of Federal and Provincial Data Protection Commissioners. In it, the Data Protection Commissioners set out the following framework requirements, which apply both to the legal storage of personal data and to the further processing of personal data:

Personal data should be collected, stored, and processed only for purposes permitted by law or agreed upon by both parties. DPOs argue that the warehouse of personal data in a data warehouse is contrary to the original purpose and therefore constitutes unlawful warehouse without purpose.

Change of purpose only with consent: the purpose for which personal data are stored can only be changed with the data subject’s consent. In addition, the consent parameters must be communicated to the data subject. The procedures for collecting personal data must be implemented to allow data subjects to assess the risks and exercise their rights. Consent may be withdrawn at any time.

Personal data should be collected only when necessary: data processing systems should be designed to collect only the minimum amount of personal data necessary. DPOs declare that anonymous and pseudonymous procedures are secure.

Long-term storage of personal data is prohibited: the warehouse of personal data must comply with legal retention periods. Storage of data after the expiry of the specified periods is prohibited.

Avoid automated individual decision-making: data mining techniques are sometimes used in automated individual decision-making. These decisions are based solely on the electronic processing of personal data to assess the individual’s characteristics. This strategy is unacceptable, as under the European Data Protection Directive, everyone has the right to avoid being subject to burdensome automated individual decisions.

The solution of the EDPS is to advise manufacturers and users that data warehouse systems and data mining techniques should, in principle, use privacy-preserving technologies that prevent the storage of personal data through anonymization or pseudonymization.


Data warehouse has reached the media sector. In addition to expensive enterprise solutions, the market for BI solutions and data warehousing systems also includes a number of useful open-source initiatives. This reduces the financial barriers for SMEs associated with the former world of big data analytics.

BMWI advises SME users to focus on reporting first when using BI tools. Entrepreneurs create initial added value by combining existing data at a controlled cost. If the analysis reveals gaps in the database, the next step should be to restructure data collection using the ETL or OLAP technologies mentioned above. Data mining tools complete the integration of the data warehouse architecture into the appropriate IT infrastructure. These tools can uncover new trends and overlaps through in-depth analytics (e.g., shopping cart analytics) that provide important insights for strategic decisions.

When considering building a data warehouse, mid-sized companies should pay attention from the outset to ensure that their BI strategy is implemented in a data privacy-compliant way.

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