Data Governance and BI Compliance
An effective framework for BI compliance and data governance is an absolute prerequisite for being able to innovate as fast as possible with a BI system architecture. This means that your enterprise must instill this framework consistently throughout the company, in order to continue operating along company goals, while complying with both German and European regulations.
Effective BI Compliance demands thoroughness from the start
Your enterprise should ensure compliance with data privacy laws/regulations, even the first time you process personal information. This approach will help avoid violations and possible inheritance in the value chain. In our experience at b.telligent, we have found it best to adhere to the following principles:
- Transparency: which data are to be processed and for which purpose?
- Data management: delete all unneeded data
- Direct sourcing: do not acquire data from a third party
- Purpose-driven: use data for the defined purpose only
Another critical aspect that you cannot afford to ignore is the penal relevance of data governance that raises the pressure: this is the so-called exposure/pillory problem, in place since September 1, 2009. Here, we are referring to your duty to promptly inform the authorities and all employees or customers affected by a violation. Failure to comply may lead to a fine, but deploying a data governance and management system will help you avoid unnecessary costs and any damage to your enterprise’s image following such data privacy infringements.
BI Compliance – the Law, Risks, and Strategy Software
Address BI compliance by ensuring that all departments uphold the law
The German data privacy laws, in particular the Bundesdatenschutzgesetz (BDSG or Federal Data Protection Act) set forth the conditions under which an enterprise may use personal information. These are among the strictest of their kind in the world. Yet, there are also national, European, and international rules & regulations to be considered.
The rule of thumb in the field of BI and big data is that everything is banned, unless expressly permitted – i.e., unless you have prior consent, it is unlawful. Consequently, departments like your IT unit are very much under pressure to uniformly address highly complex compliance requirements, resolve data silos, and ensure proper documentation. We also recommend involving experts and lawyers.
Strategy to ensure data privacy
The strategy to resolve this issue means you need to set up a procedure for proper use and compliance of your BI information. Below is a four-phase approach to assure compliance of your CRM and BI systems with data privacy laws:
- Phase 1: establish the status quo of your BI infrastructure and data management program
- Phase 2: identify potentially critical issues in your BI application, and any processes of relevance to data privacy
- Phase 3: check the data privacy admissibility of each relevant process: develop concepts for authorization and access, and the conditions for encrypting and anonymizing information in the database to comply with the regulations
- Phase 4: implement the concept(s) devised in the above steps and find software to execute this as best as possible.
Simplified Data Integration with a Metadata Warehouse
Assuming compliance with data privacy laws is assured, a metadata warehouse makes it easy to set up the basic structures of the required system.
Advantages of a metadata warehouse
- Up-to-date documentation of the DWH and BI infrastructures
- Simple access to your own documentation on various departments
- Traceability of corporate data along the entire value chain
Functions that bolster data management
Deploying data dictionaries like D-QUANTUM or MetaDataWiki, which are designed in line with data governance requirements, can help your enterprise avoid ambiguous use of information in databases. Furthermore, they enable you to comply with data governance guidelines, advance the transformation to utilization-driven BI, and raise the quality of data. This approach greatly expedites analyses and cost estimates, and makes these more precise. At the same time, your enterprise retains knowledge of the data and its management.
Documentation for consistent Quality of Data
There are many ways to document a database in your enterprise. Technical approaches apply an automated process to derive metadata from the system. Specialist approaches often use a structured wiki to manually manage metadata and responsibilities, and develop the database according to the specialized requirements. Use of the latter approach is presently spreading under regulations like BCBS 239 and Germany’s MaRisk, primarily in the financial sector. In a full-scale setup, the system provides a data lineage at a specially defined, implementation, and physical level. In addition to purely capturing the information, this approach plays a key role in terms of data ownership: you can effectively support the naming and administration of data owners from a specialized standpoint and data custodians from a technical standpoint.
This method ensures consistent quality of documentation, by minimizing poor quality data, comprehension problems, and decision-making errors. The information in the database is administered via a manufacturer and system neutral link to structured and unstructured metadata. The result guarantees overarching consistency and collaboration of the data management process.
b.telligent empowers you to master the Challenges of Data Governance
We have broad-based, interdisciplinary expertise in BI, CRM, big data, and DWH services. Hence, we are extremely knowledgeable of data privacy requirements concerning BI infrastructures and data governance. You can thus rely on our expert guidance and support to address data governance issues and ensure that you are always covered – in spite of the highly complex compliance regulations.
Our consultants possess the right skills to identify potentially critical issues, and verify their compliance from a data privacy standpoint. They follow this up by creating a framework for data encryption and anonymization in your enterprise’s database, as prescribed by law. At the same time, we make certain that data management in your enterprise is continually improved and that the quality of data is maintained. This approach fulfills all the requirements of efficient data governance – i.e., accessibility, user-friendliness, integrity, and security of your data/information.
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