In relation to Big Data there is usually a barrage of terms such as data mining, predictive analysis or machine learning, but different technologies such as Hadoop, Apache Hive or Apache Spark are also mentioned. Or the topic is examined in the context of challenges such as data protection and security. These and other aspects can be summarized under the term big data governance.
In particular, when it comes to managing and controlling access to extremely large amounts of data, data governance ensures an operational framework and ensures compliance with regulations:
- Quality standards
- Safety regulations
- Data acquisition rules
- Processing standards
Last but not least, data governance also represents the solution to a challenge that we find again and again in practice. It is true that many companies have a lot of data. But not all available data can be processed in data science projects. In addition, descriptive metadata to identify relevant data for analysis is often missing.
Basics: What is data governance?
Neither theory nor practice currently provides a clear definition of data governance. The most general formula to which data governance can be reduced is: the specification of the rules to be observed when dealing with a defined data spectrum. Sometimes reference is also made to data governance in this context, without major deviations in terms of content.
In short, the tasks of governance or data management are planning, control and provision of data. In its original form, data governance refers to the distribution of access rights and the related tasks related to data management in companies.
In addition, data governance ensures compliance with all legal requirements, such as data protection. Another important task is to provide the necessary tools to ensure that secure access to company data is always possible. Thus, data management serves several purposes:
Ensuring access to data
Identify and avoid risks
Recognizing and utilizing business potential
Reduce data storage and management costs
Four areas of data management decision and competence
Therefore, there are four basic areas that are included in the term data governance:
- Data quality
- Data maintenance
- Data privacy
- Data compliance
In each of these four areas it is always a matter of assigning functions (data functions), ensuring compliance with standards and defining processes.
- Data quality: This is about ensuring that data is fully recorded, that data is up to date, that data is suitable for further processing and is prepared accordingly, and that access is guaranteed and regulated.
- Data maintenance: The aim is to enrich the data, correct it and maintain the master data.
- Data privacy: Ensure that all relevant standards are observed with regard to security and confidentiality aspects towards the client.
- Data compliance: Compliance with legal regulations, ethical and moral guidelines, as well as company standards and guidelines.
Data governance as a necessary complement to the data lake
Companies that store data in a data brine need a well-positioned strategy to manage the large amounts of data stored there. Without the necessary standards for data collection, storage and processing, Data Lake companies risk ending up with a “data swamp”.
Finding and processing the relevant data is then difficult and the whole system becomes inefficient. But even beyond the case of using the Data Lake, if data is increasingly recognized as an important part of the value chain, it can only be processed in a meaningful and economically profitable way if important standards are defined and met. In particular, compliance with the legal framework is crucial for the opening up of new markets.
The benefits of data governance
- Better understanding through shared vocabulary
- Time saving through descriptive metadata
- Standardization of access processes
- More confidence in the digital transformation
Data management offers several advantages that go far beyond mere compliance with regulatory requirements. First, a common vocabulary of business terms allows analysts to find the relevant data for their use cases without unnecessary detours. Second, descriptive metadata saves employees valuable time in understanding data sources. Third, access processes are standardized and data protection rules are met through automatic anonymisation, clear regulations and enforcement of access rights. And fourthly, employees and customers gain more confidence in the digital transformation process because there are clear guidelines for assessing and complying with rules and regulations in data processing.
Data governance: an integral component of data strategies
Data management is an integral part of successful data science strategies. It ensures that data quality is measurably improved through compliance. At the same time, it ensures that data processing processes are optimized. It is not a purely IT task, but lies at the interface of several areas.
Furthermore, it is not a single task or project that has to be performed only once. Rather, data governance is an ongoing process; therefore, many companies rely on an information governance officer or – depending on the size of the company – institutionalize the task as a department. The main objective of data management is to preserve and enrich the company’s internal knowledge and maintain standards in its strategic use.
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