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The digital transformation has been responsible for a dramatic surge in the number of sources and volume of available data. This represents an opportunity for companies for whom data has long been an integral part of their product and service portfolio.Those who do not know how to use their growing data store properly, however, can find themselves in a difficult situation – and not just legally. Data governance can help. If you take an intelligent and agile approach to the use of your data, you can not only rest assured that your data is secure and legally compliant but the insights you gain into your company's processes will also provide you with an invaluable decision-making resource.Data analysis and linkage also has an important role to play in enabling companies to develop new services and secure a competitive advantage in the digital age. The key requirement here is having an intelligent data catalog that is closely aligned with your long-term goals.
Data governance is the holistic management of the information used across your organization. It is about security, protection and integrity, but also about extracting value from your data. Because the quality of each of these aspects is paramount, it is normally assured by a wide range of verification, data planning and provisioning tools. Compliance and digital traceability are two important reasons why a data governance initiative is needed, but they are by no means the only ones. By developing mandatory standards, access to available information becomes more transparent and data-based intelligence can be used across departments. And by linking data, processes can be simplified, and results improved.
The creation of a data catalogue tailored to the company's needs and opportunities and also optimally capturing its data stock is key to ensuring the long-term success of any data governance initiative. An appropriate metadata management system can capture, maintain and check the integrity of the metadata. An intelligently structured data catalog looks just like a shopping catalog in which customers or users (who in this context are "shopping" for data) can find and select the information they need about a product and its producer (i.e. the data source and data controller).
When creating a data catalog, it is essential to use software tools capable of accurately mapping your management processes. Information about the origin of the data and how and where it was obtained must be incorporated into the development of the data catalog so that, when it is deployed, the data governance solution can span the entire company like a data canopy.
Key component: data catalog
The mapping tool used is therefore a key component of the metadata management system. One of the important aims of a data catalog is to break up information silos, enabling users from different areas of the business to work independently with the data while at the same time supporting each other through interdepartmental knowledge sharing. Equally important is the provision of background information on existing data, such as its origin, key figure definitions, data controllers and access rights. And finally, let's not forget that the operational linkage of data from different processes can also have a positive impact on performance.
Data linkage for improved performance
How much effort goes into the development and functions of the data catalog depends on the intended application scenario, the company's key issues and the resources available. It is certainly the case that Excel can sometimes offer an introduction to the topic of data management, but if you really want to generate added value with your data, there is no better option than a professional data catalog solution. And professional does not have to mean expensive. Many tools are now scalable to different company sizes and requirements and offer options for extending the range of functions as and when needed. A small company with just a few users can therefore start with a preconfigured solution, while a larger company with many more data citizens can also deploy a preconfigured model but then extend it or invest in the development of a bespoke model. In any case, it is important to maintain a business-driven view of the data and its use rather than approaching the project primarily from a technical perspective.
Managing and analyzing metadata – the right solution makes it possible
When gathering, managing and analyzing metadata, it makes sense to have a tool that supports companies of any size from the outset. Synabi, a company established by the b.telligent consulting firm, offers a Confluence-based application. Its D-QUANTUM software gathers, manages and analyzes all metadata in compliance with the relevant legislation and generates considerable data value. It delivers a clear graphical representation of all data-relevant interrelationships and offers companies new perspectives on their data and its route through the company. "The heart of any data governance initiative is metadata," says Jörg Westermayer, who heads the data strategy & governance competence center at b.telligent. "Almost every project step of a data governance initiative generates metadata, and this is best stored in a data catalog."
Development of a prototype data catalog for an agile start in a DG initiative
When developing a smart and agile data governance solution, Westermayer recommends investing in a professionally supported pilot project. This agile approach combines the data governance initiative's most important action areas, such as data scope, organization, processes and roles, with the creation of a template-based data catalog. During the preparation of the catalog, the company accumulates insights into the processes and organization that are important for data governance as well as into the specific benefits the initiative can offer the company. "The catalog serves as a reality check for the action areas involved. In a series of associated workshops, we usually deepen the knowledge already gained and establish the requirements for the data governance strategy," says the data governance and strategy expert. The first potential solutions for the company are jointly developed and then firmed up with the aid of a roadmap. The final evaluation of the results of the pilot project will determine the scope and cost of the initiative. Modularization of the implementation project is also possible by prioritizing action areas and identifying quick wins. This can result in an agile, tailored, phased plan that will also have a beneficial, long-term effect on the issues the company faces.
Conclusion:
As your data governance initiative progresses, your data catalog will grow and deliver immediate value to your business. This assumes that the initiative is managed professionally and that the catalog is delivered in such a way that it offers clear benefits for the daily work of the staff authorized to use it, such as, for example, easier access to and processing of data, or time savings, or improved performance. The greater the operational effectiveness of the tool, the more staff will be motivated to use it for the long-term benefit of the company.
