Harmonization and Knowledge Management with a Semantic Single Source of Truth
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Harmonizing heterogeneous data sources with semantic graph databases.
In large companies, especially those that have grown through acquisitions, there often exists a coexistence of the most diverse tool landscapes. There are various business areas and competencies, individual processes, and historically grown environments that cannot be replaced, migrated, or standardized in the short term or economically in practice. This involves technologies, but also the people who use them.
Current State
From demand and requirements management, product and application lifecycle management, development and version control, test, deployment, and continuous integration tools, to operations and monitoring tools: In practice, developers are confronted with complex heterogeneous environments, a variety of distributed data sources and quantities with the most diverse interfaces, data formats, quality grades, or availabilities.
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For management, the smart harmonization of data offers higher transparency and quality as well as better analyses for their business decisions - across the boundaries of tools and departments. For us, it offers a common understanding and the reusability of knowledge, thus freeing up more space and time for creativity and innovation - across personal skills, different working models, and cultures.
Challenges
Many tasks in merging information are obvious and can usually be solved by so-called Extract-Transform-Load (ETL) tools. These include downloading data from the primary sources, transforming it into a standard format, optionally validating it, and finally making it available via APIs.
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Once security and legal concerns are resolved, individual data sovereignty is added. Once these are resolved and there is a willingness to harmonize on all sides, the effort to create a common understanding of the available information can continue.
Harmonization versus Standardization
Complex heterogeneous environments pose diverse challenges. Applications, like people, often mean the same thing - but speak different languages, using not only different data but also different terminologies. And that leads to friction at the interfaces, both technical and human.
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With a Single Source of Truth, it is therefore not about standardizing information and processes, but about harmonizing them, with the goal of creating compatibility instead of conformity and thus ultimately supporting strategic corporate goals.
Compatibility versus Conformity
As an example, let's consider the use of different task tracking systems, for simplicity's sake, GitLab and Jira. If you want to show across the board in a reporting tool for your project management how many tasks with which priority are open, this assumes that both systems have the same configurations and values for specifying the priority - this is rarely the case in practice.
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The linking of information in a semantic graph creates attractive automation potentials, but only on the basis of the defined criteria; exceptional cases remain unconsidered. An assistance system should therefore not mean taking over control of all decisions, but rather it should support us. To stay with the priority, a manual intervention to control the ranking within the framework of valid values should certainly be maintained.
Common Language, Terminology
A strategic goal in large companies is often to improve internal collaboration. One measure to achieve this is to improve communication. To achieve this, a uniform terminology, a common language - technically a common semantic definition of identifiers and their synonyms - helps.
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Eliminating or at least reducing ambiguities is one of the goals of an SSOT. A glossary is a useful feature here. It represents a central reference of identifiers including a human-understandable description, the actual meaning. You can find important identifiers for understanding the terminology in the Glossary box at the botton of this article.
Semantics
A semantic graph, also called an ontology, essentially consists of a class hierarchy, the so-called taxonomy or T-Box, and the individuals as well as the instances of the classes, the so-called A-Box. Multiple inheritance is also supported. In addition, there are the properties, which are divided into data properties and object properties, as well as the annotations.
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That's it for now. In the following article, you we’ll delve into more details of a reference model as well as the mapping in the reference model.
Glossary
The most important terms for understanding graph databases:
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Author: Alexander Schulze, published October 2019
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Create a summary of the article with a maximum of 600 characters focussing on the key issues and key benefits of harmonizing data in a semantic single source of truth to become high quality and resuable enterprise knowledge assets.Translated by Ashesh Goplani, published October 2019