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.
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.
When the marketing department with its CRM system or support, human resources, legal, or finance department also comes on board, the challenges quickly multiply when it comes to consolidating data across departments to create a basis for business intelligence (BI), for example, for reports and key performance indicators (KPIs).
With sometimes manual, often repetitive preparations, transformations, and transfers, it is not only data but also existing established processes that come under scrutiny. For management, transparency, reusability, automation, and cost reduction may be strategic goals. After all, measures such as the formalization and persistence of knowledge or the analysis of practices and processes using data analytics, semantics, artificial intelligence (AI), process mining (PM), or robotic process automation (RPA) open entirely new, sometimes still unimagined possibilities here.
In contrast to the approaches of Data Warehouse and Data Lake, the word semantics comes first in a semantic Single Source of Truth (SSOT) - and thus the meaning of the information it contains. This makes it a central, ideally company-wide source of knowledge. Metadata ensures a common understanding of stored information, a central terminology for a common language. The goal: to make collaboration more efficient, between people as well as between machines.
Persisting knowledge means making it sharable and available. Formalizing it makes it understandable and thus reusable. Enriching information with semantics makes it possible to automatically infer new knowledge from existing knowledge, thus creating a self-learning knowledge database. Because a Single Source of Truth manages not only the data itself but especially its meaning, semantics is an important basis for intelligent integration of information.
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.
However, when incompatibilities, different terminologies, and thus ambiguities - i.e., semantic differences between the tools - come into play, a simple one-to-one transformation quickly reaches its limits. Possible inconsistencies - i.e., discrepancies between multiple environments - can make things even more difficult, for example, errors in synchronization between systems or uncontrolled updates of individual tools. Support not only in centralizing data but also in improving data quality in the primary systems is another crucial factor for the success of a Single Source of Truth (SSOT).
With a Single Source of Truth, it is therefore not about standardizing information and processes, but about harmonizing them, to create compatibility instead of conformity and thus ultimately support strategic corporate goals.
Click here to read the original article published by LinkedIn Corporation.
Please give your feedback on this article or share a similar story for publishing by clicking here.
