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Data management is becoming increasingly challenging for organizations. With an unprecedented amount and diversity of data coming from various sources, it’s like trying to put together a picture with pieces from different puzzles. The resulting data silos make it more and more expensive for enterprises to integrate, share and use their data.
At the same time, there are more demands for data to be used in real-time and for businesses to have a better understanding of it. In addition, there is a growing trend of automating data integration and management processes. All this makes it difficult to navigate the enterprise data landscape and stay ahead of the competition.
While the topic has gotten a lot of buzz, a data fabric is not a specific application or software package you download and solve all your data management challenges. Instead, it’s a set of principles and strategies that enable the seamless integration and management of data across multiple platforms and sources.
Unlike traditional data storage systems, which rely on a central repository, a data fabric utilizes a layer over all data sources. This enables a consistent and coherent view of data, regardless of where it is stored or who is accessing it and thus eliminates the need for costly integration.
Last but not least, a data fabric is constantly evolving, adapting to the needs of the organization it serves.Being a unifying layer built on top of heterogeneous data, data fabric eliminates the need for costly integration, thus enabling a consistent and coherent view of data, regardless of where it is stored or who is accessing it. Click To Tweet
The key to implementing a data fabric is the ability to understand the context of your data. Imagine trying to navigate a huge city without a map – it would be a nightmare. The same applies to data. You need to know where everything is, how it’s related and what it means.
This is where knowledge graphs play a vital role in creating a data fabric.
A knowledge graph is a collection of interlinked descriptions of entities that put your data into context and make it easier for computers to understand and work with it. It uses formal semantics to represent real-world facts and then applies automated reasoning to extract further knowledge out of these facts. This helps users as well, by making data easier to find, use and share.
But to extract value from data, it is also essential to have a comprehensive understanding of various data formats, both structured and unstructured. Even if your data resides in a single place, finding the hidden relationships and buried knowledge for further analysis can still be challenging. And although 80-90% of enterprise data is unstructured, it is usually neglected because it is difficult to interpret.
Here, knowledge graphs come to the rescue as they can integrate billions of facts from disparate sources and formats. The formal semantics of the RDF standards stack allows data and metadata of different types to be represented in a knowledge graph in such a way that they can be analyzed and interpreted together, without ambiguity, clashes or loss of information. In this way, they provide a solid framework for data integration, unification, analytics and sharing.
A data fabric architecture powered by a knowledge graph engine, such as Ontotext GraphDB, can easily translate disparate data into useful company knowledge. It can extract information from unstructured data. It can check the data and metadata from different systems and environments for quality. This can also get automatically updated as new data and metadata becomes available.
By enabling semantic data modeling, knowledge graphs make it easier to understand your data. All information is organized and put into semantic data models, using ontologies that provide the domain knowledge needed to get deeper insights from the data. These models can then be used to query the data and to make further inference on it as part of the data fabric. The resulting knowledge can then be consumed by various stakeholders in your organization for different decision-making purposes.
As part of a data fabric, knowledge graphs simplify the complexities of data ingestion, processing and storage. As a result, users can work with company knowledge more effectively, which can lead to new insights and opportunities for innovation.
Ontotext technology offers all the necessary tools to manage, integrate and analyze disparate data for a successful data fabric implementation. As every organization has its own unique needs, challenges and goals, our approach is to provide a tailored solution that aligns with your specific data strategy and requirements.
Through working with clients from various industry sectors, we have gained significant knowledge of what their typical pain points are. To address such needs, we have created a unique methodology for managing large amounts of data and providing easy access to it through our products and services. This approach has been adopted by various industries for better data, knowledge and content management and analytics.
The increasing complexity of data management has led to the emergence of the data fabric as a solution. By implementing a data fabric, organizations can simplify data management and make it easier to access and share data.
Knowledge graphs play a vital role in this process by providing a solid foundation for efficient data integration and by creating a more holistic view of enterprise data. As a result, organizations can gain new insights and opportunities for innovation, leading to cost savings and improved efficiency.