We’ve received a number of requests to put the Integrated Data Enterprise Series into an easier to consume format, and that is precisely what this is. Here you will find every video and some of the key highlights that accompany each chapter post.
Table of content
- The difference between Data Fabric, Data Mesh, Data-centric revolution, and FAIR data?
- Why a data fabric wins out over traditional architecture
- Knowledge graph technology for a data fabric
- Getting started on building your data fabric
As enterprise data continues to be more and more distributed, a variety of concepts have been developed about how to manage data that is distributed. The approaches and movements that represent cutting-edge thinking about this topic are Data Fabric, Data Mesh, Data-centric revolution, and FAIR data.
- Centralized views of data management are ingrained in culture, and data management skill sets and mindsets.
- Ambiguity and awareness between different distributed data management concepts.
- Use resources (like The Rise of the Knowledge Graph ebook) at your fingertips to help convince data management stakeholders that a distributed data management architecture is the best way to deliver all forms of data transformation.
- Analysts seem to be rallying behind Data Fabric as the term coining this distributed data management architecture. We, Cambridge Semantics, are using this term as well.
A common awareness in the ways typical data architectures fail with distributed data are the driving factors of the Data Fabric movement. The failure modes discussed in this video are common in enterprise data systems, and in any shared data situation.
- Again, resistance to change. Data managers want to stick with what they know.
- Ask yourself if your system can meet all of these requirements:
- Is it flexible in the face of complex or changing data?
- Does my data include description in terms of business concepts?
- Can we deal with unanticipated questions without a fire drill?
- Are we data-centric (as opposed to application-centric)?
- Do we view data as a product (with SLA, customer satisfaction, etc.)?
- Is our data FAIR (findable, accessible, interoperable, and reusable)?
We believe that knowledge graph technology, in particular one based on the W3C Semantic Web standards (RDF, OWL, and SKOS), is the best way to achieve a successful data fabric. If you look at the above requirements, you realize pretty quickly that a knowledge graph is the best solution for data fabric implementation.
- A basic data fabric tenet is that enterprise data management should embrace a wide variety of technologies, and in fact, it should be flexible enough to adapt to any new technology. So it would be shortsighted to say that the data fabric must rely on one particular technology.
- Nevertheless, any realization of a data fabric will be built on some technology. We believe that knowledge graph technology is the appropriate choice.
This video speaks to how you can provide executives in your enterprise with an integrated experience when consulting business data. Technology is no longer an obstacle to realizing this data integrated dream.
- Apprehension to change: Digital transformation changes how the business operates. The adoption of a data fabric is the manifestation of that change in enterprise data management.
- Don’t go “all-in” for the entire enterprise at once. Find a use case to prove, then expand.
- First, find yourself a guide post. We recommend using The Rise of the Knowledge Graph to leverage as a tool for informing your colleagues about data fabrics and knowledge graphs.
- Second, remember that nothing succeeds like success; you will have to deliver something that provides real business value to bring these ideas home.
- Third, when you organize the application’s data, don’t organize it just for that application; organize it for the enterprise. Follow the principles in The Rise of the Knowledge Graph for further examples.