Introduce Knowledge Fabric to Enable Collaborative, Business-Driven Data Model Design

Why Are We Building This?
Relying on IT alone to design and build a semantic data model inhibits business agility. It can even lead to data chaos when business teams are forced to find another way to get data insights. By using agile methods, organizations can develop a business-sourced model—a knowledge fabric—that is designed and built through business-led and iterative collaboration. Publishing that certified knowledge fabric delivers governed access to the broader community. making analytics accessible to everyone.

What is the knowledge fabric?

A business-orientated data model simplifies access to and understanding of underlying data. But when IT is solely responsible for designing, creating, vetting, and testing any sanctioned model, getting access to critical data can often take weeks, or even months. The knowledge fabric will enable analysts and business teams to create these business models faster and certify them for use across a workgroup or enterprise. Here are examples of how the knowledge fabric works:

  • In digital marketing. A company’s digital marketing team publishes and certifies the logical view of digital marketing data to the entire department. This model is made up of many tables and other datasets, along with key performance indicators (KPIs) and other calculations defined by the digital marketing team. Instead of waiting for an IT-generated model, users have the sanctioned marketing model to analyze. The administrator then certifies the data source, ensuring oversight and business-led governance.
  • In finance. Working with financial analysts, the CFO creates a model to give the department comprehensive views of their data. Leveraging work already done in other areas such as payments and cash receipts, data models are built for the different areas of finance such as revenue, expense and balance sheet.

This approach can be used to do responsible crowdsourcing across the organization, evolving the knowledge fabric for use by broader communities of users, and then publishing the model for governed access to data.

Understanding the consumer/analyst experience with these models becomes key to streamlining the discovery process. For example, the system can suggest existing model(s) in place for data you want to analyze, eliminating constant rework and potential data chaos. The system can suggest other sources of related data based on other use patterns. For example: Consider looking at source X and Y in addition to source A.

How does this benefit you, the customer?

By combining the agility of self-service–generated models, along with the reusability of components generated from artifacts built by content experts in each business area, our customers make data insights accessible to more users throughout the enterprise. Knowledge fabric delivers the best of both worlds.

Safe Harbor

The preceding is intended to outline our general product direction. It is intended for informational purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, timing, and pricing of any features or functionality described for Oracle’s products may change and remains at the sole discretion of Oracle Corporation.