Data integration architecture is the glue that binds an organization together. However, many enterprises now recognize that conventional, centralized solutions for managing and accessing data are failing. A distributed data approach is essential to give everyone access to the data they need when they need it.  

The data fabric and data mesh are among a few ideas that aim to enable the transformation of traditional data integration architecture and open the door to distributed networks. Crucially, all these ideas are built on the principle that data is a valuable asset in its own right. By sharing and managing that data more effectively, its value to the organization is further enhanced. 

Four Key Concepts for Transforming Data Management and Access

• Data fabric is a design concept for data integration architecture, emphasizing flexibility, extensibility, accessibility, and connecting data at scale.
• Data mesh is an architecture for managing data as a distributed network of self-describing products (i.e. including metadata which describes the data and its relationships) within an enterprise, and where data provisioning is taken as seriously as any other product within that enterprise.
• Data-centric revolution is based on the idea that, while business applications come and go, an organization’s data retains its value indefinitely. Recognizing the durable role of data is a fundamental change in how data integration architecture is viewed.
• FAIR (Findable, Accessible, Interoperable, Reusable) data is a concept that outlines good practices for data sharing on a global scale.   

All these approaches strive to create a unified data environment. However, none of these ideas propose a specific technological solution. Altair® Graph Studio™ hosts a synergistic technology for your data integration architecture called a knowledge graph. Knowledge graphs offer a highly effective means of achieving your organization’s data analytics goals.  

Key Limitations of Conventional Data Integration Architecture

Before considering the benefits of a knowledge graph, let’s review the limitations of a conventional data integration architecture. 

Centralization of data is the most obvious failure mode. Any solution called a “database” supervised by a “data manager” encourages storing data in a single location.

This central depository will almost invariably need to scale out simultaneously in several dimensions to combine information from multiple sources. Numerous types of data must be integrated and represented. What’s more, data delivery will need to consistently meet the organization’s performance requirements. 

Centralized data solutions will always struggle to achieve these critical objectives. The inherent weaknesses of centralization include:  

• Insufficient speed
• Inflexibility
• High costs
• High levels of risk (due to frequent delivery failures)
• Tendency to suppress variation in datasets
• Lack of clarity regarding data ownership
• Multiple copies of similar data held in silos across the organization
• Data is focused on applications and not regarded as an asset in its own right; data becomes “trapped” and processes are required to set it free.

None of these issues are new. But in the current environment, they create powerful headwinds. As a result, the arguments in favor of a new approach to data are compelling:

• The speed of business is accelerating.
• Commercial techniques for managing enterprise data are failing to keep pace.
• Profit centers can’t afford to be slowed down by application development.

The Vital Role of the Knowledge Graph 

In recent years, many organizations have utilized a knowledge graph to deliver flexible, insightful analysis within a single application. However, a knowledge graph is a distributed system by design, meaning it can be employed well beyond the scope of a standalone application. At the same time, one of the basic tenets of a data fabric is that data integration architecture should be flexible enough to adapt to any new technology. Organizations can therefore achieve a successful data fabric by harnessing the multiple benefits of a knowledge graph:

• Flexibility
• Ability to embrace an individual organization’s specific concepts and processes
• Data-centricity
• Ability to deal with unanticipated questions
• Support for data-as-a-product
• Support for FAIR data principles

Knowledge graphs won’t be another warehousing application in an enterprise, adding another application to your enterprise. Rather, knowledge graphs become the backbone of a distributed data fabric, enabling the interoperability of data sources across the enterprise.

How to Start Building a Data Fabric 

A data fabric is more than just a design concept for data integration architecture. Implementation requires rethinking how data is perceived and treated. 

How Should Organizations Build a Data Fabric?

The knowledge graph represents a proven and accessible technology solution for building a successful data fabric. However, cultural change must come first. This requires an iterative approach that addresses resistance from within the organization. For example, developers may tend to see the problem as “just another application” that must be built. In reality, a sea change in the perception of data is required. Similarly, data scientists may be inclined to focus on the problem in front of them, rather than consider the bigger picture. 

To overcome such hurdles, the best strategy is to start small and create a single application that demonstrates the knowledge graph’s ability to deliver business value. At the same time, the project team should operate on the basis that this initial application will form the first stitch in their organization’s new data fabric. 

Some of the basic principles to follow include the following:

• Data should be organized for the entire business, not just the application in question.
• The data becomes meaningful throughout the organization when it’s represented as a graph and gives entities within the graph global names.
• Data owned by the enterprise should be utilized and have an application built around it.
• Each dataset should be treated as a product that other internal teams might want to use. Metadata should be represented on the same basis.

In the short term, this pilot application should offer powerful collateral for overcoming internal inertia. In the long term, the knowledge graph’s inherent scalability will provide a springboard for the journey to a new data fabric.   

For any ambitious enterprise that wants to make full use of its data, reimaging data integration architecture is no longer a luxury – the data fabric has become an essential business asset. To learn how Altair can help transform your organization’s data integration architecture, visit https://altair.com/altair-graph-studio or contact us.