Why data fabrics are the next step in the maturation of data management

Navin Sharma, vice president of product at Stardog

Gartner indicates that data fabric is the foundation of the modern data management platform, enabling augmented data integration and sharing across heterogeneous data sources. Relying on traditional integration paradigms that involve moving data and manually writing code is the primary reason data scientists and data engineers spend almost 80% of their time wrangling data before any analytics are actually performed. It may also be the reason why Gartner believes that by 2024, data fabric deployments will quadruple efficiency in data utilization while cutting human-driven data management tasks in half.

One way to eliminate this burden is by simplifying data integration tasks, reducing data storage costs, and improving cross-domain insights to power downstream analytics. Recently, organizations are discovering solutions that can help, including enterprise knowledge graphs, which have become the centerpiece of a properly implemented data fabric and compound its value for better, faster, lower-cost analytics.

The enterprise data landscape is increasingly hybrid, varied, and changing. The emergence of IoT, the rise in unstructured data volume, the increasing relevance of external data sources, and the trend towards hybrid multicloud environments are obstacles to satisfying each new data request. Data fabrics enabled by Enterprise Knowledge Graphs offer a new way forward by weaving together data from internal silos and external sources and creating a network of information to power business applications, AI, and analytics. Quite simply, they support the full breadth of today’s complex, connected enterprise. And it’s their time thanks, in part, to the following trends:

• The broad adoption of enterprise knowledge graph across the enterprise as part of the modern data and analytics stack
• Partners and systems integrators are looking for enterprise knowledge graphs to provide innovative ways to address old problems
• Multi-cloud adoption is accelerating, with workloads for data management and analytics shifting to the cloud at a rapid pace

Graph technologies were originally created decades ago supporting niche situations, but just as we’ve seen the number of data scientists, engineers, and analysts grow as business’ data needs increased, the technology, too, has scaled, improved, and adapted to new applications and new users. We see wider adoption of these technologies throughout the enterprise as part of a modern data and analytics stack, from applications that support streamlining data operations for companies that build their entire business model around data monetization to large enterprise organizations supporting various cross-functional needs for data uniformity and data linking across the enterprise for faster, richer insights. 

The knowledge graph platform provides the glue within this modern data and analytics  stack, operating between the storage layer, the consumption layer, and the data catalog to link all relevant data and metadata to a semantic layer that brings the data to life. This semantic layer enables better data storytelling by attaching meaning and relating similar ideas and providing knowledge of the data supply chain for further context without introducing the complexity of the underlying data structures. 

As enterprises are looking to increase the adoption of enterprise knowledge graph technologies, we see that the system integrators (SI), that likewise serve enterprises, are starting to seek out enterprise knowledge graph skills and technologies. These SIs are looking to leverage an enterprise knowledge graph to help their client organizations become data-driven in support of new revenue streams in the digital world. According to a recent study from McKinsey, high-performing organizations are twice as likely to make data accessible across the organization. It is no surprise then that we are seeing growing interest among the leading system integrators to become educated in this type of technology and are willing to invest their time, money, and resources. They recognize this as their opportunity to bring innovation to their clients as companies invest in modernizing their data and analytics stack. We see this interest as one of the early signs of how the market is responding to technology like ours, which is crossing the chasm from early innovators and adopters to the early mainstream majority.

In part, the reason for this wide-ranging and emerging interest is because enterprise knowledge graphs are well-suited to operate across clouds, which is where the data and analytics workload is shifting these days, primarily seen as cost-saving measures while leveraging more data. Enterprises too may have concerns about selecting the best cloud configuration for their needs. There is a lot of hype about, and competition between, various cloud providers, which adds pressure and can make things murky. An enterprise knowledge graph-enabled data fabric, on the other hand, provides a lot of choice. You can grab what you need now and start using it, no matter where it is and what adjustments are required for the future. It also enables you to future-proof your investments, minimizing business disruption and operations, should you want to switch the underlying data storage layer or the data consumption layer on top.

In the above-mentioned Gartner report, when it comes to the data fabric approach, “One of the most important components is the development of a dynamic, composable, and highly emergent knowledge graph that reflects everything that happens to your data. This core concept in the data fabric enables the other capabilities for dynamic integration and data use case orchestration.” Organizations need to consider a knowledge graph-enabled data fabric to weave together existing data management systems and enrich all connected apps, as they are truly the next step forward in the maturation of the data management space. 

Data fabrics powered by enterprise knowledge graph deliver answers via powerful querying capabilities as well. Because it is not a static entity, its “queryable” data layer allows users to answer questions from across data silos, enabling just-in-time analytics. In a data fabric, query happens at the compute layer above the actual storage layer, connecting otherwise disjointed silos and systems. Data flows from source to app and back again, constantly improving the data fabric. 

Mark Beyer, Distinguished VP Analyst at Gartner, summed it up nicely when he wrote, “data fabric can be a robust solution to ever-present data management challenges, such as the high-cost and low-value data integration cycles, frequent maintenance of earlier integrations, the rising demand for real-time and event-driven data sharing and more.” 

In the end, with an enterprise knowledge graph-powered data fabric, people and algorithms can make better decisions while reducing the likelihood and risk of data misuse or misinterpretation. It helps create a data culture focused on data sharing, versus data control, that provides an opportunity for self-service and self-sufficiency by making data and insight available to all and not just a handful of data specialists. Navin Sharma is the vice president of product at Stardog.