H Data Fabric

What is data fabric and how can it unleash the full power of AI?

June 7, 2023
Data fabric provides organizations with a holistic view of their information assets, enabling them to leverage technologies like automation, AI and machine learning.

By Douglas Vargo, CGI vice president of consulting services

The adoption of AI has been hindered by a common challenge: the presence of unclear, unstructured and unconnected data.  

One solution revolutionizing the way businesses approach data management is the construction of an integrated data fabric that connects information from disparate sources. Data fabric provides organizations with a holistic view of their information assets, enabling them to leverage technologies like automation, AI and machine learning (ML) more effectively. 

Laying the foundation of the data journey 

Organizations must first establish a solid foundation for their data-management practices by determining what type of data architecture to employ by aligning on a data and analytics operating model. This includes considering factors such as decentralized versus centralized data management, mesh versus fabric data architecture, real-time integration and identifying relevant use cases ahead of time.   

Organizations also must align to a modern data stack that is adaptable and can scale as the business grows to allow the business and customer demands to drive the priorities.  

There are three core functions IT leaders should consider:  

  • Data ingestion—Prioritize data-ingestion techniques that support batch, real-time and event-driven formats. Leveraging technologies such as Kafka and share-storage gateways will enable high-speed data ingestion, ensuring the timely availability of critical information for analysis and decision-making. 
  • Data storage—Focus on establishing three layers of storage (RAW Storage, TRANSFORMED Storage and CURATED Storage) to efficiently manage and store petabytes of data. By doing so, organizations can achieve low-cost storage with exceptional reliability. 
  • Data indexing and cataloging—Properly indexing and cataloging data into the lake ensures its visibility and searchability for data users. This enables quicker and more efficient discovery and retrieval. 

By addressing these aspects in the early stages, organizations can avoid mid-flight challenges and establish a coherent and efficient approach moving forward. 

Deciphering the data 

To achieve a holistic view of data and maximize return on investment (ROI), organizations must avoid a few common missteps. One obvious obstacle—a lack of strong engineering talent that understand data, as teams need not only technical skills but also domain knowledge relevant to the industry for understanding its true potential value.  

Additionally, organizations need to ensure their business leaders are invested and excited about how modern data and analytics can deliver transformation. Then to support the wider teams, enterprises must invest in master data management and quality tools and techniques, enabling a strong vision for 360-degree views of customers, products and other key entities.  

Many times, companies create boundaries that do not allow the data-product owner or domain knowledge to collaborate within IT, operations and business groups. This hinders the realization of the true business value.

Another common challenge is adopting a lean and agile methodology for delivering data and analytics. Demonstrating progress at all stages of the data journey is essential, as complex technical jargon and lengthy implementation cycles can lead to confusion, delays and a loss of expected value. Taking this smarter approach allows organizations to deliver tangible results and keep up with the rapidly evolving business landscape. 

Implementing data learnings  

Once a solid data foundation is in place, organizations can then leverage AI and other technologies to transform data into actionable insights and drive new programs. Some notable technologies and use cases include:  

  • Data sharing. Organizations should prioritize internal and external data sharing to unlock new possibilities for collaboration, innovation and value creation across the company.   
  • Customer applications. Seamless integration through external APIs embedding AI and analytics solutions into customer-facing applications enhances user experiences and improves satisfaction. 
  • Open-source AI platforms and libraries. There are tremendous opportunities for organizations to build upon workplace 2.0 capabilities, such as automated productivity and data discoverability. 

To remain competitive and fully leverage the power of AI, organizations must first address the challenge of unclear, unstructured, and unconnected data. Implementing a data-fabric approach enables businesses to integrate and connect information from various sources, providing a holistic view of their assets to inform a strategic approach to data management.