Introducing Data Fabric

Data fabric is a term used to describe a set of technologies and practices that enable organizations to manage and access data across multiple platforms and environments. This includes supporting an organization’s need to break down data silos, gain more insight into metadata, optimize data sharing across apps and data platforms. Organizations are starting to explore more flexible ways of managing their data ecosystems and ensuring they can leverage data more effectively. This blog looks at pros and cons of implementing a data fabric architecture.

Pros include - data integration, governance, agility, scalability and cost savings. Each of these require more than software to succeed.

Cons include - complexity, integration challenges, data security, potential lack of vendor support, and limited integration options. 

These pros and cons are not exhaustive but provide a good starting point for organizations evaluating their options. 

Why The Discussion About Data Mesh and Data Fabric Are Important

Lately I have talked a lot about data mesh and whether or not an organization can actually create a successful data mesh and manage it over time. Whether or not, the reality is that organizations and also discussing data fabrics. This blog isn't going into the differences but discusses the pros and cons of data fabric because the reality is that organizations are struggling and selecting an enterprise data warehouse or data lake over a data mesh approach isn't the right conversation. Organizations most likely need a diversified approach to their data management because they have varying needs. Operational storage and analytics consumption require different platforms and tools to enable successful business outcomes.

Therefore, when people talk about data mesh and data fabric, they are looking for answers to the challenges they have surrounding the ability to leverage data across disparate data sources without sacrificing visibility into the business.  

Before moving on, let's provide a general definition of data fabric. For the purposes of this blog, data fabric is a term used to describe a set of technologies and practices that let organizations manage and access data across multiple platforms and environments. The goal is flexibility and agility.

Some of the Pros and Cons of Data Fabric

Before deciding to adopt a data fabric approach, it is important to look at the pros and cons to evaluate if it is the right approach to adopt within your organization. 

Pros:

  1. Data integration: An advantage of data fabric is the ability to integrate data from different data sources and platforms. This includes structured and unstructured data, as well as data stored in the cloud or on-premises. By integrating data from multiple sources, organizations can gain a more complete view of their data and make better use of it.

  2. Data governance: Data fabric enables better data quality, data security, and data lineage. These tools and practices help organizations ensure that their data is accurate, secure, and compliant with regulatory requirements. At the same time, data governance requires a framework, processes, people, etc. to be successful and not only a data fabric approach. It is important to make sure that, although easier to maintain, using this approach does not guarantee better data governance unless there is commitment to a full data governance program.

  3. Data agility: Organizations can be more agile in their approach to data management. Companies can quickly and easily access and move data between different platforms and environments, enabling them to respond quickly to changing business needs. This helps ensure proactive decision making and limits the potential for shadow IT.

  4. Scalability: Data fabric allows organizations to scale their data management infrastructure as data volumes increase. It is designed to work with large and complex data sets, and can handle the high volumes of data that are generated by modern applications and systems.

  5. Cost savings: Organizations can reduce costs by eliminating the need for multiple data management tools and platforms. Organizations can manage and access data using a single set of tools, which can be more cost-effective over time. It also supports better visibility into the data across sources. 

Cons:

  1. Complexity: Implementing a data fabric can be complex. It requires a high level of expertise and the right resources to set up and manage, which can be challenging for organizations with limited IT and data skill sets.

  2. Integration challenges: Integrating data from multiple sources and platforms can be challenging, especially when dealing with data that is stored in different formats or has different structures. This requires organizations to have a high level of expertise in data integration and data mapping.

  3. Data security: Data fabric requires organizations to have a high level of expertise in data security. Organizations need to ensure that data is protected from unauthorized access and potential security breaches. This includes taking into account and putting in measures to address data encryption, data masking, and data lineage.

  4. Limited vendor support: Data fabric is a relatively new concept, and there are a limited number of vendors that offer data fabric solutions. This can make it difficult for organizations to find a vendor that meets their specific needs. Additionally, organizations rely too heavily on vendors to provide the answers without realizing that for data fabric, organizations need to develop the right framework to support any software implementation. This requires stakeholder and executive support across functions. 

  5. Limited integration with existing tools: Data fabric may not be able to integrate with existing data management tools and platforms. This may limit overall effectiveness and make it difficult for organizations to adopt it and gain tangible business value. 

Conclusion

Data environments are complex. Organizations are leveraging diverse datasets across supply chains, ecommerce, and other industries. Data analysis is required to make sense of data assets and deliver data to data consumers while making sure that data pipelines support business users and strong outcomes. Adding multi-cloud and hybrid cloud to the mix creates added interest in data fabric adoption because the assumption is that the creation of a data fabric framework will support end-to-end workloads and visibility to support data transformation.