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7 Key Data Fabric Use Cases in 2024

Updated on Jan 12
4 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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7 Key Data Fabric Use Cases in 20247 Key Data Fabric Use Cases in 2024

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Table of contents
Data fabric use cases

Figure 1. Interest in data fabrics is growing.1

Worldwide interest in data fabrics has significantly increased since the end of 2020 (Figure 1). Nevertheless, if we look at Google searches for data fabric and ETL,  we can see that data fabric is still an unpopular data integration tool.

One of the primary and trending use cases of data fabric is data integration, since data fabric’s share in data integration has also seen an increase. Apart from data integration, data fabric offers many other use cases including:

This is because data fabric architecture can be especially beneficial for businesses that need to manage, integrate, and analyze large amounts of data from multiple sources. Companies in industries such as finance, healthcare, government, logistics, manufacturing, and retail can fall into this category. As a result, depending on the business requirements, data fabric architecture can be a viable alternative to extract, transform, and load (ETL) tools and enterprise service bus (EBS) tools. In this article, we explain the 7 use cases of the data fabric solution to business executives.

Data fabric use cases

1. Data integration

Data integration has piqued the interest of data engineers all over the world, and this interest is expected to grow even more by the end of 2020. Data integration is one of the most important applications of data fabric architecture. Data fabrics’ data integration can allow organizations to combine data assets from multiple sources, such as databases, applications, and file systems, into a single, unified view.

This unified view of data can provide organizations with valuable insights, allowing them to make more informed decisions. Primarily, it can reduce data silos. In the finance industry, for example, data integration can be used to combine data from multiple financial systems. This can enable data engineers to create efficient data pipelines to promote data access. This can boost finance organizations to gain a comprehensive view of their financial and enterprise data and make data-driven decisions.

2. Data analytics

Data analytics is one of the few fields that has grown steadily since the 2010s. Organizations can use data analytics to analyze large amounts of data and gain valuable insights that will help them make informed decisions.

Data analytics is a critical application for data fabric architecture. By providing a unified view of data from multiple sources, a data fabric architecture can enable data analytics. This has the potential to broaden the applications of data science in business.

Data fabric’s real-time data integration can allow for real-time data analytics. Data fabric architecture can also help organizations with real-time data analytics by allowing them to access, integrate, and analyze data in near real-time. For example, in the healthcare industry, this can be used to analyze patient data and gain insights into patient care, treatment, and outcomes.

3. Data governance

Since the 2020s, the importance of data fabric in data governance has grown. This is because, with a data fabric architecture, organizations can implement robust data governance policies that allow them to manage their data effectively.

Data fabric can be especially beneficial for those organizations where data governance is vital to ensure the accuracy, consistency, and security of their data. For example, government organizations can use data fabric for data governance to ensure that sensitive information like personally identifiable information (PII) is protected and secure.

Also, increasing data accuracy and consistency can enhance data quality and improve the reliability of data analysis. 

4. Data virtualization

Data virtualization is one of the current technologies in vogue. Data virtualization is a key application of data fabric architecture. With data virtualization, organizations can combine data from different sources without physically moving or replicating the data. 

Very importantly, it can minimize the risks associated with data movement or replication during data preparation and analysis. In the manufacturing industry, for example, data virtualization can be used to combine data from multiple plants to gain a comprehensive view of manufacturing operations.

5. Master data management

In the last 5 years, master data management has regularly attracted interest, especially in New York. Master data management is another important use case of data fabric architecture. 

Master data management can help businesses make better decisions and improve the quality of their data overall. This is because, with master data management, organizations can ensure the accuracy and consistency of their data by creating a single, authoritative source of data. For example, in the retail industry, master data management can be used to ensure that product data is accurate and consistent across all channels.

6. Cloud migration

The interest in cloud migration peaked between 2020 and 202. Nevertheless, interest is expected to remain. According to Microsoft’s recent survey of CTOs and IT experts, the use of multi-cloud and hybrid cloud technologies will continue to expand (Figure 10).

One important application for data fabric architecture is cloud migration. By migrating their data and applications to the cloud, organizations can benefit from the advantages of cloud computing, such as scalability, flexibility, and cost savings. For instance, cloud migration can be used to shift call data records to the cloud in the telecoms sector, which can allow them to process and analyze call data in real-time.

Data fabric is an architectural approach that allows businesses to build a unified, distributed data architecture that spans on-premises and cloud environments. Very critically, data fabric can enable elastic data processing, which is critical during cloud migration because workloads and data volumes can fluctuate rapidly.

7. Data federation

Data federation is another important use case of the data fabric architecture. Data virtualization is more popular than data federations as a way to connect data, except in Australia (Figure 11). However, data federations can also come in handy. With data federation, organizations can combine data from different sources without physically moving or copying it. This is similar to data virtualization. However, the key difference is that data virtualization provides a single, abstracted view of the data, while data federation provides a virtual database that federates queries across multiple data sources. 

In certain business situations, data federation can enable organizations to combine data from different sources more flexibly and dynamically than data virtualization. In cases where there are very large datasets or very complex queries that require high performance and scalability, data federation can be preferred over data virtualization. In such cases, data fabric can offer flexibility to energy companies because it can offer both data federation and virtualization services.

Especially in the energy industry, large oil and gas companies can have terabytes of data spread across multiple systems, as well as a complex query that requires joining and aggregating data from multiple sources. In such cases, data federation can help improve performance and scalability. Because it can help run queries in parallel across multiple data sources and return the results in a virtual database to the user. 

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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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