Data Fabric Approach for Effective Data Management
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A discussion on All Things Data with Katrina Briedis, Senior Product Marketing Manager (APAC) at Denodo, with a special focus on Data Fabric approach for effective Data Management.

Accelerated Power of Data 

Data is everywhere and with time, there will only be an increase in the amount of data generated. Nowadays, data availability has become innovative and disruptive at the same time, the innovation being that data usage has evolved immensely over the past few decades & the evolution of data accessibility has been greatly profitable for businesses in terms of understanding their audience and catering to their specific requirements. However, the unprecedented growth in the amount of data generated comes with its own challenges in terms of integrating and managing the ever-increasing quantities of data. Read along to find out how data fabric is a trustable approach & an effective data management solution for businesses. 

Deciphering Data Fabric

What exactly is this concept of data fabric? Typically, data is disparate and resides in silos and needs to be integrated for different purposes across the business. A data fabric is a comprehensive data integration and data management solution. It is an emerging architectural approach to integrate data across the enterprise and make it more accessible. 

Think of data fabric as a fishing net that is cast out across all of your data assets and the concept of a data fabric connects or ties all of the data together in a framework that is easier to secure & govern. This approach is about automating the design, integration and deployment of data objects.

Moreover, there are different types of data integration methods available including physical data fabric or logical data fabric or even a hybrid of both. Logical data fabric means the predominant data integration style is data virtualization, whereas Physical data fabric means the predominant data integration style is ETL – where the output data after transformation is persisted in a target repository.

With a logical data fabric, we leave the data where it is and do not move it to another location to support a specific need or business use case. We leave the data at its source, where possible and as much as possible, so that we are leveraging the power of existing technologies and minimizing associated data movement and data storage costs.

From an enterprise perspective, historically there were attempts at building data warehouses (for more structured data) and data lakes (for more unstructured data) to get a centralized view of the business data. However, this was not totally successful and an alternate approach was soon required. Creating more and more physical copies of data was adding to the complexity and challenges of data management and overall governance. 

The Secret Behind an Effective Data Management Strategy

If you are wondering how a logical data fabric is the secret behind creating a future-proof data management strategy for businesses, we have the answer for you. A logical data fabric is a data management architecture, a framework, that can optimize access to distributed data, providing curated access to data for self-service by data consumers and by different applications used across the business.

It is a logical layer that sits between the data sources and the consuming applications and data virtualization is the technique used to provide this logical layer and serve data through the data fabric. A logical data fabric helps businesses swap technologies in and out without causing too much disruption to the business. It also considerably reduces data movement and provides accelerated data delivery. 

Logical Data Fabric for Data Management & Governance

Logical data fabric aids data management and governance in a way that it lowers data security risks and provides a single place for access and control of data. It provides that auditability in the data ecosystem in  terms of who is accessing what data at any given timeframe. 

Secondly, it also allows data delivery  in real time, so along with consolidating historical data, users also have access to the most up-to-date data. Logical data fabric encourages the concept of data ownership and data stewardship too, to introduce more accountability across the data assets that you have within the enterprise. 

Logical data fabric can also help to promote data sharing and reusability of data, so you’re not creating personal pools of data where you might pose the risk of getting different answers depending upon your skills and how you bring the data together.

A logical data fabric can help provide that data discovery component and complete that self service model and build more trust in data so that users can start to leverage data more, collaborate on data, recommend data, and repurpose data a lot more. 

Importance of a Flexible Data Architecture 

From a technology standpoint, it is imperative for businesses to have a flexible data architecture that provides a logically consolidated view of all enterprise data. This was crucial especially during the pandemic, where many businesses realized  that their data architecture was not as flexible as they would like it to be. With Data Virtualization, organizations can take a more rapid approach to consolidating data and data delivery. 

At the end of the day, organizations have enough of a bottleneck sometimes, so logical data fabric or data fabric, in general, helps organizations become more self-sufficient. But most of all, it provides flexibility, and that means that we can adapt to change not only within the business, but out in the market too. So, data fabric or a logical data fabric really benefits organizations by providing that agility in terms of faster times of value, less data replication, less requirements for resources from it, and lower total cost of ownership.

Neha Gurudatt
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