Data fabric architecture: building blocks, use cases, and benefits

Data fabric architecture: building blocks, use cases, and benefits

May 16, 2022

Tatyana Korobeyko

Data Strategist

In the modern, data-driven world, information only becomes valuable when contextualized and can be accessed timely and used by business users. For many organizations today, however, the operationalization of corporate information is still on the agenda.

One of the major reasons behind that is the increasing complexity of the data landscape. Businesses now want to back up their decisions with not only ERP and CRM data but also live clickstream data, equipment sensor data, and customer real-time location data scattered across its various systems.

Number of data sources used for decision-making

In addition to this, the number of data consumers is growing exponentially, as different-level IT and business users are now typically provided access to information to solve their work tasks. With all this, balancing security and accessibility becomes a challenge, especially for organizations operating in highly-regulated sectors.

Data management challenges

Trying to overcome the datasets variability challenge with minimal effort and have their investment in data infrastructure pay off, firms are now turning to data management services. And that’s where the data fabric concept comes into the picture.

Data fabric architecture and capabilities

Data fabric is a design approach and a set of technologies that help you break down data silos and deliver data to consumers promptly regardless of its location, type, and volume. Data fabric architecture incorporates knowledge graphs, data integration, AI, and metadata activation capabilities to enable consistent access, consolidation and exchange of data across the organization without preprocessing as well as its storage in a centralized structured repository.

As an industry-agnostic design concept, it can be implemented into any sector to help achieve:

  • Enterprise intelligence

Data fabric architecture streamlines the consolidation of information from internal and external sources, helping companies regardless of their size obtain a bird’s-eye view of their business with the possibility of drill-down and drill-through.

For example, using a self-service dashboard to overview enterprise-wide sales during the last quarter, a sales manager can spot a sudden drop in sales last month and in several clicks identify that the reason lies in the shipment delays due to a new carrier performing poorly. This way, without turning to IT teams, business users can analyze corporate performance and identify departments, teams or employees with highest and lowest KPIs, run risk analysis, work out detailed budget plans, and more.

  • Operational intelligence

Data fabric architecture enables the delay-free movement of large volumes of sensor data, security logs, clickstream data, etc. into storage repositories and then to analytics, data science, and visualization tools for further usage. As a result, manufacturers can shift from scheduled to condition-based equipment maintenance, supply chain companies can balance inventory and demand in real-time, financial institutions can instantly decide on the viability of credit expansion, etc.

  • Customer 360

The info companies gather across different customer touchpoints is usually stored across multiple locations, including the CRM system, ecommerce platforms, and point of sale databases. After being collected and aggregated into customer golden records (unified customer profiles), this information may be used by marketing teams to dynamically segment potential clients, and create targeted campaigns for upselling and cross-selling. Customer service and sales reps, in turn, can personalize their communication with clients. Relying on customer sentiment analysis, companies can further rebrand underperforming products or optimize their service portfolio to cover newly arisen customer needs.

  • Regulatory compliance

With the AI-enabled data governance policy enforcement, data fabric supports automated classification of data assets, and sensitive data detection and masking. Relying on the data fabric’s robust data governance capabilities, you can also track where data comes from, how it was aggregated, who viewed it, when, and so on. 

  • Data marketplace capability

Data lineage, a catalog-based knowledge graph of enterprise data, dynamic metadata management, as well as governed self-service capabilities, transform data fabric into an internal search system that all authorized parties can access and get accurate and approved data. 

Data fabric adoption across industries 2018-2026

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Data fabric architecture building blocks

Having decided to set up the data fabric architecture, many businesses start formulating a strategy to fulfill the task. Here, we outline six core components to set and maintain to continuously improve the data integration and management processes in an organization.

1. Data management

Data management is a fundamental component of a data fabric and is closely intertwined with its other elements. The practice defines who can access business information, how granular it would be, how often it would be refreshed, what information would be masked or encrypted due to high sensitivity, what transformations (cleansing, enrichments, etc.) would be applied, and so on.

Knowledge graphs, the AI engine incorporated into the data management component and metadata activation, allow for unified data governance, owing to which the quality, accuracy, safety and availability of organizational information is constantly increasing.

2. Data ingestion

The second data fabric architecture component enables users to connect to all types of business information regardless of its localization and volume. Therefore, companies across any industry vertical can combine multiple data types, for instance, video recordings from brick-and-mortar stores with financial transactions from OLTP systems, as well as aggregate information in streams or batches in real-time.

3. Data processing

This component serves as a staging area for data of any type and format to be filtered for further usage. 

4. Data orchestration

This stage is aimed at cleansing, enriching, aggregating, reformatting, and blending the pre-processed business information for it to meet the requirements of the target data repositories (analytical data stores, for example) or software system.

5. Data discovery

Enabling the data modeling, curation and virtualization functionality, the fifth element helps business and IT specialists to apply data for recognizing dependencies and spotting inaccuracies.

6. Data access

It is responsible for delivering data to multiple downstream consumers, whether they are applications, people conducting analytics and reporting, or even to the data marketplace, where business users can find the data they need. 

Big data fabric architecture layers

Why implement a data fabric architecture?

Data-driven decisions made at the speed of business

According to Forrester’s research Unleash Your Growth Potential With Continuous Planning, 90% of companies believe real-time decision-making to be the crucial component for market success. With data fabric enabling self-service data handling and automating time-consuming manual data management processes, both required for real-time or near-real-time decision-making, its adoption becomes rather a question of “when” than “why”.

Increased data quality

All kinds of research conducted by reputable agencies show that the economic impact of bad data may cost companies up to 30% of their revenue. Lost sales opportunities deriving from inaccurate customer records, incorrect credit score calculations due to flawed data fed into ML algorithms, employee overpayments resulting from the incomplete and disorganized payroll records – these are only some of the possible consequences of poor-quality information. Data fabric addresses the challenge by incorporating AI and ML capabilities for constant data quality refinement.

Data quality issues companies face

Enhanced data security

In addition to granting self-service data access to a large number of users, companies are constantly seeking new ways to enforce data security management across the organization. While traditional data security practices such as dynamic data masking, end-to-end data encryption, and granular data access controls prove satisfactory, data fabric additionally suggests the AI-driven enforcement of data governance policies.

Projected impact of a data fabric

How to ensure the data fabric architecture success

Develop a healthy data culture

No matter how advanced and mature your data management tech infrastructure is, the old concept “garbage in, garbage out” remains relevant. The data in your CRM system, for example, may be simultaneously accessed by the marketing team, sales reps, and customer service specialists. You have to ensure that all of them realize the importance of clean and accurate data and feel responsible for making it so. That way, if any of them spot inaccuracies in the client’s name or address, they have to be capable of fixing the issue themselves.

Choose tech vendors wisely

So far, no technology vendor can offer a single software package to enable end-to-end data fabric functionality. You’ll have to mix and match separate tools for data integration, quality, security, and metadata management, and tie them into a single solution, integrating as many tools as possible. Usually, the more components are available from a single provider, the better. However, keep in mind that each particular tool should fit your needs and the environment well because you wouldn’t want to compromise the performance of the whole solution due to vendor lock-in. 

Enterprise data fabric software providers

Make your data fabric approach future-proof

Data fabric is more than just a methodology for managing the existing data environment. The adopters should regard it as a standardized but scalable framework for transforming each particular data source into a unified data interface. With this future-proof approach in place, you can expand your data fabric to other corporate data areas and generate more value in the future.  

Data fabric: popular questions

Data fabric vs data lake 

Data fabric vs data lake is not an either-or choice, because the two solutions complement each other. Data fabric is an approach to revolutionize the integration and management of information flows in a company, where each function – storage, processing, analysis, and management of information – is performed by a separate software product. This way, data lake is a repository for raw data storage at scale and data fabric is an architectural approach, which helps access and manage this data efficiently, as well as integrate it with other data silos. 

Data fabric vs data virtualization

Data virtualization is one of the major tools to promote data fabric architecture. The technology abstracts the technical aspect of stored data, enabling data consumers to access data at different levels of detail when the information cannot be physically moved due to legal issues, huge volumes, or any other reason. 

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Data lakes and cloud services are great for storing disparate and multi-formatted voluminous data. However, this more traditional way of storage doesn’t enable easy search, analysis, and consolidation with other datasets.

Allowing to extend the business intelligence approach to all enterprise data assets regardless of their size, type, and location, the data fabric concept emerges as the optimal solution for modern data-driven enterprises. However, do remember that deploying the data fabric architecture is a time and resource-consuming endeavor, the success of which depends on your data ecosystem maturity, the comprehensiveness of your data management strategy, and the tech expertise at your disposal.