Data fabric democratizes data access across the enterprise, at scale. It is a single, unified architecture – with an integrated set of technologies and services, designed to deliver integrated and enriched data – at the right time, in the right method, and to the right data consumer – in support of both operational and analytical workloads.

Data fabric combines key data management technologies – such as data catalog, data governance, data integration, data pipelining, and data orchestration.

To explain how data fabric enables big data stores to handle operational workloads, a comparison between data fabric, data lakes and databases is useful.

The following chart summarizes the pros/cons of each data store, as it relates to massive-scale, high-volume, operational use cases.

In enterprise operations, there are scores of use cases that require a high-scale, high-speed data architecture capable of supporting thousands of simultaneous transactions. Examples include:

Delivering a Customer 360 solution

Delivering a single view of the customer to a self-service IVR, customer service agents (CRM), customer self-service portal (web or mobile), chat service bots, and field-service technicians.

Grounding GenAI apps with real-time data

Injecting unified, fresh data from multi-source enterprise applications into LLMs, using a Retrieval-Augmented Generation (RAG) framework, to generate personalized, trustworthy recommendations.

Complying with data privacy laws

With a flexible workflow and data automation solution, employing data orchestration and data masking tools, for compliance across people, systems, and data – designed to address current and future regulations

Pipelining enterprise data into data lakes and warehouses

Enabling data engineers to prepare and deliver fresh, trusted data – from all sources, to all targets – quickly and at scale.

Provisioning test data on demand

Creating a test data warehouse, and delivering anonymized test data to testers and CI/CD pipelines – automatically, and in minutes – with complete data integrity.

Modernizing legacy systems

Safely migrating data from legacy systems into data fabric, and then using the fabric as the database of record for newly developed applications.

Securing credit card transactions

Protecting sensitive cardholder information by encrypting and tokenizing the original data to avoid data breaches.

Predicting churn, detecting customer fraud...credit scoring, and more

Data fabric offers many advantages over alternative data management approaches, such as master data management, data hubs, and data lakes, including:

• Enhanced data management
  Allowing data to be retrieved, validated, and enriched automatically – without any transformation scripts, or third-party tools

• Expanded data services
  Using innovative engines to manage and synchronize data with full support for SQL, and an embedded web services layer

• High consistency, durability, and availability
  Meeting enterprise standards, with a trusted database layer and processing engine

• Excellent performance
  Relying on an architecture capable of running every query on a small amount of data, and in-memory processing

• Tight security
  Eliminating the possibility of mass data breaches, due to a sophisticated, multi-key encryption engine

The operational benefits data fabric provides to enterprises include:

• Unified data access
  Providing a single, integrated data layer that allows users and applications to access data across different sources and environments seamlessly. This simplifies data access and reduces data silos.
• Enhanced data governance
  Establishing and enforcing consistent data governance policies across all data sources. This ensures compliance with data privacy regulations (GDPR, CPRA, LGPD,...) and improves data quality and security.
• Improved DataOps
  Reducing the complexity of data management, automating data discovery and data integration tasks, and optimizing data movement.  
• Real-time data access and integration
  Supporting faster, more informed decision-making, and taking timely action, based on fresh, trusted data.  
• Reducing GenAI Hallucinations
  Grounding LLMs with integrated and fresh enterprise data to deliver more accurate GenAI recommendations that users trust.

There are multiple vendors that deliver an integrated set of capabilities to support the data fabric architecture. The top 5 data fabric vendors appear below:

It is commonly held that data fabric is built to support big data analytics – specifically trend analysis, predictive analytics, machine learning, and business intelligence – performed by data scientists, in offline mode, to generate business insights.

Data fabric is equally important for operational use cases – such as churn prediction, credit scoring, data privacy compliance, fraud detection, real-time data governance, and Customer 360 – which rely on accurate, complete, and fresh data.

Data teams don’t want to have one data fabric solution for data analytics, and another one for operational intelligence. They want a single data fabric for both.

The ideal data fabric optimizes the field of vision – and the depth of understanding – for every single business entity – customer, product, order, and so on. It provides enterprises with clean, fresh data for offline data analytics, and delivers real-time, actionable data for online operational analytics.

1. The data fabric continually provisions high-quality data, based on a 360 view of business entities – such as a certain segment of customers, a line of company products, or all retail outlets in a specific geography – to a data lake or DWH. Using this data, data scientists create and refine Machine Learning (ML) models, while data analysts use Business Intelligence (BI) to analyze trends, segment customers, and perform Root-Cause Analysis (RCA).

2. The refined ML model is deployed into the data fabric, to be executed in real-time for an individual entity (customer, product, location, etc.) – thus “operationalizing” the machine learning algorithm.

3. Data fabric executes the ML model on demand, in real time, feeding it the individual entity’s complete and current data.

4. The ML output is instantly returned to the requesting application, and persisted in the data fabric, as part of the entity, for future analysis. Data fabric can also invoke real-time recommendation engines to deliver next-best-actions.

K2view is the only data fabric capable of responding to entity-centric data queries in real time, at massive scale, and supporting both operational and analytical workloads.

Here are 5 reasons that K2view has become the data fabric of choice among some of the world’s largest enterprises:

1. A Micro-Database for every business entity

K2view’s patented Micro-Database™ delivers unmatched performance, ease of access, completeness of data, and a common language between Business and IT.

K2view Data Fabric unifies multi-source data for every business entity source into a single Micro-Database, one for every instance of a business entity.

A customer Micro-Database, for example, unifies everything a company knows about a specific customer – including all interactions (emails, phone calls, website portal visits, chats…), transactions (orders, invoices, payments…), and master data – regardless of underlying source systems, technologies, and data formats. In this case, one Micro-Database is managed for every customer.

The Micro-Database may be enriched with new fields that are captured, or calculated on the fly – such as KPIs, consent information, propensity to churn, and more. And it can be easily defined, using auto-discovery, to extract a suggested data schema from the underlying systems.