What is Data Fabric?

Data fabric serves a broad range of business, technical, and organizational alignment drivers.

Business drivers

• Quicker time to insights and decision-making, by pipelining data into data lakes and warehouses reliably and quickly.

• Real-time, 360-degree view of any business entity – such as a customer, claim, order, device, or retail outlet – to achieve micro-segmentation, reduce customer churn, alert on operational risks, or deliver personalized customer service.

• Reduced total cost of ownership – to operate, scale, maintain, and change legacy systems –by modernizing them incrementally and quickly.

Data management drivers

• Data preparation automation saves data scientists, data engineers, and other IT resources, from undertaking tedious repetitive data transformation, cleansing, and enrichment tasks.

• Access to enterprise data in any data delivery method – including bulk data movement (ETL), data virtualization, data streaming, change data capture, and APIs.

• A data fabric platform integrates and augments a company’s data management tools currently in use, and enables the retirement of others, for increased cost effectiveness.

Organizational drivers

• A common language shared between data engineers and data consumers improves collaboration between data and business teams.

• Self-service data access capabilities let data consumers get the data they need, whenever they need it, for increased business agility and speed.

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

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:

• Simplified data orchestration
  Integrating operators for external databases, business logic, masking, parsing, and streaming
• Automated test data management
  Generating data from production systems, and then providing high-quality test data to testing teams
• Rapid data privacy compliance
  Configuring, managing, and auditing Data Subject Access Requests associated with data privacy regulations such as GDPR, CCPA, LGPD, etc.
• Comprehensive data administration
  Configuring, monitoring, and administrating data, with admin management tools, an intuitive visualization studio, and web admin tools
• Optimized cost of ownership
  Relying on in-memory performance on commodity hardware, complete linear scalability, and risk-free integration

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.

But 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.

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:

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 the data for every business entity from all underlying source systems 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.