Everything You Need to Know About Data Fabric for Digital Businesses

Digitization of your business can not be complete until you bridge siloed business data in one data fabric that helps you comply with risk, governance, and privacy policy and yet process data efficiently. 

Organizations with different teams and departments harvest and manage their data. Data governance and privacy constraints also stop merging various public or private data.

Then what could be the solution for truly centralized and digitized data processing? Here comes the fabric of data. Continue reading to learn it from the inside out. It will help you make the right decision when buying a data fabric tool.     

What Is Data Fabric? 

Mesh data network or data fabric is one of the top ten technology trends of 2019, according to a Gartner report. Experts of analytics and data technology domains are swearing on it as the future-ready data management tool for technology startups, small and medium businesses, and enterprises.

It is considered an information technology environment with uniform architecture connecting various data sources to business apps. At the backend, there will be a powerful artificial intelligence (AI) agent. The AI will analyze data securely and only present the need-to-know data to a sales rep, customer support agent, or business manager.

Through a bird’s eye view, the mesh data network looks like a virtual fabric on which various data storage and computational systems connect and share information.

The Purpose of Data Fabric

The hurdles of different business apps, time, space, data storage, data retrieval methods, data security protocols, etc., are the macro bottlenecks that pull the company from behind. These checks and balances also help your business secure confidential data. Hence, neither can you do away with these nor keep them as is.

Here you need a mesh data network. A freeway that makes way for data from various facilities, business apps, field offices, storefronts, servers, and many more. Also, these data could be structured, semi-structured, and raw. Not to mention, different data come with different levels of security policies.

But, the end user, like a customer, sales reps, support executives, and managers, does not need to understand all these. They just need access to data securely to complete their tasks. Data fabric will fulfill this through automation, AI, and machine learning (ML).

Other notable purposes are:

• Connects to all the business data sources through containers and connectors
• Offers data integration and ingestion capabilities within the storage, apps, etc
• Works as high-speed data infrastructure for big data analysis
• Brings data consumers and sources to one mesh network
• Offers hybrid data operations between private cloud, public cloud, multi-cloud, on-premise, and bare metal workstations

Businesses spend more time deciding and approving data rather than processing it. Employees go through hundreds of email threads before getting approvals for data processing.

It is a serious threat to the productivity of future-ready businesses. But, data fabric can rescue organizations in the following ways:

• Single window platform for accessing, submitting, safekeeping, and analyzing any type of data.
• Though everyone within the business can access data up to a certain level, all data governance and regulation policies will be upheld.
• Make data more trustworthy and easy to digest by enabling AIs to process data before humans access it.
• Enable machine-to-machine or the internet of things (IoT) communication to reduce human intervention in sensitive data.
• Easily adapt to the increase and decrease of applications, customer requests, internal data access tickets, the sudden inflow of huge marketing data, etc.
• Reducing business’s needs and dependencies to host legacy infrastructure and thus reduces costs.
• Make the best use of cloud technology by connecting all sorts of digital data sources in one place guarded by stringent AI algorithms.

Ultimately, the frontline agent will get data on their CRMs faster and process customers’ requests quickly. This, in turn, increases customer trust and satisfaction in your business.           

Benefits of Data Fabric

Reinforces the Agile DevOps Model

Agile software or product development projects can suffer big from intermittent data processing problems. Onboarding a mesh data network tool, you can virtually remove all data downtimes. 

Complying With Data Governance

The underlying AI and ML can help enforce data privacy and governance policy. Whereas the same AI algorithm will process requested data and present that to an employee according to company guidelines.  

Scalability

Managed service providers (MSPs) can scale up or down your data processing needs instantly.

Metadata Management

A data analytics catalog will host data sources, assets, and metadata. By seeing metadata, AIs can fetch requested data faster. 

Error Detection

AIs can detect data corruption, integrity issues, and errors before your business suffers revenue losses. 

Role-Based Access

Employees can request processed data depending on their security clearance within the organization. 

Abolish Data Silos

Data silos can not threaten the business anymore when data fabric brings all data on an encrypted data highway. Teams can access legitimate data from any department without jumping through hoops. 

Data Integration

Data fabric and its underlying AI enable instant data integration with real-time software like CRMs, ERPs, customer apps, frontline agent apps, etc. 

High-Quality Data

Intelligent algorithms of a mesh data network tool always analyze all data sources. Hence, employees can trust input data without validating that from supervisors.

The Architecture of Data Fabric

Mesh data network needs to ensure improved data accessibility without compromising quality and security. Hence, a standard data fabric architecture should have the following components:

Data Catalog

A data catalog is an organized form of all business data. Users can access such catalogs to find the information they need to complete tasks. The data catalog has the following sub-components: Meta Data and Knowledge Graph.

AI and ML-Based Automation

Multiple AI should be at the center of the data fabric that handles all query resolution, data quality control, security checks, etc. 

Data Integration and Transportation

Data meshes integrate data from all sources like on-site servers, cloud storage, employee laptops, etc. There should be data connectors to link information to a distant computer or transporter to move the data through the fabric of data.   

How to Implement Data Fabric

It will depend totally on which type of organization you are and your needs. Dues to the varied requirements of businesses, there is no one-size-fits-all solution to mesh data network implementation. But, there are some common features or layers of the data fabric architecture.

Data Management: This layer works for data security and governance.

Data Ingestion: This layer begins to stitch all cloud data together while locating how the structured and unstructured data are connected.

Data Processing: It ensures that relevant data is available during data extraction.

Data Arrangement: This layer includes the execution of tasks, including siloed data collection, data structuring, data cleansing, integration, and transformation to create usable data.

Data Detection: It allows you to collect data by integrating diverse sources. It is crucial for client satisfaction.

Data Access: This layer is dedicated to data consumption. Simultaneously, this layer assists in accessing relevant data through data visualization tools or application dashboards.

Data Fabric Principles

The idea of mesh data networks is to unify distributed and diverse data assets of businesses in any industry. Additionally, it combines end-to-end data management processes as a unified data management platform. 

Data fabric achieves such goals by capitalizing on the following data management principles: 

• Data discovery
• Data curation
• Data organization
• Data modeling
• Quality checks
• Siloed data orchestration 
• Data integration
• Data governance 

Data Fabric Capabilities

Never-Ending Data Query Resolution

Mesh data networks rely on high-speed internet, solid-state drives, and supercomputers to fetch requested data constantly without any downtime.   

Endless Data Integration, Discovery, and Cataloging

The primary AI responsible for data management within the fabric must work day and night to accept new raw data, analyze, catalog, and integrate it into business apps. 

Passive and Active Metadata

Active metadata is information like data quality, data usage, current editor, etc. On the other hand, passive metadata is static data that the author ads. Data fabric AI constantly changes these to reduce manual data exploration or preparation efforts. 

Flexibility

The fabric of data is highly flexible and accepts changes whenever your business needs them. 

Implementation of a mesh network of data is effortless with intelligent software. There are quite a few, but the followings are appropriate for small and medium businesses: 

Atlan

Atlan is a powerful but simple Active Metadata platform and data workspace that allows you to easily access data from any source. It functions as a modern data catalog for your data fabric needs. The platform offers solutions for all things data, including cataloging, profiling, discovery, quality, governance, exploration, and integration.

It comes with an interface that looks like a Google Search UI and a rich business glossary where you can search for understanding your data. Companies can leverage gestures like granular governance and access controls to manage data usage across an ecosystem.

Additionally, Atlan supports integration with applications like Big Query, Amazon Redshift, Snowflake, MYSQL, Looker, and Tableau.

K2View

If you are looking for a platform with end-to-end data fabric functionality, go for K2View. This data product application assists you with all stages of the mesh data network, including data integration, preparation, data orchestration, and pipelining. 

With its help, companies can enable the most sophisticated data fabric architectures in the cloud, on-premise, and hybrid environments. As a result, human data management will reduce as data fabric deployment becomes easier. It can unify data from multiple sources and pipeline them to data integrity target systems. 

Using K2View, you can instantly create data lakes and data warehouses that you can analyze immediately. Even if you have no experience in coding, it allows you to control the movement and transformation of data from source to target. 

Companies can even use the configurable rules of this platform to control data access, synchronization, and security. Moreover, it is suitable for data service automation with an easy-to-use framework.

Talend

Talend is a data fabric platform that ensures healthy access to data while helping you drive business value. Every business needs to manage uncompromised and complete data assuring its usability, integrity, availability, and security. This application lets organizations keep data in good condition by mitigating risk.

Talend is a unified platform for reliable and accessible data that offers governance, integration, and integrity. It can deliver healthy data with the help of service infrastructure and partner ecosystems. Here, you can discover your necessary data through documentation and categorization.

Since it automatically cleans the data in real-time, there is no chance of bad data entering your system. Companies can improve their productivity and save money using this tool that ensures regulatory compliance and reduces risk. 

You can offer your customers better experiences using its application and API integration. These also ensure self-service capabilities for sharing trusted data internally and externally. 

Incorta

Incorta is a self-service data analytics platform where companies can use their data to its full potential to gain insights at a reduced cost. The solution offers you a more agile data experience so you can make timely and informed decisions.

It uses in-memory analytics and Direct Data Mapping features to deliver unprecedented speed and scalability for data storage and management. Even if you want to analyze your data from multiple resources, Incorta can ensure true business agility for flexible data pipelining. 

Moreover, it helps you with data collection, processing, analysis, and presentation of business applications data. You can also present full-fidelity business data using its native visualization feature.

Conclusion

Data fabric is the next-generation data storage, processing, safekeeping, and management architecture. Though it is a future-ready application of IT, many digital businesses are already using data fabric tools to prepare their workforce for the future.

Not to mention, small ventures, medium businesses, and startups can benefit maximum from this technology since they can not afford delays in workflow due to approvals and scrutiny. Visit any or all of the tools mentioned above to check out their offerings and how those features could add value to your business.

Your RevOps business model can largely benefit from data fabric. Learn here more about revenue operations (RevOps) tools.