Cinchy Platform Documentation
Cinchy v5.6
Cinchy v5.6
  • Data Collaboration Overview
  • Release Notes
    • Release Notes
      • 5.0 Release Notes
      • 5.1 Release Notes
      • 5.2 Release Notes
      • 5.3 Release Notes
      • 5.4 Release Notes
      • 5.5 Release Notes
      • 5.6 Release Notes
  • Getting Help
  • Cinchy Glossary
  • Frequently Asked Questions
  • Deployment Guide
    • Deployment Installation Guides
      • Deployment Planning Overview and Checklist
        • Deployment Architecture Overview
          • Kubernetes Deployment Architecture
          • IIS Deployment Architecture
        • Deployment Prerequisites
          • Single Sign-On (SSO) Integration
            • Enabling TLS 1.2
            • Configuring ADFS
            • AD Group Integration
      • Kubernetes Deployment Installation
        • Disabling your Kubernetes Applications
        • Changing your File Storage Configuration
        • Configuring AWS IAM for Connections
        • Using Self-Signed SSL Certs (Kubernetes Deployments)
        • Deploying the CLI (Kubernetes)
      • IIS Deployment Platform Installation
    • Upgrade Guides
      • Upgrading Cinchy Versions
        • Cinchy Upgrade Utility
        • Kubernetes Upgrades
          • v5.1 (Kubernetes)
          • v5.2 (Kubernetes)
          • v5.3 (Kubernetes)
          • v5.4 (Kubernetes)
          • v5.5 (Kubernetes)
          • v5.6 (Kubernetes)
          • Upgrading AWS EKS Kubernetes Version
          • Updating the Kubernetes Image Registry
          • Upgrading AKS (Azure Kubernetes Service)
        • IIS Upgrades
          • v4.21 (IIS)
          • v4.x to v5.x (IIS)
          • v5.1 (IIS)
          • v5.2 (IIS)
          • v5.3 (IIS)
          • v5.4 (IIS)
          • v5.5 (IIS)
          • v5.6 (IIS)
      • Upgrading from v4 to v5
  • Guides for Using Cinchy
    • User Guides
      • Overview of the Data Browser
      • The Admin Panel
      • User Preferences
        • Personal Access Tokens
      • Table Features
      • Data Management
      • Queries
      • Version Management
        • Versioning Best Practices
      • Commentary
    • Builder Guides
      • Best Practices
      • Creating Tables
        • Attaching Files
        • Columns
        • Data Controls
          • Data Entitlements and Access Controls
          • Data Erasure
          • Data Compression
        • Formatting Rules
        • Indexing and Partitioning
        • Linking Data
        • Table and Column GUIDs
        • System Tables
      • Deleting Tables
        • Restoring Tables, Columns, and Rows
      • Saved Queries
      • CinchyDXD Utility
        • Building the Data Experience (CinchyDXD)
        • Packaging the Data Experience (CinchyDXD)
        • Installing the Data Experience (CinchyDXD)
        • Updating the Data Experience (CinchyDXD)
        • Repackaging the Data Experience (CinchyDXD)
        • Reinstalling the Data Experience (CinchyDXD)
      • Multi-Lingual Support
      • Integration Guides
    • Administrator Guide
    • Additional Guides
      • Monitoring and Logging on Kubernetes
        • Grafana
        • Opensearch Dashboards
          • Setting up Alerts
        • Monitoring via ArgoCD
      • Maintenance
      • System Properties
      • Enable Data At Rest Encryption
      • MDQE
      • Application Experiences
        • Network Map
          • Custom Node Results
          • Custom Results in the Network Map
        • Setting Up Experiences
  • API Guide
    • API Overview
      • API Authentication
      • API Saved Queries
      • ExecuteCQL
      • Webhook Ingestion
  • CQL
    • The Basics of CQL
      • CQL Examples
      • CQL Functions Master List
      • CQL Statements Overview
        • Cinchy DML Statements
        • Cinchy DDL Statements
      • Cinchy Supported Functions
        • Cinchy Functions
        • Cinchy System Values
        • Cinchy User Defined Functions
          • Table-Valued Functions
          • Scalar-Valued Functions
        • Conversion Functions
        • Date and Time Types and Functions
          • Return System Date and Time Values
          • Return Date and Time Parts
          • Return Date and Time Values From Their Parts
          • Return Date and Time Difference Values
          • Modify Date and Time Values
          • Validate Date and Time Values
        • Logical Functions
        • Mathematical Functions
        • String Functions
        • Geometry and Geography Data Type and Functions
          • OGC Methods on Geometry & Geography Instances
          • Extended Methods on Geometry & Geography Instances
        • Full Text Search Functions
        • Connections Functions
        • JSON Functions
  • Meta Forms
    • Introduction to Meta-Forms
    • Meta-Forms Deployment Installation Guide
      • Deploying Meta-Forms (Kubernetes)
      • Deploying Meta-Forms (IIS)
    • Forms Data Types
    • Meta-Forms Builders Guides
      • Creating a Dynamic Meta-Form (Using Tables)
      • Creating a Dynamic Meta-Form Example (Using Form Designer)
      • Adding Links to a Form
      • Rich Text Editing in Forms
  • Data Syncs
    • Getting Started with Data Syncs
    • Installation & Maintenance
      • Prerequisites
      • Installing Connections
      • Installing the Worker/Listener
      • Installing the CLI and the Maintenance CLI
    • Building Data Syncs
      • Types of Data Syncs
      • Common Design Patterns
      • Sync Behaviour
      • Columns and Mappings
        • Calculated Column Examples
      • Listener Configuration
      • Advanced Settings
        • Filters
        • Parameters
        • Auth Requests
        • Request Headers
        • Post Sync Scripts
        • Pagination
      • Batch Data Sync Example
      • Real-Time Sync Example
      • Scheduling a Data Sync
      • Connection Functions
    • CLI Commands List
    • Error Logging and Troubleshooting
    • Supported Data Sync Sources
      • Cinchy Event Broker/CDC
        • Cinchy Event Broker/CDC XML Config Example
      • Cinchy Table
        • Cinchy Table XML Config Example
      • Cinchy Query
        • Cinchy Query XML Config Example
      • Copper
      • DB2 (Query and Table)
      • Dynamics 2015
      • Dynamics
      • DynamoDB
      • File Based Sources
        • Binary File
        • Delimited File
        • Excel
        • Fixed Width File
        • Parquet
      • Kafka Topic
        • Kafka Topic Example Config
        • Apache AVRO Data Format
      • LDAP
      • MongoDB Collection
        • MongoDB Collection Source Example
      • MongoDB Collection (Cinchy Event Triggered)
      • MS SQL Server (Query and Table)
      • ODBC Query
      • Oracle (Query and Table)
      • Polling Event
        • Polling Event Example Config
      • REST API
      • REST API (Cinchy Event Triggered)
      • SAP SuccessFactors
      • Salesforce Object (Bulk API)
      • Salesforce Platform Event
      • Salesforce Push Topic
      • Snowflake
        • Snowflake Source Example Config
      • SOAP 1.2 Web Service
    • Supported Data Sync Destinations
      • Cinchy Table
      • DB2 Table
      • Dynamics
      • Kafka Topic
      • MongoDB Collection
      • MS SQL Server Table
      • Oracle Table
      • REST API
      • Salesforce Object
      • Snowflake Table
      • SOAP 1.2 Web Service
    • Supported Real-Time Sync Stream Sources
      • Cinchy Event Broker/CDC
      • Data Polling
      • Kafka Topic
      • MongoDB
      • Salesforce Push Topic
      • Salesforce Platform Event
  • Other Resources
    • Angular SDK
    • JavaScript SQK
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On this page
  • What’s the purpose of Dataware and Data Collaboration?
  • The root causes of IT delay and frustration
  • Data Collaboration: Using Dataware for accelerated solutions delivery
  • Not just connected, but autonomous.
  • Universal access controls, automated governance
  • Game Changer: Network Effects for IT delivery

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Data Collaboration Overview

This page provides a brief overview of Data Collaboration

NextRelease Notes

Last updated 2 years ago

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You are currently browsing the Cinchy v5.6 platform documentation. For documentation pertaining to other versions of the platform, please navigate to the relevant space(s) via the drop down menu in the upper left of the page.

What’s the purpose of Dataware and Data Collaboration?

Dataware is the emergence of a common group of technologies that solve data related problems across many business use cases. One of the most exciting new categories within this group is Data Collaboration.

Data Collaboration solves the costly, time-consuming, and ineffective integration processes born from silo-ing your data in a traditional app-centric environment. Instead of your data serving your applications, data collaboration refocuses and pivots to a model where your data is at the forefront and is being served by your apps (Image 1).

Harnessing this power, Cinchy connects unlimited data sources within a networked architecture, offering persistent delivery of real-time solutions, without complex integrations. And the more data you connect into your data network, the more powerful your processes can be.

The root causes of IT delay and frustration

When a new IT project is green-lit, you often pay a hefty fine called the integration tax where you're continuously building new integrations between applications, just to reuse data that is already available in your systems.

Over time, this never-ending cycle of copying data between fragmented apps gets more complex, resulting in delayed launches, budget overruns, and “shadow IT” projects.

This process of making copies now consumes up to 50% of the resources on large IT projects, and it's the reason that delivery often takes months, sometimes years, and costs millions (Image 2).

How can this be fixed?

Data Collaboration: Using Dataware for accelerated solutions delivery

With data collaboration, you shift your approach from integration for data sharing, to access for data collaboration (Image 3).

For every app you build where you leverage data collaboration, you’re able to access the network to reuse information for future apps. Your IT team will find that previous projects have already connected many of the data sources they need to the network.

Cinchy's data collaboration platform does for application development what the power grid does for individual buildings. In the same way that buildings no longer need to generate their own power thanks to the power grid, with a data collaboration platform applications no longer need to manage, integrate, and protect their own data (Image 4).

Organizations can build data collaboration applications in half the time, while enabling effortless and copyless data sharing across applications. Using Cinchy is unique in that it eliminates the "integration tax" that today consumes half of most enterprise IT budgets -- that is to say, data collaboration makes integration obsolete (Image 5).

Now, instead of connecting apps to gain access to data through costly point-to-point integration, your apps serve your data by leveraging and connecting it all together via Cinchy. In this way, you can still gain the best usage out of your apps through zero-copy integration while avoiding the disadvantages of data silo-ing. You have both full access to and full control of your data (Image 6).

Not just connected, but autonomous.

​Simply putting pipes between data silos, and centralizing a few housekeeping tasks, is not data collaboration. What that's actually doing is leading you down a path of managing endless copies. True data collaboration not only connects your data but upgrades it as part of an interconnected, autonomous network.

Autonomous data exists independently of any application. It is self-controlled, self-protected, and self-describing. This creates a number of benefits compared to traditional app-dependent data, including the ability to simplify cross-application usage and reporting. And when you use autonomous data in an interconnected network, wherein individual contributors maintain their roles and priorities as they apply their unique skills and leadership autonomy in a problem solving process, you get: Collaborative Autonomy.

Collaborative Autonomy is thus the principle underpinning Collaborative Intelligence, the entire basis of Dataware and Cinchy.

Individuals are not homogenized, as in consensus-driven processes, nor equalized through quantitative data processing, as in collective intelligence. Consensus is not required. Problem resolution is achieved through systematic convergence toward coherent results. Collaborative intelligence relies on the principle of collaborative autonomy to overcome “the consensus barrier” and succeed where other methods have failed.

Universal access controls, automated governance

One of the most significant advantages of dataware is the ease with which data owners can set universal data access controls at the cellular level, and automate data quality (Data Governance) with a “golden record” of data.

In effect, it is removing the need to maintain access controls within individual apps and centralizing these functions in an incredibly efficient way.

Compare this with designing and maintaining controls within thousands of apps and systems independently. It’s not only incredibly challenging and costly, but virtually impossible to maintain consistency (Image 7).

Game Changer: Network Effects for IT delivery

Dataware is a game changer for IT delivery: it produces network effects, where each new solution actually speeds up delivery times and reduces costs

Network-based designs scale beautifully and become more efficient as they expand. Consider the human brain; its neuroplasticity helps it learn more as it grows. The more interconnected it gets, the better. The neural pathways are reorganizing themselves such that the fewer connections, the higher the intelligence, because information is more efficient to operationalize.

It's the same with dataware. The more you connect your data, the harder and better it works. It's the ability to have the platform take care of your whole data journey and transformation. You don't have to manage the changes of all your applications, regression testing, the QA you have to go through, etc.

And it's also your time machine - you can have applications based on different points in time of your data, and it's all done through network-based design.

Now that you know how efficient and secure things can be there is no going back.

Let's build the connected future, together.

Image 3: The shift to data collaboration
Image 1: Apps serve your data
Image 2: The growing cost of integration
Image 4: Dataware is like the power grid
Image 5: Saving on integration costs
Image 6: Dataware means we have total control
Image 7: Dataware ensures consistency