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Get Control by Automating Data Quality Processes at Scale

Accessible intelligent solutions that let you be in control of your data quality across the organization.

  • Configure AI powered data quality detection to scale
  • Built in anomaly and error detection to reveal issues
  • Identify hidden issues and events within the data
How it works
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Put It Forward Data Quality

Requirements for Successful Data Quality Automation

Identify Hidden Patterns

Identify hidden patterns and connections within data volumes and integration pipes at scale without reporting or profiling

Easy Model Configuration

Configure the embedded machine learning to test different data models to quickly see different results

What If Data Quality Scenarios

Change the data quality inputs that you're working with to let the automation software determine the optimal path. 

Explainable Decisions Guide

Put It Forward Delphi is an open predictive data quality system that you can configure yourself.

Your Powerful AI Data Quality Workbench

Designed to be used by the professional that needs to include test multiple different scenarios quickly and at scale.

  • Quickly adjust the models with new parameters to identify hidden patterns and correlations

  • Predict impacts of correlations within the data and feed into operational workflows to trigger business events

Put It Forward Delphi AI Data Quality
Put It Forward Data Analytics Reporting

Capture and See What's Going On In The Data Flows

Work directly with predictive lead scoring analytics models or the data scientists on your team to define which criteria the machine learning should consider.

  • Quickly know the quality of your input data before it proliferates across your enterprise

  • Assign relative quality scores to records in flight between systems
  • Drill down to the field level as it's flowing through

Learn More About How To Succeed With Put It Forward Delphi

5 Keys to Successful Data Governance

Once your data is created and stored you need to plan for the management of its lifecycle. A data warehouse like Snowflake requires a solid governance plan.

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What is Data Integration?

Data integration comes in many styles and formats that can easily be confusing to the novice.  Learn about the different types and when to use each for your benefit.

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Scaling by AI Automation

Repeatable patterns for success to scale by automation and AI driven processes.  Learn from how we’ve managed to leverage these two concepts for scale.

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Ready to Take the Next Step With Predictive Lead Scoring?

Key Features
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Control Your Data Story - Why Partner With Put It Forward?

Proven Scale

Battle tested data integration software that takes on all the use cases thrown at it from api integration to continuous integration.

Independent Roadmap

Vendor roadmap independence and enterprise reliability. You can change your applications and even systems at any time because the Put It Forward software solution is vendor independent.

Complete Data Platform

Integration, Automation, Governance and Predictive Insights in a single solution that you can use individually or together.

Secure and Compliance Ready

Data governance while being audit compliant. Unlike vendor or developer supplied code - Put It Forward provides for an end to end governance monitoring function.

Put It Forward - An Integrated Solution Suite

Modern tools that run alone or together to help you scale the intelligent data story

Platform Manager

Automate and orchestrate the components of your platform from a single workspace.
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Integration Designer

Code-free design that delivers codeless integration, business rules and data transformations.
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Data Governance

Report at any moment in time what is happening with your data.
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Delphi - Predictive

Built-in predictive analytics and insights that you can control directly.
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Foresight - Insights

Embedded 360 degree insights of an account, contact or member.
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Key Features Of A Predictive Data Analytics Platform

Dynamic Prediction Modelling

Use different inputs into the prediction model to understand possible outcomes before committing

No Code Configuration

A no code environment that allows you to work efficiently and scale through configuration techniques

Certified Connectors

Hundreds of supported industry leading pre built connectors to accelerate delivery with all of your solutions

Embedded 360 Analytics

Insight and dashboards embedded directly into the software where people work to create understanding

Automated Quality Analysis

Automated predictive models and data tools that give you the power to perform a number of checks that at scale.

Easy Transformations

Use expressions to transform from any source so it becomes compatible with and acceptable to the destination system

In-flight Governance

Check fields, manage exceptions and perform error repair on the fly or answer audit calls

Secure On-Premise Connection

Secure and seamless connectivity with on-premise enterprise applications and systems

Frequently Asked Questions about Data Quality

What is data quality automation?

Data quality automation is the process of using machine learning and artificial intelligence to ensure that data is of high quality and free of potential errors. 

The process can include steps like making sure that data is complete, accurate, and free of duplicates. Intelligent data quality automation helps organizations improve the quality of obtained data by catching and fixing errors before they become a problem.

How to use an automated data quality platform?

An automated data quality platform by Put It Forward gives organizations full control over their data and its quality. 

With its use, organizations can test multiple different scenarios efficiently and identify hidden patterns or correlations by adjusting the data models with new parameters. 

The automated data quality platform can also be used to predict the impacts of correlations and modify operational workflows to trigger specific business events. 

What are the benefits of data quality automation?

There are many data quality automation benefits, including the ability to:

  • Automate data quality checks and improve their accuracy and efficiency,
  • Streamline data management processes,
  • Improve data governance and control,
  • Reduce costs associated with manual data quality management processes,
  • Increase consistency and accuracy of data across systems and departments,
  • Reduce the need for manual data quality checks,
  • Improve data quality by identifying and correcting errors before data is entered into systems.

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