data quality engine

Reliable Data.
Accurate Insights.
Return on Investment.

Our Data Quality Engine (DQE) cleans and structures your data, and identifies and helps you correct errors. This self-learning solution identifies data anomalies and provides an easy workflow to correct them. The DQE learns from human inputs and quickly becomes more accurate.

Start your data quality journey today

“Cognistx has been a true partner

in helping us to identify and address our data quality issues. Their team has become an extension of the AmeriGas team and that has helped us to move in the direction of continuous data monitoring, remediation of issues, and automation through machine-learning.”

Tim Jordan – Amerigas Group Director, Supply Chain

The Data Error Problem

Lost Revenue

US Companies lose over $3T in revenue and direct expenses annually as a result of poor or erroneous data.

Wasted Time

25% of senior managers report spending up to a quarter of their day searching for data.

Garbage In/Out

85% of senior managers believe their existing systems do not produce trustworthy data.

have a data error problem? Speak to a data scientist

The Cognistx Data Quality Engine (DQE)

Step 1

Client data (erp, crm, unstructured) is prepared for input to the system via cloud storage dump or API integration.

Step 2

Data is ingested and prepared for the rule engine application through preprocessing, cleaning and normalizing.

Step 3

Rule engine applies business rules and AI/ML to identify and track anomalies, and suggest corrections.

Step 4

Anomalies are aggregated and converted to human-readable form with reporting metrics.

Step 5

Dashboard presents anomalies for user interaction and feedback to further improve system accuracy.

DQE Features and Benefits

Scalable and Customizable

High Volume and Velocity

Deep-Learning Algorithm

Tailored Data Architecture

Granular Data Analysis

Decreases Data Cleaning Effort

Improves Planning & Forecasting

Increases On-Time Delivery

Optimizes Delivery Routes

interested? Request to Speak to a data scientist

Why Data Quality is Critical to Success

Results in Weeks, not Years

Initial Consultation

System Implementation

Model Testing & Improvement

DQE Error Detection & Correction

Due to the complexity of data and it's numerous sources, actual timeline for full implementation of our DQE will vary.

try our free roi calculator for a savings estimate

case study

supply chain

Our DQE Solution for Armada involves a defined set of business rules to identify anomalous data and prevent misalignment of resources. Analysis of process data and identification of anomalies helps Armada to reduce data error, improve operational efficiency, make data-driven decisions, and save significant time & money.

DQE Analytical Objectives:

Error Detection
Cost Reduction
Workforce Efficiency

With over a million records retrieved per day and no way to track data quality, Armada faced multiple supply chain inefficiencies. Through our Anomaly detection solution, we've analyzed high velocity and volume data sets to help Armada make better decisions and save significant time, money and effort.

50M+

records
processed/year

1M+

anomalies
detected/year

$10M+

dollars saved/year

40GB+

data processed/year

case study

Logistics

Our DQE solution for AmeriGas uses Machine Learning and Artificial Intelligence to improve delivery routes and optimize loads, enhancing the entire supply chain. Optimized delivery routes significantly reduce costs and improve relationships with customers and business partners.

DQE Analytical Objectives:

Route Optimization
Cost Reduction
Operational Efficiency

AmeriGas deployed our AI-enabled Data Quality Engine and Route Optimization Solution to improve operational efficiency, increased revenue and reduced cost. The Data Quality Engine detects incorrect manual entries based on business rules and route optimization computes the best possible paths for propane transport. To date, the DQE has resulted in significant cost reduction by reducing the sub-optimal and zero-fills, and revenue increase by decreasing no-fills.

$1M+

dollars saved/year

22K+

anomalies
detected/year

19+

districts covered by
route optimization

60K+

customer accounts
analyzed/year

Cognistx DQE: Measuring Success

$12M+

dollars saved/year

51M+

records
processed/year

1M+

anomalies
detected/year

150K+

customer accounts
analyzed/year

now are you ready to learn more?

Can a Data Quality Engine Help Your Company?

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