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.
Contact us today for more details or a demo

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?

Contact us to begin your Data Quality Journey!
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