Ensure High-Quality, Reliable Data with DataLark

Enhance data accuracy, consistency, and compliance across your enterprise with automated data quality management.

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Ensure Data Accuracy, Consistency, and Compliance

Keep your data clean, structured, and reliable for seamless
business operations.

Data Profiling & Analysis

Uncover data inconsistencies and structure issues with automated profiling tools. Integrated with Python Profiling libraries.

Detect missing, inconsistent, or duplicate records.
Visualize data relationships for better structure management.
Identify anomalies and outliers before they impact operations.

*The visuals are the result of DataLark's processing, leveraging Pandas Profiling integration.

Automated Data Cleansing

Eliminate errors and ensure clean data for enterprise-wide use.

Duplicate detection and removal.
Automatic correction of missing or inaccurate values.
Consistency checks across datasets.

Intelligent Data Validation

Ensure data integrity by enforcing business rules and validation criteria.

Pre-configured validation rules for SAP and non-SAP systems.
Customizable rule creation to meet business-specific requirements.
Real-time data quality checks before loading into target systems.

Standardized Data Across Systems

Maintain uniform naming conventions, formats, and structures for seamless compatibility.

Automatic standardization of codes, labels, and naming conventions.
Format enforcement for numeric, text, and date fields.
Global and industry-specific standard compliance (ISO, GDPR, etc.).

Continuous Data Monitoring & Auditing

Gain real-time visibility into data quality and compliance.

Automated tracking of data quality KPIs.
Alerts for non-compliance or data quality deterioration.
Audit trails for historical analysis and regulatory reporting.

OPTION 1

On-Premise Deployment

OPTION 2

Cloud Deployment

Why DataLark is Essential for Data Quality Management

Enforce Consistency

Standardize data formats and naming conventions automatically.

Automate Data Cleansing

Reduce manual effort in fixing data issues.

Improve Decision-Making

Trust data-driven insights with accurate, reliable information.

Ensure Compliance

Maintain regulatory standards effortlessly.

Proactive Monitoring

Detect and resolve data quality issues before they impact operations.

Trusted by Leaders in the Industry

A global leader in industrial automation
A global manufacturer of architectural products

Problem

The client faced challenges in maintaining high-quality master data across their SAP landscape. The complexity of their operations, involving numerous SAP objects such as materials, BOMs, and others, led to:

Data inconsistencies across various SAP modules, affecting reporting and decision-making.
Manual validation efforts, requiring extensive time and resources to ensure compliance with business rules.
Error propagation due to lack of proactive data quality checks, impacting downstream processes like procurement, production planning, and finance.
Difficulty in maintaining data integrity as multiple systems contributed to data creation and updates.

These challenges resulted in inefficiencies, operational risks, and increased costs associated with poor data quality.

Solution

To address these issues, the client leveraged DataLark as a platform for data quality management. The solution introduced:

Automated Data Extraction: Seamlessly reading and analyzing master data from SAP, covering materials, BOMs, and other key objects.
Business Rule Validation: Configurable rule sets to automatically detect data inconsistencies, missing attributes, duplicate records, and misaligned relationships.
Deviation Reporting: Reports highlighting data quality issues, allowing stakeholders to take corrective action.
Automated Adjustments: For predefined scenarios, the system corrected data inconsistencies automatically, reducing manual intervention.
Continuous Monitoring: Ongoing validation to ensure data integrity as new records were created or modified.

This approach enabled the client to establish a proactive data governance framework, ensuring SAP master data met business and regulatory requirements.

Results

With DataLark, the client established a scalable and efficient data quality management process, ensuring that SAP master data remained accurate, reliable, and aligned with business objectives.

65%

Reduction in Manual Reconciliation Efforts: Automated validation streamlined data comparison, significantly reducing the need for manual intervention.

50%

Improvement in Data Consistency: Intelligent rule-based validation minimized discrepancies and enhanced data reliability.

40%

Faster Issue Resolution: Automated deviation reports and alerts enabled quicker identification and correction of data mismatches.

Problem

The client had issues with ensuring data consistency between multiple systems, including SAP and external databases. Some of the challenges were:

Complex data validation requirements, involving multilevel comparisons across different data sources.
Manual reconciliation efforts, which were time-consuming and prone to errors.
Lack of a standardized framework for defining and managing data validation rules, leading to inconsistencies.
Dependence on IT for developing alternative solutions making it difficult for data management teams to adapt validation logic to evolving needs.

These issues resulted in delays in data processing, inaccurate reporting, and inefficiencies in operations that relied on synchronized master data.

Solution

To overcome these challenges, the client implemented DataLark as a data validation platform. Key features included:

Automated Data Extraction: Seamlessly reading data from multiple sources, including SAP and external databases.
Flexible Data Mapping: Providing an intuitive interface for business users to define relationships between datasets without technical expertise.
Configurable Multilevel Validation Rules: Enabling comparison logic across different hierarchies, including product structures, pricing conditions, and supplier information.
User-Friendly Rule Management: Allowing master data teams to create, modify, and manage validation rules without IT involvement.
Deviation Reports and Alerts: Automatically identifying discrepancies and presenting them in easily understandable reports.

With DataLark, the client eliminated the need for custom development, enabling business users to maintain and adapt validation rules independently.

Results

With DataLark, the client established a scalable, efficient, and user-friendly approach to data validation, ensuring data accuracy across critical business processes.

70%

Reduction in Manual Reconciliation Efforts: Automated validation replaced labor-intensive data comparison, freeing up resources for higher-value tasks.

90%

Improvement in Data Accuracy: Faster identification and resolution of discrepancies ensured more reliable and consistent data.

50%

Increase in Business Agility: Master data teams could modify validation rules independently, reducing reliance on IT and accelerating response times.

Unlock the Power of Reliable Data Quality

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Ensure High-Quality, Reliable Data with DataLark

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Choose a Deployment Option that Better Suits Your Business Needs

Why DataLark is Essential for Data Quality Management

Trusted by Leaders in the Industry

Problem

Solution

65%

50%

40%

Problem

Solution

70%

90%

50%

Unlock the Power of Reliable Data Quality