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UNLOCK REAL-TIME DATA TRUST – LAUNDERING DATA FOR QUALITY AND OBSERVABILITY WITH COLLIBRA & SAP

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To address the complexities of data quality management in SAP environments, Collibra has launched its Data Quality & Observability with Pushdown solution, focusing on SAP HANA, HANA Cloud, and Datasphere systems. This integration brings data quality monitoring directly within SAP, allowing businesses to streamline processes by handling data quality checks at the source.

Let’s have a closer look at how this capability enhances performance and reliability in SAP environments.


 

Key Benefits of the Collibra-SAP Integration

  1. Pushdown Technology for Performance Efficiency
    The integration leverages pushdown processing, meaning data quality rules are applied directly in SAP systems rather than transferring data to external platforms. This approach reduces data movement and improves processing speeds, which is critical for large datasets.

  2. Real-Time Observability and Machine Learning Rules
    Observability is key to maintaining trusted data. Collibra’s solution employs machine learning algorithms to detect data quality issues as they arise. By keeping quality checks within SAP, data teams get real-time insights into data integrity and can promptly address issues, preventing faulty data from propagating through systems.

  3. Improved Resource Efficiency
    By eliminating the need to transfer data externally, organizations can reduce infrastructure costs and increase operational efficiency. This efficiency is particularly valuable for enterprises with complex SAP environments that require extensive data processing.

  4. Enhanced Data-Driven Decision Making
    Reliable, accurate data enables organizations to make faster, data-driven decisions. With integrated quality monitoring, teams can trust the data they rely on for analytics and reporting, leading to better-informed business strategies.

Unlocking Potential for SAP + Data-Intensive Organizations

Organizations heavily invested in SAP can benefit significantly from Collibra’s integrated observability. This solution is especially valuable for those aiming to scale data-driven initiatives while minimizing overhead and maximizing data reliability. By focusing on data quality at the source, Collibra ensures that organizations can effectively manage their data’s integrity, performance, and trustworthiness.

 

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Also an SAP customer with the need to get a better grip on your data quality & data governance? Discover how Datalumen can help you. 


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AUGMENTED DATA QUALITY: AN AI-FUELED APPROACH FOR YOUR DATA ZEN MOMENT

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    Data’s effectiveness hinges on its quality and here’s where Augmented Data Quality (ADQ) steps in, revolutionizing how we ensure our information assets are accurate, reliable, and ready to use.

    Traditional Data Quality: A Manual Marathon

    For years, data quality relied on automated but nevertheless manual processes. Data stewards meticulously combed through datasets, identifying and correcting errors like inconsistencies, missing values, and formatting issues. This painstaking approach, while crucial, becomes increasingly inefficient as data volumes explode.

    Augmented Data Quality: AI-Powered Efficiency

    Augmented Data Quality tackles this challenge head-on by leveraging artificial intelligence (AI) and machine learning (ML). These powerful tools automate data quality tasks, freeing up human experts for more strategic endeavors.

    Here’s how ADQ makes a difference:

    • Automated anomaly detection: AI algorithms can scan huge datasets, pinpointing anomalies and potential errors that might escape manual analysis.
    • Intelligent data cleansing: ADQ can suggest corrections for identified issues, streamlining the cleaning process. Machine learning even allows the system to “learn” from past corrections, continuously improving its accuracy.
    • Proactive monitoring: ADQ can be configured for real-time monitoring, enabling early detection and rectification of data quality issues before they impact downstream processes.

    Benefits Beyond Efficiency

    The advantages of ADQ extend far beyond simply saving time and resources. Here’s what organizations can expect:

    • Enhanced data trust: ADQ fosters a culture of data trust within an organization. With a high degree of confidence in data quality, employees across departments can make informed decisions based on reliable information.
    • Improved decision-making: Clean, accurate data leads to better insights. ADQ empowers businesses to leverage data for strategic planning, risk management, and optimized operations.
    • Reduced costs: Data quality issues can lead to costly rework and missed opportunities. ADQ proactively addresses these challenges, minimizing associated costs.

    Conclusion

    ADQ represents a significant step forward in data management. By harnessing the power of AI and automation, organizations can unlock the full potential of their data assets. As data continues to be the cornerstone of success, ADQ will be a critical differentiator for businesses that prioritize reliable information and data-driven decision making.



    CONTACT US

    In need for support with your Data Quality initiatives? Discover how Datalumen can help you getting there. 

     

    Contact us and start our data conversation