Archive for category: Data Architecture

You are here: / / / Data Architecture

FORGET THE BUZZWORDS: WHAT SAP SAPPHIRE 2026 MEANS FOR THE FUTURE OF AUTONOMOUS DATA

/by

The Autonomous Enterprise isn’t a distant milestone on a roadmap anymore—it’s officially here, and it is entirely powered by unified, enterprise-grade data.

At SAP Sapphire 2026, SAP unveiled a massive vision for the future of business operations, centered squarely around the SAP Business AI Platform and the SAP Autonomous Suite.

For data leaders, enterprise architects, and business innovators, the event delivered a crystal-clear message: the next era of productivity isn’t just about playing around with standalone AI tools. It’s about embedding trusted data and autonomous agents directly into your core business workflows.

Here is a breakdown of the key takeaways from a data and AI perspective, and what they mean for your organization.

1. A Unified Data Foundation

The backbone of the new SAP Business AI Platform emphasizes executing workflows with absolute accuracy, compliance, and security.

The Reality Check: You cannot have a successful autonomous system without a pristine data layer.

True autonomy requires cross-domain data that is rigorously governed, secure, and ready for action. If your data foundation is fractured, your AI-driven decisions will be too.

2. Orchestration & Execution at Scale

One of the most compelling highlights of the event was the introduction of a three-tiered collaboration model:


This model shifts how we think about productivity. Data pipelines and business logic will increasingly run end-to-end autonomously via Joule Assistants and Agents. The result? Teams can finally pivot away from tedious, manual data processing and focus on high-value data strategy and critical decision-making.

3. Domain & Industry-Specific AI

If 2024 and 2025 taught us anything, it’s that generic, one-size-fits-all AI models aren’t enough for the enterprise.

SAP is tackling this by embedding domain-specific Joule Assistants and tailored Industry AI solutions directly into Finance, Supply Chain, and HR. This hyper-focused approach ensures that the unique context of your specific business data remains completely intact, leading to highly accurate, relevant, and compliant outcomes.

The Road Ahead: Treating Data as a Living Asset

The shift toward the Autonomous Enterprise means you can no longer view data as static rows in a database. It must be treated as a living, breathing asset that powers automated, connected, and governed systems.

As we step into this next chapter of AI-enabled work, the ultimate differentiator won’t just be the AI models you use—it will be the readiness of the data foundation you feed them.

CONTACT US

Over to You. How is your organization preparing its data foundation for autonomous agents? Are you ready for the next chapter of AI with SAP and beyond? Connect and let’s discuss your specific challenges.

Contact us and start our data conversation

BEYOND THE MODEL: WHY CONTEXT IS THE REAL KING OF GENERATIVE AI ROI

/by

Fresh back from the London Gartner Data & Analytics Summit, the crowded keynotes and deep-dive peer discussions left an unmistakable impression. While the last year was dominated by raw model sizes and parameter counts, the enterprise conversation has officially matured.

The overriding takeaway from the sessions, late-night workshops, and executive circles can be elegantly summed up in just three words: Context is King.

For years, the technology industry championed the phrase “Data is the new oil.” Yet, as we progress deeper into the age of Generative AI, we are discovering that raw data without structural framing, lineage, and business relevance is nothing more than expensive noise. The true AI ROI bottleneck isn’t a technological model problem; it is a structural data context problem. If you want large language models (LLMs) and intelligent agents to deliver authentic, repeatable enterprise value, they must inherently understand your unique business reality.

The New Bottom Line:

Models are rapidly becoming a commodity. Context is your true, non-replicable competitive advantage. An enterprise simply cannot scale reliable AI without a holistic, integrated framework to govern, clean, and enrich its entire data ecosystem. That foundational, trusted context layer is constructed where four crucial architectural pillars converge:

1. AI Governance: The Engine, Not the Brake

True governance is often misunderstood as a bureaucratic mechanism designed to slow things down. In reality, it acts as the engine. Robust “Governance for AI” delivers the crisp business definitions, clear lineage tracking, and strict access controls that supply models with the boundaries and trusted guardrails they require to operate safely, transparently, and effectively.

2. Unstructured Data Governance: Activating Hidden Knowledge

With the emergence of GenAI, unstructured data assets—such as complex contracts, internal documentation, and legacy files trapped in PDFs, Word documents, or presentations—are no longer passive text sitting idle in a repository. They are actively fueling LLMs. Implementing rigorous cataloging, metadata extraction, and privacy standards to unstructured formats is the only way to tap into this institutional context without risking devastating hallucinations or compliance leaks.

3. Data Quality (DQ): Garbage In, Hallucination Out

The foundational rule of computing, “garbage in, garbage out,” has magnified tenfold under modern AI architectures. High-quality, semantically enriched, and meticulously validated data is the ultimate dividing line separating an AI initiative that successfully scales into production from one that collapses due to downstream data unreadiness.

4. Master Data Management (MDM): The Anchor of Business Truth

To firmly anchor AI outputs in real-world business execution, your core operational entities—your customers, your products, and your vendors—must be crystal clear. Master Data Management provides the authoritative “golden records” that give an intelligent system its definitive source of truth and baseline operational context.

Moving Forward: From Insight to Execution

Success in this next era of digital transformation will not go to organizations that deploy the largest models, but to those who establish an unshakeable infrastructure for governing, cleaning, and contextualizing their data landscape.

Now that the summit has concluded, the real work begins. Let’s turn these collective insights into strategic execution. The race for context is on! 

CONTACT US

Exploring AI Governance? Want to scale AI safely with measurable impact?
Datalumen provides expert support to manage and control your AI agents and system across the full AI lifecycle, delivering near real-time visibility, trust signals and built-in complianc. Contact us to discover how we can help you succeed.

Contact us and start our data conversation

OPENLINEAGE: UNVEILING DATA LINEAGE FOR MODERN DATA ECOSYSTEMS

/by

Understanding the journey of data from its source to its final destination is crucial for businesses and organizations. This journey, known as data lineage, has become increasingly complex with the proliferation of data sources, transformation processes, and analytical tools. Enter OpenLineage, an open-source standard that aims to simplify and standardize data lineage tracking across diverse data ecosystems.

What is data lineage?

Data lineage is the process of tracing the journey of data from its origin to its destination, tracking every transformation, processing step, and the tools or systems it interacts with along the way. With data flowing through increasingly complex architectures, the ability to accurately map and understand these movements is vital for ensuring data quality, compliance, and operational efficiency.

However, tracking data lineage is no small feat, especially with the explosion of data sources, analytics platforms, and transformation tools that make up modern data stacks.


What is OpenLineage?

OpenLineage is an open standard for data lineage collection and analysis. Initiated by Datakin and now part of the Linux Foundation, OpenLineage provides a set of standardized definitions and APIs that allow different tools and platforms in the data ecosystem to share lineage metadata in a consistent format.

The primary goal of OpenLineage is to create a unified approach to collecting and utilizing data lineage information. By establishing a common language for data lineage, OpenLineage enables better interoperability between various data tools, platforms, and processes.


Key Components of OpenLineage

  1. OpenLineage Specification: This defines the core concepts and data model for representing lineage metadata. It includes definitions for jobs, datasets, runs, and the relationships between them.
  2. Integration Libraries: OpenLineage provides libraries and SDKs for popular data processing frameworks like Apache Spark, Apache Airflow, and dbt. These integrations allow developers to easily instrument their data pipelines to emit lineage events.
  3. API: The OpenLineage API defines how lineage events should be structured and transmitted. This standardization ensures that all tools speaking the OpenLineage language can understand and process lineage data consistently.
  4. Facets: These are extensible metadata attributes that can be attached to core OpenLineage entities, allowing for custom metadata to be included in lineage information.



Why should I care about this?

Standardization and InteroperabilityOne of the most significant advantages of OpenLineage is its ability to standardize lineage data across different tools and platforms. This standardization enables seamless integration between various components of a data stack, from data ingestion tools to transformation engines and analytics platforms. As a result, organizations can build a comprehensive view of their data lineage without being locked into a single vendor or tool.
Enhanced Data Governance and ComplianceWith the increasing importance of data privacy regulations like GDPR and the AI Act, understanding data lineage is crucial for compliance. OpenLineage makes it easier to track the flow of sensitive data across systems, helping organizations ensure that data is handled in accordance with regulatory requirements. This comprehensive lineage information also aids in auditing processes and demonstrating compliance to regulatory bodies.
Improved TrustBy providing visibility into the entire data pipeline, OpenLineage helps data teams identify and resolve data quality issues more efficiently. When inconsistencies or errors are discovered, teams can quickly trace the problem back to its source, understanding all the transformations and processes the data has undergone. This transparency builds trust in the data and the insights derived from it.
Efficient Troubleshooting and DebuggingWhen issues arise in data pipelines or analytics, OpenLineage’s detailed lineage information becomes invaluable. Data engineers and analysts can trace the path of data through various systems, identifying where problems may have occurred. This capability significantly reduces the time and effort required for troubleshooting, leading to faster resolution of data-related issues.
Support for Data Cataloging and Metadata ManagementOpenLineage integrates seamlessly with data catalogs and metadata management tools. By providing rich lineage information, it enhances the capabilities of these tools, allowing for more comprehensive documentation of data assets. This integration supports better data discovery, understanding, and utilization across the organization.

Conclusion

OpenLineage represents a significant step forward in the field of data lineage and metadata management. By providing a standardized, open-source approach to tracking data lineage, it addresses many of the challenges faced by modern data-driven organizations. From improving data governance and quality to enhancing troubleshooting capabilities and fostering collaboration, OpenLineage offers a wide range of benefits.

As data ecosystems continue to grow in complexity, tools like OpenLineage will become increasingly crucial. Organizations that adopt OpenLineage can expect to gain a competitive edge through better data management, increased efficiency, and improved data-driven decision-making capabilities.

The open nature of the project ensures that it will continue to evolve and improve, driven by the needs of the data community. As more tools and platforms adopt the OpenLineage standard, we can expect to see even greater interoperability and capabilities in the future of data lineage tracking.

 

 

 

CONTACT US

Need expert support with your data agenda? Discover how Datalumen can help you. 

Contact us and start our data conversation




DEMYSTIFYING DATA PRODUCTS: A DATA GAME CHANGER YOU CAN’T IGNORE

/by

    The world of data management is undergoing a transformation. While some traditional methods had limitations, the concept of data mesh is paving the way for a more effective approach.  In this article, we dive into the concept of data products, a important element of the data mesh approach and we explore its key characteristics.

    What are They & What makes Them Different?

    Think of data products as self-contained information packages designed to address specific business challenges. They can be used internally or externally and come in various forms, from simple database tables to complex machine learning models.

    Here are some real-world examples:
    • A customer 360 that unifies data from sales, marketing, and customer service departments.
    • A pre-built report with a user-friendly interface for sales & marketing teams to analyze customer trends.
    • A machine learning model for predicting customer churn, embedded within a CRM platform.

    They go beyond just delivering raw data and focus on the entire data lifecycle, from understanding the user needs to ensuring proper data quality and security. Traditional data management focused primarily on the technical aspects of data creation and delivery. Data products on the other hand, emphasize the user experience and business value of data, adopting a “product thinking” mentality.

    Key Characteristics

    Data Products - Key Characteristics

     

    Building meaninful products requires a data team with diverse expertise. Next to the expertise, here are some essential characteristics to consider:

    1. Discoverability & Data Collection: Users should be able to easily find and understand available data products. Data registries with detailed descriptions and metadata are crucial.
    2. Observability: Data is constantly changing. They should be equipped with tools to detect and address anomalies promptly, ensuring ongoing reliability.
    3. Quality: Trustworthy data is paramount. They should leverage robust quality control measures to ensure accurate and reliable information.
    4. Consumability: Making your data consumable & insightful in an easy and flexible way is key. This doesn’t only apply on the development but also the presentation.   
    5. Security: Data security is especially important in a self-service analytics environment. Access controls and adherence to data privacy regulations are vital.
    6. Process: Streamlining the data product development process is key. DataOps practices, including automation and continuous integration & improvement, can accelerate delivery.

    Benefits

    By implementing data products, organizations can expect several advantages:

    • Increased data utilization: Discoverable and user-friendly data products encourage broader data consumption.
    • Improved decision-making: Data-driven insights empower businesses to make informed choices.
    • Enhanced agility: Faster development and deployment of data products lead to quicker adaptation.
    • Potential for monetization: Certain data products can be valuable assets for external use.

    Conclusion

    Data products are revolutionizing data management by transforming data into readily consumable information. By focusing on user needs, quality, and operational efficiency, companies can leverage them to unlock new levels of business success. If your organization is looking to gain a competitive edge through data-driven decision-making, then embracing this approach is a powerful step forward.

    CONTACT US

    Want to modernize your Data Architecture? Discover how Datalumen can help you getting there. 

     

    Contact us and start our data conversation




    DEFENSIVE DATA MASTERY: THE LINES OF DEFENSE TO SHIELD YOUR DATA

    /by

    Last week it was made public that the personal information of 33 million French citizens could be exposed after two French health insurance operators suffered a data breach early February. Unfortunately, this isn’t an isolated incident. Other recent breaches occurred at i.e. EuroParcs, Air France KLM, JD Sports, T-Mobile, Sony, Cloudflare, …

    Cybersecurity goes beyond network, application and endpoint security. Especially in today’s digital age, where data is the lifeblood of organizations, safeguarding sensitive information has become paramount. As organizations amass vast amounts of data, protecting it from unauthorized access, breaches, and misuse has become a complex challenge. In this context, implementing robust lines of defense through techniques such as data masking, data encryption, data  security gateway and data governance policy management is crucial to fortify an organization’s data management strategy.
     

    Data Masking: Concealing the Vulnerabilities

     

    What is Data Masking?

    Data masking involves the transformation of sensitive information within a database, making it unintelligible to unauthorized users. The primary objective is to protect sensitive data while maintaining its usability for testing and analytics and overall usage in your processes.

     

    The Defense Mechanism:

    Data masking acts as the first line of defense by obscuring sensitive data such as personal identifiers, financial details, or confidential business information. This ensures that even if unauthorized access occurs, the exposed information is rendered useless and non-identifiable. This mechanism can also be useful in the context of specific compliancy driven initiatives such as i.e. GDPR.

     

    Data Encryption: Securing the Data Source

     

    What is Data Encryption?

    Data encryption is the process of converting plain text into ciphertext, making it unreadable without the appropriate decryption key. It is a fundamental technique in securing data during transmission and storage.

     

    The Defense Mechanism:

    By implementing data encryption, organizations create a robust barrier against unauthorized access to sensitive information. It safeguards data in transit, preventing interception and tampering, and protects stored data from being deciphered by unauthorized entities. This mechanism can also be useful in case your infrastructure is outsources to a third party. Depending on the setup, even internal or external IT personal doesn’t have access to encrypted data.

     

    Data Security Gateway: Active Control for Holistic Protection

     

    What is a Data Security Gateway based on Data Virtualization?

    A Data Security Gateway based on Data Virtualization acts as a centralized control point for securing data access, ensuring that only authorized users can retrieve and interact with sensitive information based on their role or profile.

     

    The Defense Mechanism:

     

    By implementing a Data Security Gateway, organizations gain real-time visibility into data access and usage. This proactive approach allows for immediate detection and response to potential threats, providing an additional layer of defense alongside masking, encryption, and governance.

     

    The security layer of the data virtualization platform not only offers extra authentication functionality but also offers row, column and even cell-level security. With this approach you can enforce a security layer that is more strict than the underlying data sources.

     

    Data Access Governance: Establishing Regulatory Compliance

     

    What is Data Access Governance?

    Data governance policy management involves defining and enforcing policies that dictate how data is collected, stored, processed, and shared within an organization. It provides a structured framework for managing data assets and allows you to easily create data access policies with a few clicks and preview them before they’re implemented. 

     

    The Defense Mechanism:

    Data governance policy management acts as the overarching defense strategy, ensuring that data is handled in accordance with regulatory requirements and internal standards. By establishing clear guidelines and enforcing policies, organizations mitigate risks associated with data breaches and non-compliance. Depending on the technology this can be enabled with a no-code approach to configure and execute a policy in a matter of minutes accross .

     

     

    Conclusion: Integrating Defense Mechanisms for Holistic Protection

    While each technique offers a specific layer of defense, their true strength lies in their integration. Data masking, encryption, data security gateways and governance policy management work synergistically to create a comprehensive and resilient data protection strategy.

    By combining these techniques, organizations not only mitigate the risk of data breaches but also ensure compliance with industry regulations and standards. This is crucial in maintaining the trust of customers and stakeholders and avoiding legal repercussions.

    By adopting the combination of these techniques, businesses can fortify their data management practices, instill confidence in stakeholders, and navigate the digital data landscape with resilience and security.

    CONTACT US

    Interested in elevating your data security to the necessary standards? Discover how Datalumen can assist you in achieving this goal. 

     

    Contact us and start our data conversation



    KEY TAKEAWAYS OF THE LATEST FORRESTER WAVE ON DATA GOVERNANCE SOLUTIONS

    /by

    Data Governance as the Foundation for Data Democratization. The data governance solution market has evolved from an emerging space to a mature one, with core capabilities including data catalogs, policy management, and privacy and compliance modules. The focus now is on elevating data governance to a strategic capability, enabling organizations to move to a federated model for trusted data. The market emphasis has shifted from regulatory compliance to realizing the full potential of data for business insights.

     

    TOP3 Key considerations for data governance solution customers include:

    1. Stable Foundation for AI:
      Providers should offer a stable foundation for trusted data intelligence for AI. This involves implementing robust data lineage, versatile access controls, and comprehensive data quality checks. Privacy compliance and security policies are used to ensure data reliability, integrity, and quality for AI applications.
    2. Support for Stewardship Functions:
      Data stewards require solutions with intuitive workflows and advanced collaboration features. This includes seamless user experiences and modern collaboration tools to ensure accurate ownership and flexible cocreation. Robust stewardship capabilities directly impact efficiency, streamline governance processes, and reduce the time and effort needed for data stewardship tasks.
    3. Empower Data Democratization:
      Organizations should prioritize providers that enhance data literacy and enable self-service data access. Solutions with advanced glossary capabilities and granular personalization options support a wide range of personas in accessing, understanding, transforming, and personalizing data. Self-service capabilities with intuitive embedded controls expedite data access, reducing dependence on IT departments and eliminating the need for business users to possess specialized technical knowledge.


    Conclusion

    In summary, the current state of the data governance market is marked by a shift towards strategic capabilities, focusing on AI-driven decision-making, efficient stewardship functions, and empowering data democratization for enhanced business value creation.

    CONTACT US

    Want more info about the latest Data Governance Solutions from vendors like Collibra, Allation, Informatica or others? Let us help you unravel the pros and cons, ensuring a perfect match for your needs. Don’t miss out – kickstart the conversation now!


     

    Contact us and start our data conversation

    DATA LINEAGE. THE CRYSTAL BALL YOU ALWAYS HAVE BEEN LOOKING FOR?

    /by

    Data is king. Businesses rely on data to make decisions, understand their customers, and improve their operations. However, in complex organizations, it can be difficult to get insight into relevant data flows.

    There are a number of reasons why this is the case. First, complex organizations often have a large number of data sources. This can make it difficult to track the movement of data between different systems and applications. Second, data flows in complex organizations can be complex and dynamic. This means that the path that data takes can change over time, making it difficult to keep track of. Third, data in complex organizations is often siloed. This means that data is stored in different systems and applications, making it difficult to get a holistic view of the data.

    As a result of these challenges, it can be difficult to get insight into data flows in complex organizations. This can make it difficult to ensure the quality of data, comply with regulations, and make informed decisions. Despite these challenges, there are a number of things that organizations can do to get insight into data flows. Data lineage is typically a fundamental capability.

    What is Data Lineage?

    Data lineage is the tracking of data as it flows through an organization’s systems and processes. It provides a detailed record of the data’s origin, transformation, and destination. Data lineage is important for a number of reasons, including:

    • Data quality: Data lineage can help to ensure the quality of data by providing a way to track its history and identify any potential problems. For example, if a data point changes unexpectedly, data lineage can be used to trace back the change and identify the source of the problem.
    • Compliance: Data lineage can help organizations to comply with regulations that require them to track the movement of data. For example, the European Union’s General Data Protection Regulation (GDPR) requires organizations to track the personal data of their customers. Data lineage can help organizations to meet this requirement by providing a record of how personal data is collected, used, and stored.
    • Auditing: Data lineage can be used to audit data flows and identify potential security risks. For example, if an organization is concerned about the possibility of a data breach, data lineage can be used to track the movement of sensitive data and identify any potential vulnerabilities.
    • Business intelligence: Data lineage can help organizations to gain insights into their data by providing a way to track the relationships between different data sets. For example, data lineage can be used to identify which data sets are used to calculate a particular metric. This information can then be used to improve the accuracy of the metric and make better business decisions.

    Benefits

    • Improved data governance: Data lineage can help organizations to improve their data governance practices by providing a way to track the ownership, access, and usage of data. This information can then be used to develop and enforce data policies and procedures.
    • Reduced risk of data loss: Data lineage can help to reduce the risk of data loss by providing a way to track the location of data. This information can then be used to recover data in the event of a loss or corruption.
    • Increased data agility: Data lineage can help organizations to become more data agile by providing a way to track the changes to data. This information can then be used to update data models and applications as needed.

    How to Implement Data Lineage

    There are a number of ways to implement data lineage. One way is to use a data lineage tool. These tools can help to automate the tracking of data flows and provide a visual representation of data lineage. Another way to implement data lineage is to manually track data flows. This can be done by creating spreadsheets or diagrams that track the movement of data.

    The best way to implement data lineage will depend on the specific needs of the organization. However, all organizations should consider implementing data lineage to improve the quality and usability of their data.

    A sample data lineage diagram illustrating the high-level data flow between a Tableau Workbook and a Snowflake datawarehouse.


    Conclusion

    In summary, data lineage is an important tool for organizations that want to ensure the quality, compliance, security, and usability of their data. By tracking the movement of data, data lineage can help organizations to identify and address problems, comply with regulations, and gain insights into their data.


    CONTACT US

    Also want to take your data agenda to the next level? Would you like to find out how Datalumen can help?
     

    Contact us and start our data conversation

    BUSINESS GLOSSARY VS DATA CATALOG: A TALE OF TWO CITIES?

    /by

    Business glossaries and data catalogs play vital roles within data management. They are essential components in virtually any data architecture, but their purposes and interconnections are not always clear to everyone and as such worth exploring.

    Exploring the relationships

    A business glossary and a data catalog are closely related components within the field of data management. They both serve the purpose of organizing and documenting information about data assets within an organization, but they focus on different aspects.

    Business Glossaries – Establishing the common language

    A business glossary is a centralized repository or collection of terms and definitions that are specific to the organization’s business domain. It provides a common understanding and consistent definition of business terms used across different departments and stakeholders. The business glossary helps ensure clear communication and alignment between business users, data professionals, and technical teams by establishing a shared vocabulary.

    Data Catalogs – Unveiling the data landscape

    On the other hand, a data catalog is a comprehensive inventory of the available data assets within an organization. It provides detailed information about the structure, content, and characteristics of each dataset or data source. A data catalog captures metadata about the data, including data lineage, data sources, data quality, and other relevant information. It serves as a valuable reference for data consumers, data analysts, and data scientists to discover, understand, and effectively utilize the available data assets.

    Complementary forces

    The link between a business glossary and a data catalog lies in their complementary roles in facilitating data understanding and governance. While the business glossary focuses on defining business terms and ensuring consistent business vocabulary, the data catalog provides technical information about the underlying data assets. The business glossary helps users interpret and understand the data catalog by providing clear definitions of the business terms used in the metadata descriptions. In turn, the data catalog helps enrich the business glossary by associating technical metadata with the corresponding business terms, enhancing the overall understanding of the data assets and their context within the organization.

    By integrating a business glossary with a data catalog, organizations can bridge the gap between business and technical perspectives, fostering better collaboration, data governance, and data-driven decision-making.


    CONTACT US

    Also want to take your data agenda to the next level? Would you like to find out how Datalumen can help?
     

    Contact us and start our data conversation

    SAP DATASPHERE: GAME-CHANGING LEAP WITH COLLIBRA, CONFLUENT, DATABRICKS & DATAROBOT PARTNERSHIPS

    /by

    What is the SAP Datasphere announcement all about?

    SAP has unveiled the SAP Datasphere solution, the latest iteration of its data management portfolio that simplifies customer access to business-ready data across their data landscape. In addition, SAP has formed strategic partnerships with leading data and AI companies such as Collibra, Confluent, Databricks and DataRobot to enrich the SAP Datasphere and help organizations develop a unified data architecture that securely combines SAP and non-SAP data.


    What is SAP Datasphere?

    SAP Datasphere is a comprehensive data service that delivers seamless and scalable access to mission-critical business data and is in essence the next generation of SAP Data Warehouse Cloud. SAP has kept all the capabilities of SAP Data Warehouse Cloud and added newly available data integration, data cataloging, and semantic modeling features, which we will continue to build on in the future. More info on the official SAP Datasphere solution page.



    Why does this matter to you?

    The announcement is significant because it eliminates the complexity associated with accessing and using data from disparate systems and locations, spanning cloud providers, data vendors, and on-premise systems. Customers have traditionally had to extract data from original sources and export it to a central location, losing critical business context along the way and needing dedicated IT projects and manual effort to recapture it. With SAP Datasphere, customers can create a business data fabric architecture that quickly delivers meaningful data with the business context and logic intact, thereby eliminating the hidden data tax.

    As a solution partner in this ecosystem, we are excited about the collaboration and the added value it provides:

    • With Collibra SAP customers can deliver an end-to-end view of a modern data stack across both SAP and non-SAP systems, enabling them to deliver accurate and trusted data for every use, every user, and across every source.
    • Confluent and SAP are working together to make it easier than ever to connect SAP software data to external data with Confluent in real-time to power meaningful customer experiences and business operations.
    • Databricks and SAP share a vision to simplify analytics and AI with a unified data lakehouse, enabling them to share data while preserving critical business context.
    • DataRobot and SAP’s joint customers can now also leverage machine learning models trained on their business data with speed and scale to see value faster, using the SAP Datasphere as the foundation layer.

    CONTACT US

    Also want to understand how you can take your SAP and non-SAP data to the next level? Would you like to find out how Datalumen can help?
     

    Contact us and start our data conversation..




    TO CURE OR TO OBSERVE? HOW DATA OBSERVABILITY DIFFERS FROM DATA CURATION

    /by

    In the world of data management, there are many terms and concepts that can be confusing. Two such concepts are data observability and data curation. While both are important for ensuring data accuracy and reliability, they have distinct differences. In this article, we will explore the key differences between data observability and data curation.

    What is Data Observability?

    Data observability refers to the ability to monitor and understand the behavior of data in real-time. It is the process of tracking, collecting, and analyzing data to identify any anomalies or issues. Data observability is often used in the context of monitoring data pipelines, where it can be used to identify issues such as data loss, data corruption, or unexpected changes in data patterns.

    Data observability relies on metrics, logs, and other data sources to provide visibility into the behavior of data. By analyzing this data, it is possible to identify patterns and trends that can be used to optimize data pipelines and improve data quality.

    What is Data Curation?

    Data curation, on the other hand, refers to the process of managing and maintaining data over its entire lifecycle. It is the process of collecting, organizing, and managing data to ensure its accuracy, completeness, and reliability. Data curation involves tasks such as data cleaning, data validation, and data enrichment.

    Data curation is essential for ensuring that data is accurate and reliable. It involves the use of automated tools and manual processes to ensure that data is properly labeled, formatted, and stored. Data curation is particularly important for organizations that rely heavily on data analytics, as inaccurate or incomplete data can lead to faulty insights and poor decision-making.

    Key Differences Between Data Observability and Data Curation

    While data observability and data curation share some similarities, there are key differences between the two concepts. The main differences are as follows:

    • Focus: Data observability focuses on monitoring data in real-time, while data curation focuses on managing data over its entire lifecycle.

    • Purpose: Data observability is used to identify and troubleshoot issues in data pipelines, while data curation is used to ensure data accuracy and reliability.

    • Approach: Data observability relies on monitoring tools and real-time analysis, while data curation relies on automated tools and manual processes.

    Conclusion

    In summary, data observability and data curation are two important concepts in the world of data management. While they share some similarities, they have distinct differences. Data observability is focused on real-time monitoring and troubleshooting, while data curation is focused on ensuring data accuracy and reliability over its entire lifecycle. Both concepts are important for ensuring that data is accurate, reliable, and useful for making informed decisions.

    CONTACT US

    Also in need for a modern data approach to boost your data quality management strategy? Would you like to find out how Datalumen can help?  Contact us and start our data conversation.