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

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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.


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ACTIVE METADATA. SIGNIFICANT TO HELP YOU UNDERSTAND, IMPROVE AND ACTUALLY USE YOUR DATA?

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In the realm of data, metadata is widely recognized as the “data about data” – the vital information that characterizes its structure, meaning, and utility. Metadata serves as a crucial element in data management, enhancing accessibility and comprehension.

Among various metadata types, active metadata stands out as a dynamic and continuously updated entity that informs decisions and drives actions. It surpasses the notion of a static data description; instead, it thrives as a dynamic resource, revolutionizing our interaction with data.

Why Do You Need Active Metadata?

In our data-centric world, organizations generate an unprecedented volume of data. While this data holds immense value, managing it poses challenges. Active metadata emerges as an instrumental asset, empowering businesses to:

  1. Unearth deeper data insights: Active metadata provides businesses with comprehensive comprehension of their data, enabling the identification of patterns, trends, and relationships.
  2. Enhance data quality: Active metadata plays a pivotal role in elevating data quality. By pinpointing and rectifying errors, as well as ensuring data consistency and currency, businesses can significantly improve their data assets.
  3. Foster data accessibility: Active metadata promotes seamless access to data by providing users with comprehensive information and simplifying data search and utilization.
  4. Automate data-related tasks: Leveraging active metadata, businesses can automate data management tasks, freeing up valuable time for users to focus on critical endeavors. This, in turn, enhances accuracy and efficiency.

How Does Active Metadata Work?

Active metadata finds its home in a metadata repository, which can exist as a physical or virtual database hosted either on-premises or in the cloud. The repository remains interconnected with data sources, continuously monitoring data alterations. This invaluable information is then employed to update the active metadata.

The applications of active metadata are diverse. Here are a few examples:

  1. Enforcing data quality standards: Active metadata empowers the enforcement of data quality rules, ensuring utmost accuracy and consistency.
  2. Generating comprehensive reports: Active metadata serves as the foundation for generating detailed reports on data usage, allowing businesses to track patterns, trends, and usage metrics.
  3. Empowering analytical capabilities: Active metadata fuels cutting-edge analytics tools, enabling businesses to extract profound insights from their data and make well-informed decisions.

Conclusion

Active metadata emerges as a potent resource, empowering businesses to manage their data effectively. By gaining deeper insights, enhancing data quality, and fostering accessibility, active metadata equips enterprises with the ability to unlock the true potential of their data and make informed decisions.


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WHEN IS GOOD, GOOD ENOUGH? THE BALANCING ACT TO GET TO THE RIGHT DATA QUALITY LEVEL

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In the age of data-driven decision making, organizations face the challenge of determining when data quality is sufficient for their needs. Striking the right balance between investing resources in improving data quality and achieving an acceptable level of accuracy and reliability is crucial. In this article, we give you some step-by-step handles to help organizations assess and establish the appropriate data quality level.

Step 1: Define Data Quality Requirements

The first step in determining the right data quality level is to define clear and specific requirements. Take the time to understand your organization’s goals, objectives, and the decisions that will be based on the data. Identify the key dimensions of data quality that matter most to your organization, such as accuracy, completeness, consistency, timeliness, and relevancy. Defining these requirements will serve as a guide for assessing data quality.

Step 2: Evaluate Data Use Cases

Next, evaluate the different use cases and scenarios where data will be utilized. Each use case may have varying requirements and tolerance levels for data quality. Analyze the potential impact of data errors or inaccuracies on the decisions made in each specific use case. This evaluation will help prioritize the allocation of resources and efforts towards improving data quality where it matters the most.

Step 3: Assess Data Collection and Processing Methods

Evaluate the data collection and processing methods employed by your organization. Examine the data sources, collection processes, and data transformation steps. Identify potential bottlenecks, vulnerabilities, and areas where errors or inaccuracies could be introduced. Streamline the data collection process and implement quality checks at each step to ensure the integrity and reliability of the data.

Step 4: Implement Data Quality Controls

To ensure data quality is at an acceptable level, implement data quality controls throughout the data lifecycle. This includes setting up validation rules, data cleansing routines, and data profiling techniques. Establish automated checks to identify and rectify data anomalies, outliers, and inconsistencies. Leverage technology and tools to automate these processes and minimize human errors.

Step 5: Measure Data Quality

Establish data quality metrics that align with your defined requirements. These metrics may include error rates, completeness percentages, timeliness measures, or any other specific indicators relevant to your organization. Implement mechanisms to measure and monitor data quality regularly. Leverage statistical analysis, data profiling, and data visualization techniques to gain insights into the overall quality level and identify areas for improvement.

Step 6: Set Tolerance Levels

Define tolerance levels for data quality based on the specific use cases and requirements of your organization. Determine the acceptable margin of error for each use case. Consider factors such as the criticality of the decision being made, the potential impact of data errors, and the costs associated with improving data quality. Establishing tolerance levels will help determine when data quality is good enough to support the decision-making process effectively.

Step 7: Continuous Improvement

Data quality is an ongoing process that requires continuous monitoring and improvement. Regularly review the established metrics and tolerance levels. Evaluate feedback from data consumers and stakeholders to identify areas for enhancement. Invest in training and education programs to improve data literacy within the organization. By fostering a culture of continuous improvement, you can ensure that data quality is consistently enhanced over time.

Conclusion

Determining the right data quality level is a balancing act for organizations seeking to optimize resources while maintaining reliable insights. By following a structured methodology, including defining data quality requirements, evaluating use cases, assessing data collection and processing methods, implementing data quality controls, measuring data quality, setting tolerance levels, and embracing continuous improvement, organizations can strike the right balance. Achieving the right data quality level will provide confidence in the decision-making process, leading to better business outcomes and a competitive advantage in the data-driven landscape.

 

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A ROBUST DATA GOVERNANCE FRAMEWORK: YOUR BEST PROTECTION AGAINST THE PERIL OF A DATA HOUSE OF CARDS

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Data needs to be effectively managed, protected, and leveraged to derive valuable insights. And that is where data governance frameworks come into play. Data governance frameworks provide a structured approach to establishing policies, processes, and guidelines for data management, ensuring data integrity, privacy, and compliance. In this article, we will delve into the importance of data governance frameworks and explore key components that contribute to their success.

Why Data Governance Frameworks Matter

Data governance frameworks can serve as the backbone for organizations to establish a holistic and consistent approach to managing their data assets. Here are a few reasons why data governance frameworks matter:

  1. Data Quality and Integrity: Data governance frameworks emphasize the importance of data quality, ensuring that data is accurate, consistent, and reliable. By implementing data governance practices, organizations can improve the overall integrity of their data assets, reducing errors and enhancing decision-making capabilities.
  2. Compliance and Risk Management: With an increasing number of data protection regulations, such as GDPR and CCPA, organizations face stringent requirements for data privacy and security. Data governance frameworks help establish policies and procedures to ensure compliance, mitigate risks, and protect sensitive information from unauthorized access or breaches.
  3. Stakeholder Collaboration: Data governance frameworks foster collaboration among stakeholders across the organization. By defining roles, responsibilities, and processes, these frameworks enable effective communication, coordination, and decision-making regarding data management practices.

    Key Components of Data Governance Frameworks

    While data governance frameworks may vary depending on organizational needs, several key components contribute to their effectiveness:

    1. Data Governance Policies: Policies act as the foundation of data governance frameworks, defining the principles and guidelines for data management. These policies address data ownership, data classification, data sharing, privacy, and security measures. They provide a clear direction for employees on how to handle data and ensure compliance with regulatory requirements.
    2. Data Stewardship: Data stewardship focuses on the responsibility of individuals or teams within the organization to manage and govern data effectively. It involves assigning data stewardship roles and responsibilities, establishing data quality standards, and monitoring data integrity throughout its lifecycle. Data stewards act as custodians of data, ensuring its accuracy, availability, and appropriate use.
    3. Data Classification and Categorization: Data governance frameworks include mechanisms for classifying and categorizing data based on its sensitivity, criticality, and usage. This enables organizations to apply appropriate security controls, access restrictions, and retention policies. Data classification also aids in identifying and prioritizing data governance requirements based on the value and risk associated with different data types.
    4. Data Lifecycle Management: Effective data governance frameworks encompass the entire data lifecycle, from data creation to archiving or disposal. They define processes for data collection, storage, integration, analysis, and archival, ensuring data is managed consistently and securely throughout its journey. This includes data quality assessment, metadata management, data retention policies, and data archiving strategies.
    5. Data Privacy and Security: Data governance frameworks address privacy and security concerns by establishing policies and practices to safeguard sensitive information. This includes implementing access controls, encryption techniques, data anonymization, and monitoring mechanisms to detect and respond to data breaches or unauthorized access attempts.

     

    Conclusion

    Data governance frameworks are indispensable for organizations seeking to manage data effectively, mitigate risks, and comply with data protection regulations. By establishing clear policies, roles, and processes, organizations can ensure data quality, integrity, and privacy while enabling collaboration and decision-making across the enterprise. It is crucial for organizations to tailor their data governance frameworks to their specific needs and continually evolve them to adapt to changing data landscapes and emerging challenges. With a robust data governance framework in place, organizations can build a solid foundation for leveraging the power of data.


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

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    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.


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    TOP DATA OBSERVABILITY & DATA QUALITY TRENDS TO PUT ON YOUR RADAR

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    Data quality management is a critical component in the successful realization of your data strategy, and there are several hot topics that are currently gaining traction in this area. Here are some of the latest trends in data quality & data observability:

    • DQaaS or DQSaaS
    • Hybrid Usage & Island Hopping.
    • Machine Learning has hit the DQ space.
    • From patchwork to a fundamental capability.
    • From a technology to a business driven framework.


    TREND #1. DQaaS or DQSaaS

    Data quality as a service (DQaaS or DQSaaS) is an emerging trend that involves outsourcing a subset of data quality management functionality to third-party cloud application providers. DQaaS providers offer tools and services to monitor and improve data quality, reducing the workload on in-house data teams. In general, SaaS is provided in a cloud-based or hosted model.

    We see two types of DQaaS:
    • One type basically providing a complete set of data quality functionalities (equivalent to traditional on-premise offerings) that run on cloud platforms. Clients typically order them on demand from cloud enabled vendors and often use them on a subscription basis (1 year or longer).
    • The other type of DQaaS is basically based on on-demand online services to i.e. validate and verify addresses or other relevant data assets. These micro data quality services are typically used on a pay per usage / service-call basis.

    TREND #2. Hybrid Usage & Island Hopping

    Until recently, Data Quality was primarily applied in an operational/transactional context. DQ in an analytics context was also already implemented in quiet some organization, but what is relative new is the enhanced DQ usage in a number of other data related initiatives such as MDM, DG, Data Engineering and AI/ML. What we also see as part of that move, is a shift from an island DQ usage tailored towards one specific initiative to an organization-wide DQ usage. The organization-wide DQ approach provides a number of benefits ranging from more consistent data quality up to enhanced collaboration, more efficient data processing and reuse.

    TREND #3. Machine Learning has hit the DQ space

    We don’t need to explain you that AI/ML is hot and all over the place. The AI/ML examples that you read about might not always seem to be relevant to you. However, DQ is one of those areas where AI/ML can really deliver substantial added value. Machine learning algorithms can be used to automatically identify data quality issues and correct them. For example, machine learning models can be trained to detect duplicates, correct spelling errors, and identify missing data. Next to more automated error resolution a lot of DQ applications are expanding DQ to provide insights by discovering relationships, patterns and trends.

    We see both custombuild applications (Python and other open source libraries) and Data Quality platforms coming with embedded AI/ML functionality to bring DQ automation to the next level.

    TREND #4. From patchwork to a fundamental & companywide capability

    As Data Governance (DG) maturity in organizations grows, we also see that they address Data Quality as a fundamental integrated data capability. Instead of the so called one-off usage for one specific case (data migration, CRM, etc), a lot of companies make DQ a structural component that they can reuse continuously for all their data related initiatives. The benefit of this wider embedded approach is that it eases organizations to demonstrate DQ to be a profit vs a cost component.

    TREND #5. From a technology to a business driven framework

    As we mentioned in the previous trend and as maturity increases, we are also observing a shift in the approach from an it-driven towards a business-driven perspective. The main reason for this is that organizations require data quality to be seamlessly integrated into their business processes for optimal results.


    Conclusion

    In conclusion, data quality management is a critical component of data management, and there are many exciting trends and technologies emerging in this area. From data profiling to machine learning, organizations have many tools and techniques available to improve data quality and drive business growth.

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    DEMYSTIFYING DARK DATA: THE UNTAPPED POTENTIAL

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    As the world becomes increasingly digitised, organisations are generating more data than ever before. But did you know that up to 80% of that data remains untapped? This structured/unstructured, unprocessed data is known as dark data, and it has the potential to be a goldmine of insights for organisations.

    What is Dark Data?

    Dark data refers to data that organisations generate but don’t use. This data is typically unstructured and stored in various formats, such as emails, documents, images, videos, and social media posts. It’s often ignored because it is difficult to process and analyse, requiring advanced analytics tools and techniques to extract meaningful insights. However, with the right approach, dark data can be a valuable source of information that can help organisations make better decisions and gain a competitive advantage.

    Where is Dark Data Found?

    Dark data can be found in many areas of an organisation, including customer feedback, product reviews, employee emails, and social media mentions. By analysing this data, organisations can uncover hidden patterns and insights that can help them improve their products and services, enhance customer experiences, optimise operations, and reduce costs.

    Dark Data in Action

    Dark data can be used in virtually any industry to improve business outcomes. For example, an FMCG company can analyse customer reviews on social media and product forums to identify common complaints or issues. This data can be used to improve product design, customer service, and marketing strategies. Similarly, a healthcare organisation can analyse patient data to identify potential health risks, improve treatment plans, and optimise resource allocation.

    Analysing dark data requires a different approach than traditional structured data analysis. Machine learning and artificial intelligence can be used to process large amounts of unstructured data and extract meaningful insights. This technology can be used to categorise data, identify patterns and anomalies, and extract sentiment from text and other data.

    How to Leverage Dark Data?

    To leverage dark data effectively, organisations need to establish a data strategy that includes data governance, data quality, and data privacy. They need to ensure that the data they collect is accurate, complete, and secure, and that they comply with regulatory requirements. Additionally, organisations need to invest in the right tools and technologies to extract insights from dark data.

    There definitely some challenges with dark data is to extract valuable insights from it, but there are strategies you can use to put it in action:

    1. Identify your goals: Before you begin to analyze your dark data, it is essential to identify your business goals. Understanding what you want to achieve will help you determine what data to focus on and what insights you need to extract.
    2. Collaborate across teams: Dark data is often spread across different departments within an organization. Collaboration across teams can help you identify opportunities for using this data and uncover insights that might have been missed.
    3. Make your data consumable: Once you have identified the data you need, the next step is to make it available. This doesn’t mean that you need move your data in a single location as there are plenty efficient architectures to prevent data duplication. Making your data available in an agile and flexible way, will enable you to process and analyze it more efficiently.
    4. Use data analytics & AI/ML: The right tools can help you mine valuable insights from your dark data. There are various tools available, from simple data visualization software to sophisticated machine learning algorithms.
    5. Implement data governance: Finally, it is essential to have proper data governance in place to ensure that your dark data is used appropriately. This includes establishing data quality standards, data retention policies, and data security protocols.

    Conclusion

    Dark data represents a vast untapped resource for organisations seeking to gain a competitive advantage. By analysing this data, organisations can uncover valuable insights that can help them make better decisions and improve their operations. With the right strategies and technology in place, organisations can demystify dark data and unlock its full potential. It’s time to start exploring the dark data lurking within your organisation and turn it into a competitive advantage.

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    THE LINK BETWEEN ESG AND DATA: TRANSPARANCY FUELED BY MEASUREMENT & REPORTING

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    In recent years, Environmental, Social, and Governance (ESG) has become a buzzword in the corporate world. Investors and stakeholders are increasingly concerned with a company’s commitment to sustainability, social responsibility, and ethical practices. As a result, many companies are now incorporating ESG factors into their decision-making processes. But what is the link between ESG and data? In this blog, we explore how data plays a critical role in ESG transparancy.

    Tracking ESG Factors with Data

    Firstly, it’s essential to understand that ESG encompasses a wide range of factors, from environmental impact to labor practices to corporate governance. Companies must be able to track and measure these factors accurately to report on their ESG performance. This is where data comes in. By collecting and analyzing data, companies can gain insights into their ESG performance and identify areas for improvement.

    Using Data to Report on ESG Performance

    For example, an organization may collect data on its carbon emissions, water usage, and waste generation to assess its environmental impact. It may also collect data on employee turnover, diversity, and working conditions to assess its social impact. Finally, it may collect data on board composition, executive compensation, and shareholder rights to assess its governance practices. Once a company has collected this data, it can use it to report on its ESG performance.

    The Importance of Data for Benchmarking ESG Performance

    Reporting is an essential part of ESG because it allows investors and stakeholders to evaluate a company’s ESG practices and make informed decisions. ESG reporting typically involves disclosing data on a range of metrics, such as carbon emissions, employee diversity, and board diversity. Data is also crucial for benchmarking ESG performance. Companies can compare their performance against industry peers and ESG standards to identify areas for improvement.

    Benchmark Data for ESG Investing

    This benchmarking process often involves the use of ESG ratings and rankings, which are based on data collected from multiple sources. By using these ratings, companies can identify areas where they may be falling behind their peers and take steps to improve their ESG practices. Finally, data is critical for ESG investing. ESG investors use data to identify companies that are committed to sustainability, social responsibility, and ethical practices. They often look for companies with strong ESG ratings, which are based on data collected from multiple sources. By using data to identify these companies, ESG investors can make informed investment decisions that align with their values.

    Conclusion

    In conclusion, data plays a critical role in ESG. Companies must collect and analyze data to measure and report on their ESG performance accurately. Data is also crucial for benchmarking ESG performance and for ESG investing. As ESG continues to grow in importance, companies that prioritize data collection and analysis will be better equipped to meet investor and stakeholder expectations.



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    THE VITAL ROLE OF DATA SHARING AGREEMENTS AND CONTRACTS IN ENSURING SAFE & RESPONSIBLE DATA EXCHANGE

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    What Are Data Sharing Agreements & Contracts?

    Data sharing agreements and contracts are essentially documents that outline the terms and conditions of sharing data between two or more parties. These agreements are important to ensure that data is shared in a safe and responsible manner, and that all parties involved understand their rights and obligations.


    Key Elements



    Data sharing agreements typically include the following elements:

    • Purpose of data sharing: The reason why the data is being shared and how it will be used.
    • Data to be shared: The type of data that will be shared, including any restrictions or limitations.
    • Data security and privacy: The measures that will be taken to protect the data and ensure its privacy.
    • Data ownership and control: The ownership and control of the data, including any intellectual property rights.
    • Data retention and disposal: The length of time that the data will be retained and how it will be disposed of.
    • Liability and indemnification: The responsibilities and liabilities of each party involved in the data sharing, and any indemnification clauses.
    • Dispute resolution: The process for resolving any disputes that may arise during the data sharing process.

    To Conclude

    Data sharing agreements and contracts are important to ensure that data is shared in a responsible and safe manner, and that all parties involved understand their rights and obligations. They help to establish trust and transparency between parties, and can help to prevent legal and financial consequences that may arise from data breaches or misuse.

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

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    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.

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