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

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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.

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