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💡Master Data is the pulsating heart of our organization, dictating the rhythm of our operations and guiding our strategic decisions. Nurturing and safeguarding its quality should get our commitment, where SAP MDG Data Quality Management Rule Mining can play a pivotal role! #sapmdg #dqm #rulemining

See how Machine Learning Algorithms can help refine the quality of data with SAP Master Data Governance - Rule Mining. #sapmdg #machinelearning #dataquality

As data acquisition is booming, businesses are recognizing the need for both data mining and process mining to drive more effective analysis. Data mining uncovers insights from vast datasets, while process mining dives into the 'how' by analyzing event logs, enabling a holistic view of business processes. Let's explore the synergy of data and process mining for enhanced problem solving. #data #datamining #dataanalysis #processmining #bpm https://lnkd.in/gUzGxMfT

"Data Transformation in Signavio" There is no data mining without data. For process mining, we analyze process-related data using a specialized mining tool obtain insights on process inefficiencies and improvements. Process data is hidden in ERP systems that captures data in tables. This needs to first be extracted and then transformed into a specific format before they can be analyzed by a process mining tool such as SAP Signavio Process Intelligence. General concept on how to prepare transactional data for mining. This concept is called ETL and stands for: [Data] Extraction [Data] Transformation [Data] Load. "SAP Signavio ETL Data Pipelines" Process Intelligence ETL is the data ingestion component. It automates data extractions and transformations from external source systems and loads it directly to SAP Signavio Process Intelligence. All of this can be done in Process Intelligence itself without the need to configure a staging environment and only on-premise systems require additional setup on the source system side. "Standalone Connector Used for the data Transfer" The standalone connector handles the communication between the source system and SAP Signavio Process Intelligence. This connector can be used, if the source system is not covered by one of the standard connectors in SAP Signavio Process Intelligence (or any other third party systems). It extracts data from the source system, transforms it to event log format, and is then uploaded to Process Intelligence to be analyzed.  However, the ETL scripts need to run externally (outside of SAP Signavio Process Intelligence) but uses the API to push the data to a process within the system. The connector consists of multiple components working together to achieve this. This includes: A collection of extraction and transformation SQL scripts A configuration file in YAML format An SQLite database to ensure the correct data is loaded each time in case of regular loads A java application for triggering the actual extraction, transformation and loading Connect with us: stel-signavio@stellium.com Durgesh Verma,Gayathri Jayakumar (GJ),Tina Thimmaiah,Muhammad Saad,Rachana Nair,Rohit Kamath,Shri Lakshmi Reddy,Akhilesh Shinde

testing What is data mining, metadata mining, and metadata management? 🤔 💡 Data Mining: At its core, data mining involves analyzing large datasets to discover patterns, trends, and relationships that might not be immediately apparent. It encompasses a range of techniques and methodologies from statistics, machine learning, and artificial intelligence designed to extract valuable insights from data. 💡 Metadata Mining: Whereas data mining focuses on the data itself, metadata mining sifts through data about the data. Metadata provides context—such as authorship, creation date, and format—offering a bird's-eye view of the information crucial for organization, retrieval, and understanding of data at scale. 💡 Metadata Management: This is the systematic approach to handling metadata. It involves defining, organizing, protecting, and making metadata accessible within an organization. Effective metadata management ensures that metadata is accurate, consistent, and usable, serving as a solid foundation for data and metadata mining efforts. How are these concepts different or related to each other? How do they relate to Data Mesh? Find out more at https://lnkd.in/g3XHyvnz #datamining #metadatamanagement #metadata #metadatamining #datamesh

Understanding the Key Role of Data Integration in Data Mining: Finding important information is essential to making decisions in the modern era. To extract knowledge and hidden patterns from data, data mining is necessary. But data is frequently locked in […] The post Understanding the Key Role of Data Integration in Data Mining appeared first on Datafloq. #DataScince #ArtificialIntelligence #MachineLearning

What is data mining, metadata mining, and metadata management? 🤔 💡 Data Mining: At its core, data mining involves analyzing large datasets to discover patterns, trends, and relationships that might not be immediately apparent. It encompasses a range of techniques and methodologies from statistics, machine learning, and artificial intelligence designed to extract valuable insights from data. 💡 Metadata Mining: Whereas data mining focuses on the data itself, metadata mining sifts through data about the data. Metadata provides context—such as authorship, creation date, and format—offering a bird's-eye view of the information crucial for organization, retrieval, and understanding of data at scale. 💡 Metadata Management: This is the systematic approach to handling metadata. It involves defining, organizing, protecting, and making metadata accessible within an organization. Effective metadata management ensures that metadata is accurate, consistent, and usable, serving as a solid foundation for data and metadata mining efforts. How are these concepts different or related to each other? How do they relate to Data Mesh? Find out more at https://lnkd.in/g3XHyvnz [n#datamining|https://lnkd.in/gdF_VwFt] #metadatamanagement #metadata #metadatamining #datamesh

What is data mining, metadata mining, and metadata management? 🤔 💡 Data Mining: At its core, data mining involves analyzing large datasets to discover patterns, trends, and relationships that might not be immediately apparent. It encompasses a range of techniques and methodologies from statistics, machine learning, and artificial intelligence designed to extract valuable insights from data. 💡 Metadata Mining: Whereas data mining focuses on the data itself, metadata mining sifts through data about the data. Metadata provides context—such as authorship, creation date, and format—offering a bird's-eye view of the information crucial for organization, retrieval, and understanding of data at scale. 💡 Metadata Management: This is the systematic approach to handling metadata. It involves defining, organizing, protecting, and making metadata accessible within an organization. Effective metadata management ensures that metadata is accurate, consistent, and usable, serving as a solid foundation for data and metadata mining efforts. How are these concepts different or related to each other? How do they relate to Data Mesh? ➡ Read more at: https://lnkd.in/g3XHyvnz #datamining #metadatamanagement #metadata #metadatamining #datamesh

Mine or Model. It’s the #BPM equivalent of which came first the 🐓 or the 🥚. Actually the answer is much less philosophical and much more practical. One of the perceived challenges, and ergo one of the biggest objections to starting with #processmining is access to data. Process Mining as a capability and as a means to derive insight is usually owned by line of business functions, transformation office, or CoE. #data and data management is usually owned by IT departments or data engineering and therefore ‘the business’ is unsure whether the data is in the correct structure, or even available. Our very own Dr. Nick debunks this uncertainty with a very easy to understand explanation. Jonas Alain Matt Ricardo Jesper