Master Data_5 step approach.pdf
•
0 likes•3 views
The document outlines challenges with managing seismic data from multiple sources for an oil and gas project. It discusses the need to create a single master data repository that standardizes formats, ensures data quality and accessibility, and captures rich metadata to allow geoscientists to more efficiently select the best datasets. The expected benefits of the master data management approach include generating a unique, consistently tagged database that reduces time and costs while enabling artificial intelligence workflows.
Report
Share
Report
Share
Download to read offline
Recommended
Lecture 05 - The Data Warehouse and Technology
The document discusses the technological requirements for building a data warehouse. It outlines that a data warehouse requires a simpler technological setup than operational systems, with minimal needs for online updating, locking, and integrity features. It then covers various technological aspects of managing large amounts of data in a data warehouse, including storage media, indexing, interfaces, programming controls, efficient loading, and variable-length data. Finally, it discusses different database management system types and their role in data warehousing.
Data warehouse proposal
This document proposes creating a data warehouse at Rivier College to address several challenges: data is locked in different systems requiring manual extraction; administrators struggle to pull consistent data for reporting from different sources; and data analysis is basic without standard processes. The goals of the data warehouse are to improve planning and decision making through timely delivery of standardized, repeatable reports from a centralized collection of integrated, nonvolatile data. It will evolve to incorporate more institutional data sources over time.
Best practices data management
1. The document discusses best practices for managing research data over the data life cycle, from collection through sharing and archiving. It provides tips for organizing, documenting, and storing data in sustainable file formats and naming conventions. Following best practices helps ensure usability, reproducibility, and long-term access to research data.
2. Specific best practices covered include using consistent organization, standardized naming and formats, descriptive filenames, quality assurance, scripting for processing, documenting file contents, and choosing open file formats. The document also addresses data security, backup, and storage considerations.
3. Managing data properly is important for reuse and sharing data with others now or in the future. Scripting helps capture data workflows for reproducibility.
Data mining
The document discusses building a data warehouse by migrating data from legacy systems using an iterative methodology. It emphasizes the importance of high quality metadata to handle changes during the migration process and minimize errors. Uniform data access times across all machines are optimal for parallel query execution to avoid data skew. The crossbar switch architecture connects all machines equally, eliminating data skew issues seen in other architectures.
Keep Calm and Curate
Presentation given at the University of Cambridge for the English department's Food for Thought event on 27th June 2011
Tools and Techniques for Creating, Maintaining, and Distributing Shareable Me...
This document discusses tools and techniques for creating, maintaining, and distributing shareable metadata. It emphasizes that metadata should be structured to be understandable outside of local contexts and useful for other institutions. Key aspects of shareable metadata include using appropriate content and vocabularies, ensuring records are coherent, providing useful context, and conforming to standards. The document also outlines example workflows and considerations for making metadata shareable.
Elements of Data Documentation
This document discusses the importance of properly documenting research data. It notes that documentation allows data to be understood by those outside the original project and prevents inaccurate assumptions from being made if the data manipulations or variable meanings are unclear. Insufficient documentation can make data unusable or misinterpreted. The document outlines key elements to document like data elements, study details, and decisions made. It provides examples of documentation tools like codebooks, annotated instruments, and data narratives. Thorough documentation ensures research data remains useful and understandable.
DBMS.pptx
Swapnamay Samui presented on the topic of introduction to DBMS for the course Database Management Systems. The presentation covered key topics such as the definition of a database, differences between file systems and DBMS, advantages of using a DBMS, languages used in DBMS, types of DBMS, levels of abstraction in DBMS, roles of a database administrator, and structured versus unstructured data. The presentation took place on January 20, 2023 and was evaluated by faculty member Yusuf Munsi.
Recommended
Lecture 05 - The Data Warehouse and Technology
The document discusses the technological requirements for building a data warehouse. It outlines that a data warehouse requires a simpler technological setup than operational systems, with minimal needs for online updating, locking, and integrity features. It then covers various technological aspects of managing large amounts of data in a data warehouse, including storage media, indexing, interfaces, programming controls, efficient loading, and variable-length data. Finally, it discusses different database management system types and their role in data warehousing.
Data warehouse proposal
This document proposes creating a data warehouse at Rivier College to address several challenges: data is locked in different systems requiring manual extraction; administrators struggle to pull consistent data for reporting from different sources; and data analysis is basic without standard processes. The goals of the data warehouse are to improve planning and decision making through timely delivery of standardized, repeatable reports from a centralized collection of integrated, nonvolatile data. It will evolve to incorporate more institutional data sources over time.
Best practices data management
1. The document discusses best practices for managing research data over the data life cycle, from collection through sharing and archiving. It provides tips for organizing, documenting, and storing data in sustainable file formats and naming conventions. Following best practices helps ensure usability, reproducibility, and long-term access to research data.
2. Specific best practices covered include using consistent organization, standardized naming and formats, descriptive filenames, quality assurance, scripting for processing, documenting file contents, and choosing open file formats. The document also addresses data security, backup, and storage considerations.
3. Managing data properly is important for reuse and sharing data with others now or in the future. Scripting helps capture data workflows for reproducibility.
Data mining
The document discusses building a data warehouse by migrating data from legacy systems using an iterative methodology. It emphasizes the importance of high quality metadata to handle changes during the migration process and minimize errors. Uniform data access times across all machines are optimal for parallel query execution to avoid data skew. The crossbar switch architecture connects all machines equally, eliminating data skew issues seen in other architectures.
Keep Calm and Curate
Presentation given at the University of Cambridge for the English department's Food for Thought event on 27th June 2011
Tools and Techniques for Creating, Maintaining, and Distributing Shareable Me...
This document discusses tools and techniques for creating, maintaining, and distributing shareable metadata. It emphasizes that metadata should be structured to be understandable outside of local contexts and useful for other institutions. Key aspects of shareable metadata include using appropriate content and vocabularies, ensuring records are coherent, providing useful context, and conforming to standards. The document also outlines example workflows and considerations for making metadata shareable.
Elements of Data Documentation
This document discusses the importance of properly documenting research data. It notes that documentation allows data to be understood by those outside the original project and prevents inaccurate assumptions from being made if the data manipulations or variable meanings are unclear. Insufficient documentation can make data unusable or misinterpreted. The document outlines key elements to document like data elements, study details, and decisions made. It provides examples of documentation tools like codebooks, annotated instruments, and data narratives. Thorough documentation ensures research data remains useful and understandable.
DBMS.pptx
Swapnamay Samui presented on the topic of introduction to DBMS for the course Database Management Systems. The presentation covered key topics such as the definition of a database, differences between file systems and DBMS, advantages of using a DBMS, languages used in DBMS, types of DBMS, levels of abstraction in DBMS, roles of a database administrator, and structured versus unstructured data. The presentation took place on January 20, 2023 and was evaluated by faculty member Yusuf Munsi.
Lecture 01 Evolution of Decision Support Systems
The document discusses the evolution of decision support systems and data warehousing. It describes how operational systems evolved naturally over time, creating issues like lack of data credibility and productivity problems. This led to a change in approach with a new, architected environment featuring a single, integrated data warehouse. The data warehouse development process and users, namely decision support system analysts, are also discussed.
Minerva: Drill Storage Plugin for IPFS
Minerva is a storage plugin of Drill that connects IPFS's decentralized storage and Drill's flexible query engine. Any data file stored on IPFS can be easily accessed from Drill's query interface, just like a file stored on a local disk.
Visit https://github.com/bdchain/Minerva to learn more and try it out!
MEDIN Discovery Metadata Standard
The document provides an introduction to the MEDIN Discovery Metadata Standard. It describes metadata as information about a dataset that typically includes title, geographic scope, last update date, and contact details. The standard uses XML to encode metadata records to make them machine readable. It is compliant with international metadata standards and provides tools to generate, edit, validate, and transform metadata to the MEDIN format. The standard also specifies vocabularies to make metadata records "marine flavored."
Data Lakehouse, Data Mesh, and Data Fabric (r1)
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Data Lakehouse, Data Mesh, and Data Fabric (r2)
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
De-duplicated Refined Zone in Healthcare Data Lake Using Big Data Processing ...
This document discusses de-duplicating data in a healthcare data lake using big data processing frameworks. It describes keeping duplicate records and querying the latest one, or rewriting records to create a golden copy. The preferred approach uses Spark to partition data, identify new/updated records, de-duplicate by selecting the latest from incremental and refined data, and overwrite only affected partitions. This creates a non-ambiguous, de-duplicated dataset for analysis in a scalable and cost-effective manner.
KP Partners: DataStax and Analytics Implementation Methodology
Apache Cassandra is the leading distributed database in use at thousands of sites with the world’s most demanding scalability and availability requirements. Apache Spark is a distributed data analytics computing framework that has gained a lot of traction in processing large amounts of data in an efficient and user-friendly manner. The joining of both provides a powerful combination of real-time data collection with analytics. After a brief overview of Cassandra and Spark, this class will dive into various aspects of the integration.
Data mining notes
This document provides information about a course on data warehousing and data mining, including:
1. It outlines the course syllabus which covers the basics of data warehousing, data preprocessing, association rules, classification and clustering, and recent trends in data mining.
2. It describes the 5 units that make up the course, including an overview of the topics covered in each unit such as data warehouse architecture, data integration, decision trees, and applications of data mining.
3. It lists two textbooks and four references that will be used for the course.
Eidc data centre support
The document provides information about the NERC Environmental Data Centres (EIDCs) and the support they provide to NERC-funded researchers. The EIDCs are responsible for the long-term curation and management of environmental data generated through NERC-funded research. They provide guidance to researchers on data management best practices and help ensure data are properly archived and documented to be discoverable and reusable in the long term in accordance with funder and legal requirements. The EIDCs work with researchers throughout their projects to develop data management plans and accept datasets of long-term value at the end of projects for permanent storage, attribution of credit, and open access when any embargoes expire.
Ten Step Malady Fix Rev E
This document discusses modules for inputting and retrieving data and documents. It notes that supplemental input processes like EDI and fax are also used. While input data may meet standards, retrieval metadata may not capture all necessary content. A sample is provided showing different types of documents and their metadata. The document recommends tasks to analyze current processes and integrity of repositories to improve metadata and integration between input and retrieval systems.
Information Flow Mechanism in Data warehouse
This document outlines the process of information flow from source systems into a data warehouse for end users. It involves extracting data from various source systems, transforming it, and loading it into the data warehouse. The key steps are extracting data from production, internal, external, and archived sources; transforming the data by standardizing, merging, aggregating etc; loading the transformed data into the data warehouse; and delivering the information to end users through queries, reports, applications and other methods. The overall process is called ETL (extract, transform, load).
Data and Database Contexts
This Slide includes:
1. Data and Database contexts
2. Concept of Data
3. Database Structures
3.1. Hierarchical Model
3.2. Network Model
3.3. Network Model
4. Database Development
5. Types of Database
5.1. Operational Database
5.2. Distributed Database
5.3. Hypermedia Database
5.4. Multimedia Database
5.5. Client Server Database
5.6. Object Oriented database
6. Data Warehouse
7. Data Mining
- Uttar Tamang
Testing insights from data lakes
With data flowing from different mediums (RDBMS, SocialMedium, Legacy files),one of the efficacious mean for processing huge data is through Big Data, where Data Lake plays a critical role for storing Structured/Semi Structured and Unstructured Data. I have tried to give you a glimpse of , how testing of Data Lake is generally performed, what are the different types and approaches we follow.
The key to unlocking the Value in the IoT? Managing the Data!
The document discusses how managing data is key to unlocking value from the Internet of Things. It emphasizes that variety, not size, is most important with big data. Example use cases mentioned include predictive maintenance, search and root cause analysis. The technology landscape is changing with new architectures like data lakes and new patterns such as event histories and timelines. Managing data is also changing with schema on read, loosely coupled schemas, and increased importance of metadata. The document concludes that data management patterns and practices are foundational to effective analytics with IoT data.
CSU-ACADIS_dataManagement101-20120217
This document provides an overview of key concepts for effective data management, including why data management is important, common data types and stages, best practices for storage, versioning, naming conventions, metadata, standards, sharing, and archiving. It emphasizes that properly managing data helps ensure reproducibility, enables data sharing and reuse, satisfies funder requirements, and supports student work. The presentation covers terminology like metadata ("data about data") and standards like ISO and EML and provides examples to illustrate best practices for documentation to help others understand and use research data. It aims to bring together these concepts to help researchers develop effective Data Management Plans as required by funders like NSF.
Free Training: How to Build a Lakehouse
Doug Bateman, a principal data engineering instructor at Databricks, presented on how to build a Lakehouse architecture. He began by introducing himself and his background. He then discussed the goals of describing key Lakehouse features, explaining how Delta Lake enables it, and developing a sample Lakehouse using Databricks. The key aspects of a Lakehouse are that it supports diverse data types and workloads while enabling using BI tools directly on source data. Delta Lake provides reliability, consistency, and performance through its ACID transactions, automatic file consolidation, and integration with Spark. Bateman concluded with a demo of creating a Lakehouse.
MEDIN data guidelines
The document provides an introduction to MEDIN Data Guidelines. It explains that the guidelines are data archive standards intended to instill good practice, provide a consistent format for contractors to work to when returning data, and allow for easy ingestion of data into data archiving centers. The guidelines encompass a range of data types from public sightings to complex surveys. They are structured around six tables and provide standard templates for documenting project, survey, sample, and data information.
Organising and Documenting Data
Presented by Stuart Macdonald at the RDM Academic Liaison Library Training event, University of Edinburgh, 15 November 2012.
META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING
The quality is the key concept in each and every analysis as well as in computing applications. Today we
gather large volumes of information and store them in multidimensional mode that is in data warehouses
then analyze the data to be used in exact decision making in various fields. The studies proved that most of
the volumes of data are not useful for analysis due to lack of quality caused by improper data handling
techniques. This paper try to find out a solution to achieve the quality of data from the foundation of data
repositories and try to avoid quality anomalies at meta data level. This paper also proposes the new model
of Meta data architecture.
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS Template 2023-2024 by: Irene S. Rueco
More Related Content
Similar to Master Data_5 step approach.pdf
Lecture 01 Evolution of Decision Support Systems
The document discusses the evolution of decision support systems and data warehousing. It describes how operational systems evolved naturally over time, creating issues like lack of data credibility and productivity problems. This led to a change in approach with a new, architected environment featuring a single, integrated data warehouse. The data warehouse development process and users, namely decision support system analysts, are also discussed.
Minerva: Drill Storage Plugin for IPFS
Minerva is a storage plugin of Drill that connects IPFS's decentralized storage and Drill's flexible query engine. Any data file stored on IPFS can be easily accessed from Drill's query interface, just like a file stored on a local disk.
Visit https://github.com/bdchain/Minerva to learn more and try it out!
MEDIN Discovery Metadata Standard
The document provides an introduction to the MEDIN Discovery Metadata Standard. It describes metadata as information about a dataset that typically includes title, geographic scope, last update date, and contact details. The standard uses XML to encode metadata records to make them machine readable. It is compliant with international metadata standards and provides tools to generate, edit, validate, and transform metadata to the MEDIN format. The standard also specifies vocabularies to make metadata records "marine flavored."
Data Lakehouse, Data Mesh, and Data Fabric (r1)
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Data Lakehouse, Data Mesh, and Data Fabric (r2)
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
De-duplicated Refined Zone in Healthcare Data Lake Using Big Data Processing ...
This document discusses de-duplicating data in a healthcare data lake using big data processing frameworks. It describes keeping duplicate records and querying the latest one, or rewriting records to create a golden copy. The preferred approach uses Spark to partition data, identify new/updated records, de-duplicate by selecting the latest from incremental and refined data, and overwrite only affected partitions. This creates a non-ambiguous, de-duplicated dataset for analysis in a scalable and cost-effective manner.
KP Partners: DataStax and Analytics Implementation Methodology
Apache Cassandra is the leading distributed database in use at thousands of sites with the world’s most demanding scalability and availability requirements. Apache Spark is a distributed data analytics computing framework that has gained a lot of traction in processing large amounts of data in an efficient and user-friendly manner. The joining of both provides a powerful combination of real-time data collection with analytics. After a brief overview of Cassandra and Spark, this class will dive into various aspects of the integration.
Data mining notes
This document provides information about a course on data warehousing and data mining, including:
1. It outlines the course syllabus which covers the basics of data warehousing, data preprocessing, association rules, classification and clustering, and recent trends in data mining.
2. It describes the 5 units that make up the course, including an overview of the topics covered in each unit such as data warehouse architecture, data integration, decision trees, and applications of data mining.
3. It lists two textbooks and four references that will be used for the course.
Eidc data centre support
The document provides information about the NERC Environmental Data Centres (EIDCs) and the support they provide to NERC-funded researchers. The EIDCs are responsible for the long-term curation and management of environmental data generated through NERC-funded research. They provide guidance to researchers on data management best practices and help ensure data are properly archived and documented to be discoverable and reusable in the long term in accordance with funder and legal requirements. The EIDCs work with researchers throughout their projects to develop data management plans and accept datasets of long-term value at the end of projects for permanent storage, attribution of credit, and open access when any embargoes expire.
Ten Step Malady Fix Rev E
This document discusses modules for inputting and retrieving data and documents. It notes that supplemental input processes like EDI and fax are also used. While input data may meet standards, retrieval metadata may not capture all necessary content. A sample is provided showing different types of documents and their metadata. The document recommends tasks to analyze current processes and integrity of repositories to improve metadata and integration between input and retrieval systems.
Information Flow Mechanism in Data warehouse
This document outlines the process of information flow from source systems into a data warehouse for end users. It involves extracting data from various source systems, transforming it, and loading it into the data warehouse. The key steps are extracting data from production, internal, external, and archived sources; transforming the data by standardizing, merging, aggregating etc; loading the transformed data into the data warehouse; and delivering the information to end users through queries, reports, applications and other methods. The overall process is called ETL (extract, transform, load).
Data and Database Contexts
This Slide includes:
1. Data and Database contexts
2. Concept of Data
3. Database Structures
3.1. Hierarchical Model
3.2. Network Model
3.3. Network Model
4. Database Development
5. Types of Database
5.1. Operational Database
5.2. Distributed Database
5.3. Hypermedia Database
5.4. Multimedia Database
5.5. Client Server Database
5.6. Object Oriented database
6. Data Warehouse
7. Data Mining
- Uttar Tamang
Testing insights from data lakes
With data flowing from different mediums (RDBMS, SocialMedium, Legacy files),one of the efficacious mean for processing huge data is through Big Data, where Data Lake plays a critical role for storing Structured/Semi Structured and Unstructured Data. I have tried to give you a glimpse of , how testing of Data Lake is generally performed, what are the different types and approaches we follow.
The key to unlocking the Value in the IoT? Managing the Data!
The document discusses how managing data is key to unlocking value from the Internet of Things. It emphasizes that variety, not size, is most important with big data. Example use cases mentioned include predictive maintenance, search and root cause analysis. The technology landscape is changing with new architectures like data lakes and new patterns such as event histories and timelines. Managing data is also changing with schema on read, loosely coupled schemas, and increased importance of metadata. The document concludes that data management patterns and practices are foundational to effective analytics with IoT data.
CSU-ACADIS_dataManagement101-20120217
This document provides an overview of key concepts for effective data management, including why data management is important, common data types and stages, best practices for storage, versioning, naming conventions, metadata, standards, sharing, and archiving. It emphasizes that properly managing data helps ensure reproducibility, enables data sharing and reuse, satisfies funder requirements, and supports student work. The presentation covers terminology like metadata ("data about data") and standards like ISO and EML and provides examples to illustrate best practices for documentation to help others understand and use research data. It aims to bring together these concepts to help researchers develop effective Data Management Plans as required by funders like NSF.
Free Training: How to Build a Lakehouse
Doug Bateman, a principal data engineering instructor at Databricks, presented on how to build a Lakehouse architecture. He began by introducing himself and his background. He then discussed the goals of describing key Lakehouse features, explaining how Delta Lake enables it, and developing a sample Lakehouse using Databricks. The key aspects of a Lakehouse are that it supports diverse data types and workloads while enabling using BI tools directly on source data. Delta Lake provides reliability, consistency, and performance through its ACID transactions, automatic file consolidation, and integration with Spark. Bateman concluded with a demo of creating a Lakehouse.
MEDIN data guidelines
The document provides an introduction to MEDIN Data Guidelines. It explains that the guidelines are data archive standards intended to instill good practice, provide a consistent format for contractors to work to when returning data, and allow for easy ingestion of data into data archiving centers. The guidelines encompass a range of data types from public sightings to complex surveys. They are structured around six tables and provide standard templates for documenting project, survey, sample, and data information.
Organising and Documenting Data
Presented by Stuart Macdonald at the RDM Academic Liaison Library Training event, University of Edinburgh, 15 November 2012.
META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING
The quality is the key concept in each and every analysis as well as in computing applications. Today we
gather large volumes of information and store them in multidimensional mode that is in data warehouses
then analyze the data to be used in exact decision making in various fields. The studies proved that most of
the volumes of data are not useful for analysis due to lack of quality caused by improper data handling
techniques. This paper try to find out a solution to achieve the quality of data from the foundation of data
repositories and try to avoid quality anomalies at meta data level. This paper also proposes the new model
of Meta data architecture.
Similar to Master Data_5 step approach.pdf (20)
De-duplicated Refined Zone in Healthcare Data Lake Using Big Data Processing ...
De-duplicated Refined Zone in Healthcare Data Lake Using Big Data Processing ...
KP Partners: DataStax and Analytics Implementation Methodology
KP Partners: DataStax and Analytics Implementation Methodology
The key to unlocking the Value in the IoT? Managing the Data!
The key to unlocking the Value in the IoT? Managing the Data!
META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING
META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING
Recently uploaded
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS Template 2023-2024 by: Irene S. Rueco
The Diamonds of 2023-2024 in the IGRA collection
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
How to Manage Your Lost Opportunities in Odoo 17 CRM
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Walmart Business+ and Spark Good for Nonprofits.pdf
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
clinical examination of hip joint (1).pdf
described clinical examination all orthopeadic conditions .
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama UniversityAkanksha trivedi rama nursing college kanpur.
Natural birth techniques are various type such as/ water birth , alexender method, hypnosis, bradley method, lamaze method etcPollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...National Information Standards Organization (NISO)
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...Nguyen Thanh Tu Collection
https://app.box.com/s/y977uz6bpd3af4qsebv7r9b7s21935vdISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
Recently uploaded (20)
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
Liberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdf
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
How to Manage Your Lost Opportunities in Odoo 17 CRM
How to Manage Your Lost Opportunities in Odoo 17 CRM
Walmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdf
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Master Data_5 step approach.pdf
- 1. Master Data Project (Validated, integrated & accessible to all at all-time database)
- 2. Challenges: 1. Multiple seismic datasets (Basic/Reprocessed) with varied quality/objectives are available for the same AOI 2. Quality assurance/feasibility and identifying the most suitable seismic dataset consumes a lots of initial time in the Assigned project schedule 3. Accessibility for Integration of various sources of data, such as acquisition/processing/Interpretation reports are not easily available 4. Conversion of Legacy data into standard format ( Source/Quality – Unknown) 5. Creation of Seismic data base to capture the uniqueness and quality of data as Metadata (PSTM/ PSDM Stack and Gathers) Master Data Management
- 3. Reasons: 1. Multiple versions of data set with different agencies leading to non-uniqueness 2. Standardisation for datasets is more complex. Naming standards and non uniform. 3. Lack of meta data or contextual information especially for seismic data 4. Geoscientist spends significant time to select the most suitable data for interpretation Master Data Management
- 4. Expected Outcome/Benefits of Metadata tagged Master : 1. Generation of unique database 2. Ranking/ quality tagging will enable smart data selection 3. Turnaround time of the project will be optimised and hence cost effective 4. Will reduce Human intervention in identifying suitable data and enabling AI/ML workflows 5. Users can access the single and consistent database 6. Automated process workflows will enable tracking the metadata and source in database Master Data Management
- 5. 5-Step Approach towards trusted data Discussion with Experts
- 6. THANKS