This document defines a data warehouse as a central repository for integrated data from one or more sources used to support analytical reporting and business intelligence. It stores current and historical data in one place. The concept of data warehousing originated in the late 1980s to provide an architectural model for data flow from operational systems to decision support systems. Key characteristics of a data warehouse include being subject-oriented, integrated, nonvolatile, and time-variant. The document also discusses data marts, types of data stored, and applications of data warehousing and business intelligence.
Business intelligence (BI) refers to transforming raw company data into usable information through specialized computer programs. Raw data from transaction systems can be aggregated and manipulated in BI applications to generate information like sales trend graphs. This helps address challenges where companies have large amounts of raw data but lack tools to exploit it. BI applications read data from transaction systems, transform and present it to decision makers in reports, charts, queries and alerts. For BI projects to succeed, management must be committed, users involved in planning, and systems made easy to use and flexible.
“Opening Pandora’s box” - Why bother data model for ERP systems?
This presentation covers :
a. Why should you bother with data modelling when you’ve got or are planning to get an ERP?
i. For requirements gathering.
ii. For Data migration / take on
iii. Master Data alignment
iv. Data lineage (particularly important with Data Lineage & SoX compliance issues)
v. For reporting (Particularly Business Intelligence & Data Warehousing)
vi. But most importantly, for integration of the ERP metadata into your overall Information Architecture.
b. But don’t you get a data model with the ERP anyway?
i. Errr not with all of them (e.g. SAP) – in fact non of them to our knowledge
ii. What can be leveraged from the vendor?
c. How can you incorporate SAP metadata into your overall model?
i. What are the requirements?
ii. How to get inside the black box
iii. Is there any technology available?
iv. What about DIY?
d. So, what are the overall benefits of doing this:
i. Ease of integration
ii. Fitness for purpose
iii. Reuse of data artefacts
iv. No nasty data surprises
v. Alignment with overall data strategy
This document defines a data warehouse as a collection of corporate information derived from operational systems and external sources to support business decisions rather than operations. It discusses the purpose of data warehousing to realize the value of data and make better decisions. Key components like staging areas, data marts, and operational data stores are described. The document also outlines evolution of data warehouse architectures and best practices for implementation.
ERP systems tie together business processes and enable data flow between them. They are used for day-to-day activities like finance, procurement, project management, and supply chain management. ERP started in the 1990s but has evolved to embrace the cloud, providing updates several times a year instead of costly 5-10 year upgrades. Modern ERP systems deliver services via the cloud and use latest technologies like AI, machine learning, blockchain, augmented reality, and IoT. Successful ERP implementations require thorough requirements gathering and integration efforts pay off in the long run, while poor management can cause failures even with the perfect system selection.
Data must be shaped into a meaningful and useful form for human beings. Information systems are made up of interrelated components that collect, process, store and disseminate data to support decision making in an organization. Information systems can be computer-based or manual and provide organizational solutions to business challenges. Common types of information systems include transaction processing systems, management information systems, executive information systems, and decision support systems.
This document provides an overview of key concepts from the first chapter of an introduction to information systems textbook. It defines data and information, discussing the difference between the two. It also defines other core terms like process, knowledge, and system. It describes the basic components of a computer-based information system including hardware, software, databases, telecommunications and networks. It provides examples of information systems and discusses the input, processing, output and feedback aspects of how systems function.
The document discusses the key concepts of information management. It begins by defining data and how it is transformed into information. It then discusses definitions of information and management, and how information management originated from fields like archives, records management, and librarianship. It also notes the influence of information technology. The document outlines the importance of information management and its goals, strategies, elements, lifecycle, resources, and tools. It discusses access, privacy, security and relevant laws. Finally, it concludes with questions for further discussion.
This document describes a proposed data warehousing model and architecture for a medical center. It discusses setting up data marts and warehouses to store medical data from various systems to enable analysis and reporting. It outlines some goals like improving reporting, integrating different data sources, and facilitating trend analysis. It also notes some challenges around support, identifying reporting needs, and bridging technical and user needs.
Business intelligence (BI) refers to transforming raw company data into usable information through specialized computer programs. Raw data from transaction systems can be aggregated and manipulated in BI applications to generate information like sales trend graphs. This helps address challenges where companies have large amounts of raw data but lack tools to exploit it. BI applications read data from transaction systems, transform and present it to decision makers in reports, charts, queries and alerts. For BI projects to succeed, management must be committed, users involved in planning, and systems made easy to use and flexible.
“Opening Pandora’s box” - Why bother data model for ERP systems?
This presentation covers :
a. Why should you bother with data modelling when you’ve got or are planning to get an ERP?
i. For requirements gathering.
ii. For Data migration / take on
iii. Master Data alignment
iv. Data lineage (particularly important with Data Lineage & SoX compliance issues)
v. For reporting (Particularly Business Intelligence & Data Warehousing)
vi. But most importantly, for integration of the ERP metadata into your overall Information Architecture.
b. But don’t you get a data model with the ERP anyway?
i. Errr not with all of them (e.g. SAP) – in fact non of them to our knowledge
ii. What can be leveraged from the vendor?
c. How can you incorporate SAP metadata into your overall model?
i. What are the requirements?
ii. How to get inside the black box
iii. Is there any technology available?
iv. What about DIY?
d. So, what are the overall benefits of doing this:
i. Ease of integration
ii. Fitness for purpose
iii. Reuse of data artefacts
iv. No nasty data surprises
v. Alignment with overall data strategy
This document defines a data warehouse as a collection of corporate information derived from operational systems and external sources to support business decisions rather than operations. It discusses the purpose of data warehousing to realize the value of data and make better decisions. Key components like staging areas, data marts, and operational data stores are described. The document also outlines evolution of data warehouse architectures and best practices for implementation.
ERP systems tie together business processes and enable data flow between them. They are used for day-to-day activities like finance, procurement, project management, and supply chain management. ERP started in the 1990s but has evolved to embrace the cloud, providing updates several times a year instead of costly 5-10 year upgrades. Modern ERP systems deliver services via the cloud and use latest technologies like AI, machine learning, blockchain, augmented reality, and IoT. Successful ERP implementations require thorough requirements gathering and integration efforts pay off in the long run, while poor management can cause failures even with the perfect system selection.
Data must be shaped into a meaningful and useful form for human beings. Information systems are made up of interrelated components that collect, process, store and disseminate data to support decision making in an organization. Information systems can be computer-based or manual and provide organizational solutions to business challenges. Common types of information systems include transaction processing systems, management information systems, executive information systems, and decision support systems.
This document provides an overview of key concepts from the first chapter of an introduction to information systems textbook. It defines data and information, discussing the difference between the two. It also defines other core terms like process, knowledge, and system. It describes the basic components of a computer-based information system including hardware, software, databases, telecommunications and networks. It provides examples of information systems and discusses the input, processing, output and feedback aspects of how systems function.
The document discusses the key concepts of information management. It begins by defining data and how it is transformed into information. It then discusses definitions of information and management, and how information management originated from fields like archives, records management, and librarianship. It also notes the influence of information technology. The document outlines the importance of information management and its goals, strategies, elements, lifecycle, resources, and tools. It discusses access, privacy, security and relevant laws. Finally, it concludes with questions for further discussion.
This document describes a proposed data warehousing model and architecture for a medical center. It discusses setting up data marts and warehouses to store medical data from various systems to enable analysis and reporting. It outlines some goals like improving reporting, integrating different data sources, and facilitating trend analysis. It also notes some challenges around support, identifying reporting needs, and bridging technical and user needs.
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
BI is the “Gathering of data from multiple sources to present it in a way that allows executives to make better business decisions”. I will describe in more detail exactly what BI is, what encompasses the Microsoft BI stack, why it is so popular, and why a BI career pays so much. I will review specific examples from previous projects of mine that show the benefits of BI and its huge return-on-investment. I'll go into detail on the components of a BI solution, and I will discuss key concepts for successfully implementing BI in your organization.
James A. O'Brien, and George Marakas. Management Information Systems with MISource 2007, 8th ed. Boston, MA: McGraw-Hill, Inc., 2007. ISBN: 13 9780073323091
This document provides an overview of business intelligence and analytics (BIA). It discusses how BIA uses technologies and practices to analyze critical business data and provide insights to improve decision making. It also covers challenges like accessing diverse data and big data analytics. The stages of BIA include data warehousing, extraction, transformation and loading of data, analytics like descriptive, predictive and prescriptive analysis, and knowledge discovery techniques. BIA provides businesses insights from large and diverse data generated through applications to help in areas like marketing, finance, and human resources.
This document discusses Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Supply Chain Management (SCM). ERP integrates internal business processes, CRM focuses on marketing, sales, and customer service, and SCM coordinates activities from initial raw materials to final delivery of products. The document outlines the business functions and benefits of each system, as well as causes for implementation failures such as underestimating complexity and overreliance on vendor claims. Enterprise Application Integration (EAI) software connects major e-business applications like CRM and ERP.
Basic Introduction of Data Warehousing from Adiva Consultingadivasoft
This document provides an overview of Hyperion Essbase & Planning Training. It discusses key concepts like raw data transformation into information, online transaction processing (OLTP) systems, challenges with current data management, the purpose of data warehousing and data marts. It also covers dimensional modeling best practices, types of fact and dimension tables, and how Essbase is tuned for analysis and provides advantages over traditional databases for analytics.
The document provides an overview of relational databases and their advantages over traditional file-based systems. It discusses key concepts such as entities, attributes, records, files and databases. The document also describes database management systems (DBMS), schemas, data dictionaries, and relational database structures including tables, rows, columns, primary keys and foreign keys. Relational databases organize data into logically related tables to facilitate data integration, sharing, flexibility and consistency.
Master Data Management's Place in the Data Governance Landscape CCG
This document provides an overview of master data management and how it relates to data governance. It defines key concepts like master data, reference data, and different master data management architectural models. It discusses how master data management aligns with and supports data governance objectives. Specifically, it notes that MDM should not be implemented without formal data quality and governance programs already in place. It also explains how various data governance functions like ownership, policies and standards apply to master data.
Business Intelligence (BI) and Data Management Basics amorshed
This document provides an overview of business intelligence (BI) and data management basics. It discusses topics such as digital transformation requirements, data strategy, data governance, data literacy, and becoming a data-driven organization. The document emphasizes that in the digital age, data is a key asset and organizations need to focus on data management in order to make informed decisions. It also stresses the importance of data culture and competency for successful BI and data initiatives.
This document contains slides from a lecture on information management. It discusses key topics like the importance of information in today's organizations, challenges of information overload and management, how information supports business processes, and the relationships between data, information, and knowledge. It also provides examples and definitions to illustrate information management concepts. The final slides discuss knowledge management and common problems organizations face with knowledge management initiatives.
The document discusses the database development life cycle (DBLC), which follows a similar process to the systems development life cycle (SDLC). The DBLC involves gathering requirements, database analysis, design, implementation, testing and evaluation, and maintenance. It describes each stage in detail, including conceptual, logical, and physical data modeling during the design stage. The goal is to systematically plan and develop a database to meet requirements while ensuring completeness, integrity, flexibility, and usability.
Master Data Management - Gartner Presentation303Computing
This document discusses Digital Realty's implementation of a master data management (MDM) system. It provides an overview of MDM and why most projects fail. Digital Realty is succeeding by taking an agile approach with flexible multi-domain solutions. They leverage data virtualization and have identified data champions to manage master data domains like customers, products, facilities and people. The MDM implementation has provided benefits like improved data quality monitoring, faster integration of acquired companies, and ensuring compliance with data governance policies. Digital Realty is working to expand their MDM to additional transactional and dimensional master data entities.
An information system is a combination of hardware, software, infrastructure, and trained personnel organized to facilitate planning, control, coordination, decision making in an organization. There are several types of information systems including executive support systems, management information systems, decision support systems, knowledge management systems, transaction processing systems, and office automation systems. The five basic components of an information system are hardware, software, data, procedures, and people.
This document provides an overview of business intelligence. It discusses how more than 35% of top global companies regularly fail to make insightful decisions. It then describes how business intelligence tools can help by gathering and storing enterprise data systematically to transform it into knowledge through reports and graphs. This helps users make better business decisions. An example is given of a large US retail shop that used business intelligence to discover a connection between diaper and beer sales, allowing them to increase both products' sales by placing them closer together on shelves. The document concludes that business intelligence has great potential to find unexpected insights and can help organizations stand out from competitors by supporting more reliable decision-making.
Multi tiered hybrid data center designMehmet Cetin
This document discusses a multi-tiered hybrid data center design that allows for modular and flexible infrastructure. It proposes designing the data center with separate tiered sections (Tier II, III, IV) that can each be scaled independently as needed. This approach provides a more cost effective and energy efficient solution than a single-tiered design, allows the data center to meet varying operational needs simultaneously, and facilitates future-proofing and scalability as demands change over time.
Enterprise systems integrate key business processes throughout an entire firm into a single software system. This allows information to flow seamlessly between different departments. For example, when a sales representative in Brussels enters a customer order, the factory in Hong Kong receives the order automatically and begins production. Updated sales and production data then flows to accounting and other departments. Managers need to pay attention to business processes because they determine how well an organization can execute tasks and be a potential source of success or failure.
This document provides an overview of data resource management and file organization concepts. It discusses key terms like binary, bit, byte, field, record, and file. It explains different file organization methods like traditional file environments and database management systems. It also summarizes different types of databases like relational, hierarchical, network, and object-oriented databases. Finally, it discusses database design, management, querying, distribution, warehousing, and trends like linking databases to the web.
The document discusses various approaches to building information systems. It describes the core activities in the systems development process including systems analysis, design, programming, testing, conversion, and production/maintenance. It also compares structured and object-oriented development methodologies. Finally, it discusses alternative approaches like prototyping, end-user development, packaged software, outsourcing, rapid application development and joint application design.
The document provides information about data warehousing concepts. It defines a data warehouse as a relational database designed for query and analysis rather than transactions. It contains historical data from various sources and separates analysis from transaction workloads. The goals of a data warehouse are to provide a single source of integrated information, give users direct access to data without relying on IT, and allow predictive modeling. Factors like significant user requests for related historical data and advanced decision support needs should be considered when implementing a data warehouse.
Business Intelligence Data Warehouse SystemKiran kumar
This document provides an overview of data warehousing and business intelligence concepts. It discusses:
- What a data warehouse is and its key properties like being integrated, non-volatile, time-variant and subject-oriented.
- Common data warehouse architectures including dimensional modeling, ETL processes, and different layers like the data storage layer and presentation layer.
- How data marts are subsets of the data warehouse that focus on specific business functions or departments.
- Different types of dimensions tables and slowly changing dimensions.
- How business intelligence uses the data warehouse for analysis, querying, reporting and generating insights to help with decision making.
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
BI is the “Gathering of data from multiple sources to present it in a way that allows executives to make better business decisions”. I will describe in more detail exactly what BI is, what encompasses the Microsoft BI stack, why it is so popular, and why a BI career pays so much. I will review specific examples from previous projects of mine that show the benefits of BI and its huge return-on-investment. I'll go into detail on the components of a BI solution, and I will discuss key concepts for successfully implementing BI in your organization.
James A. O'Brien, and George Marakas. Management Information Systems with MISource 2007, 8th ed. Boston, MA: McGraw-Hill, Inc., 2007. ISBN: 13 9780073323091
This document provides an overview of business intelligence and analytics (BIA). It discusses how BIA uses technologies and practices to analyze critical business data and provide insights to improve decision making. It also covers challenges like accessing diverse data and big data analytics. The stages of BIA include data warehousing, extraction, transformation and loading of data, analytics like descriptive, predictive and prescriptive analysis, and knowledge discovery techniques. BIA provides businesses insights from large and diverse data generated through applications to help in areas like marketing, finance, and human resources.
This document discusses Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Supply Chain Management (SCM). ERP integrates internal business processes, CRM focuses on marketing, sales, and customer service, and SCM coordinates activities from initial raw materials to final delivery of products. The document outlines the business functions and benefits of each system, as well as causes for implementation failures such as underestimating complexity and overreliance on vendor claims. Enterprise Application Integration (EAI) software connects major e-business applications like CRM and ERP.
Basic Introduction of Data Warehousing from Adiva Consultingadivasoft
This document provides an overview of Hyperion Essbase & Planning Training. It discusses key concepts like raw data transformation into information, online transaction processing (OLTP) systems, challenges with current data management, the purpose of data warehousing and data marts. It also covers dimensional modeling best practices, types of fact and dimension tables, and how Essbase is tuned for analysis and provides advantages over traditional databases for analytics.
The document provides an overview of relational databases and their advantages over traditional file-based systems. It discusses key concepts such as entities, attributes, records, files and databases. The document also describes database management systems (DBMS), schemas, data dictionaries, and relational database structures including tables, rows, columns, primary keys and foreign keys. Relational databases organize data into logically related tables to facilitate data integration, sharing, flexibility and consistency.
Master Data Management's Place in the Data Governance Landscape CCG
This document provides an overview of master data management and how it relates to data governance. It defines key concepts like master data, reference data, and different master data management architectural models. It discusses how master data management aligns with and supports data governance objectives. Specifically, it notes that MDM should not be implemented without formal data quality and governance programs already in place. It also explains how various data governance functions like ownership, policies and standards apply to master data.
Business Intelligence (BI) and Data Management Basics amorshed
This document provides an overview of business intelligence (BI) and data management basics. It discusses topics such as digital transformation requirements, data strategy, data governance, data literacy, and becoming a data-driven organization. The document emphasizes that in the digital age, data is a key asset and organizations need to focus on data management in order to make informed decisions. It also stresses the importance of data culture and competency for successful BI and data initiatives.
This document contains slides from a lecture on information management. It discusses key topics like the importance of information in today's organizations, challenges of information overload and management, how information supports business processes, and the relationships between data, information, and knowledge. It also provides examples and definitions to illustrate information management concepts. The final slides discuss knowledge management and common problems organizations face with knowledge management initiatives.
The document discusses the database development life cycle (DBLC), which follows a similar process to the systems development life cycle (SDLC). The DBLC involves gathering requirements, database analysis, design, implementation, testing and evaluation, and maintenance. It describes each stage in detail, including conceptual, logical, and physical data modeling during the design stage. The goal is to systematically plan and develop a database to meet requirements while ensuring completeness, integrity, flexibility, and usability.
Master Data Management - Gartner Presentation303Computing
This document discusses Digital Realty's implementation of a master data management (MDM) system. It provides an overview of MDM and why most projects fail. Digital Realty is succeeding by taking an agile approach with flexible multi-domain solutions. They leverage data virtualization and have identified data champions to manage master data domains like customers, products, facilities and people. The MDM implementation has provided benefits like improved data quality monitoring, faster integration of acquired companies, and ensuring compliance with data governance policies. Digital Realty is working to expand their MDM to additional transactional and dimensional master data entities.
An information system is a combination of hardware, software, infrastructure, and trained personnel organized to facilitate planning, control, coordination, decision making in an organization. There are several types of information systems including executive support systems, management information systems, decision support systems, knowledge management systems, transaction processing systems, and office automation systems. The five basic components of an information system are hardware, software, data, procedures, and people.
This document provides an overview of business intelligence. It discusses how more than 35% of top global companies regularly fail to make insightful decisions. It then describes how business intelligence tools can help by gathering and storing enterprise data systematically to transform it into knowledge through reports and graphs. This helps users make better business decisions. An example is given of a large US retail shop that used business intelligence to discover a connection between diaper and beer sales, allowing them to increase both products' sales by placing them closer together on shelves. The document concludes that business intelligence has great potential to find unexpected insights and can help organizations stand out from competitors by supporting more reliable decision-making.
Multi tiered hybrid data center designMehmet Cetin
This document discusses a multi-tiered hybrid data center design that allows for modular and flexible infrastructure. It proposes designing the data center with separate tiered sections (Tier II, III, IV) that can each be scaled independently as needed. This approach provides a more cost effective and energy efficient solution than a single-tiered design, allows the data center to meet varying operational needs simultaneously, and facilitates future-proofing and scalability as demands change over time.
Enterprise systems integrate key business processes throughout an entire firm into a single software system. This allows information to flow seamlessly between different departments. For example, when a sales representative in Brussels enters a customer order, the factory in Hong Kong receives the order automatically and begins production. Updated sales and production data then flows to accounting and other departments. Managers need to pay attention to business processes because they determine how well an organization can execute tasks and be a potential source of success or failure.
This document provides an overview of data resource management and file organization concepts. It discusses key terms like binary, bit, byte, field, record, and file. It explains different file organization methods like traditional file environments and database management systems. It also summarizes different types of databases like relational, hierarchical, network, and object-oriented databases. Finally, it discusses database design, management, querying, distribution, warehousing, and trends like linking databases to the web.
The document discusses various approaches to building information systems. It describes the core activities in the systems development process including systems analysis, design, programming, testing, conversion, and production/maintenance. It also compares structured and object-oriented development methodologies. Finally, it discusses alternative approaches like prototyping, end-user development, packaged software, outsourcing, rapid application development and joint application design.
The document provides information about data warehousing concepts. It defines a data warehouse as a relational database designed for query and analysis rather than transactions. It contains historical data from various sources and separates analysis from transaction workloads. The goals of a data warehouse are to provide a single source of integrated information, give users direct access to data without relying on IT, and allow predictive modeling. Factors like significant user requests for related historical data and advanced decision support needs should be considered when implementing a data warehouse.
Business Intelligence Data Warehouse SystemKiran kumar
This document provides an overview of data warehousing and business intelligence concepts. It discusses:
- What a data warehouse is and its key properties like being integrated, non-volatile, time-variant and subject-oriented.
- Common data warehouse architectures including dimensional modeling, ETL processes, and different layers like the data storage layer and presentation layer.
- How data marts are subsets of the data warehouse that focus on specific business functions or departments.
- Different types of dimensions tables and slowly changing dimensions.
- How business intelligence uses the data warehouse for analysis, querying, reporting and generating insights to help with decision making.
The document provides information about data warehousing including definitions, how it works, types of data warehouses, components, architecture, and the ETL process. Some key points:
- A data warehouse is a system for collecting and managing data from multiple sources to support analysis and decision-making. It contains historical, integrated data organized around important subjects.
- Data flows into a data warehouse from transaction systems and databases. It is processed, transformed, and loaded so users can access it through BI tools. This allows organizations to analyze customers and data more holistically.
- The main components of a data warehouse are the load manager, warehouse manager, query manager, and end-user access tools. The ETL process
Data warehousing involves integrating data from multiple sources into a single database to support analysis and decision making. It includes cleaning, integrating, and consolidating data. A data warehouse is subject-oriented, integrated, non-volatile, and time-variant. It differs from a transactional database by collecting extensive data for analytics rather than real-time transactions. A typical architecture includes data storage, an OLAP server for analysis, and front-end tools. Data is mined for patterns to devise sales and profit strategies. There are three main types: an enterprise data warehouse serving the whole organization, an operational data store refreshing in real-time, and departmental data marts.
Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data.
According to Inmon, a data warehouse is a subject oriented,
integrated, time-variant, and non-volatile collection of data. He defined the terms
in the sentence as follows:
This document provides an overview of data warehousing and related concepts. It begins with definitions of key terms like data warehousing, data marts, and OLAP. It then covers the history and evolution of data warehousing in organizations. The document outlines the typical architecture of a data warehouse, including sources, integration, and metadata. It discusses benefits like providing a customer-centric view and removing barriers between functions. It also notes some disadvantages like latency and maintenance costs. Finally, it briefly touches on strategic uses, data mining, and text mining.
Data warehousing provides consolidated historical data from multiple sources to support analysis and strategic decision-making. A data warehouse is subject-oriented, integrated, stores time-variant data nonvolatile, and is maintained separately from operational databases. It differs from operational databases which focus on current data and transactions, while data warehouses integrate historical data from different sources and organizations to support analysis and informed decisions. Data warehouses are constructed separately to promote high performance of both operational and analytical systems.
this is the ppt this contains definition of data ware house , data , ware house, data modeling , data warehouse architecture and its type , data warehouse types, single tire, two tire, three tire .
This document discusses data warehousing and decision support systems. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data used to support management decision making. It describes key features of a data warehouse including being subject-oriented, integrated, time-variant, and non-volatile. The document also discusses the need for decision support systems in business and different architectural styles for data warehousing like OLTP and OLAP.
Data warehousing involves collecting data from different sources and organizing it in a way that allows for analysis to make business decisions. It provides a single, complete view of data that end users can easily understand. A data warehouse stores integrated data from multiple sources and provides historical views of data to support analysis. It allows organizations to access critical information to support reporting, queries and decision making. Common applications of data warehousing include banking, healthcare, airlines and telecommunications.
This document defines key concepts in data warehousing including data warehouses, data marts, and ETL (extract, transform, load). It states that a data warehouse is a non-volatile collection of integrated data from multiple sources used to support management decision making. A data mart contains a single subject area of data. ETL is the process of extracting data from source systems, transforming it, and loading it into a data warehouse or data mart.
ETL processes , Datawarehouse and Datamarts.pptxParnalSatle
The document discusses ETL processes, data warehousing, and data marts. It defines ETL as extracting data from source systems, transforming it, and loading it into a data warehouse. Data warehouses integrate data from multiple sources to support business intelligence and analytics. Data marts are focused subsets of data warehouses that serve specific business functions or departments. The document outlines the key components and architecture of data warehousing systems, including source data, data staging, data storage in warehouses and marts, and analytical applications.
Data Warehouse – Introduction, characteristics, architecture, scheme and modelling, Differences between operational database systems and data warehouse.
This document provides an overview of data warehousing concepts. It defines a data warehouse as a collection of data marts representing historical data from different company operations. It discusses the top-down and bottom-up approaches to building a data warehouse, as well as considerations for data warehouse design including data content, metadata, data distribution, and tools. Finally, it briefly describes different architectures for mapping a data warehouse to a multiprocessor system, including shared memory, shared disk, and shared nothing architectures.
Practice best Data warehousing interview questions and answers for the best preparation of the data warehousing interview. these interview questions are very popular and asked various times in data warehousing interview.
Vision and Goals: The primary aim of the 1st Defence Tech Meetup is to create a Defence Tech cluster in Portugal, bringing together key technology and defence players, accelerating Defence Tech startups, and making Portugal an attractive hub for innovation in this sector.
Historical Context and Industry Evolution: The presentation provides an overview of the evolution of the Portuguese military industry from the 1970s to the present, highlighting significant shifts such as the privatisation of military capabilities and Portugal's integration into international defence and space programs.
Innovation and Defence Linkage: Emphasis on the historical linkage between innovation and defence, citing examples like the military genesis of Silicon Valley and the Cold War's technological dividends that fueled the digital economy, highlighting the potential for similar growth in Portugal.
Proposals for Growth: Recommendations include promoting dual-use technologies and open innovation, streamlining procurement processes, supporting and financing new ICT/BTID companies, and creating a Defence Startup Accelerator to spur innovation and economic growth.
Current and Future Technologies: Discussion on emerging defence technologies such as drone warfare, advancements in AI, and new military applications, along with the importance of integrating these innovations to enhance Portugal's defence capabilities and economic resilience.
How Communicators Can Help Manage Election Disinformation in the WorkplaceMariumAbdulhussein
A study featuring research from leading scholars to breakdown the science behind disinformation and tips for organizations to help their employees combat election disinformation.
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Progress Report - Qualcomm AI Workshop - AI available - everywhereAI summit 1...Holger Mueller
Qualcomm invited analysts and media for an AI workshop, held at Qualcomm HQ in San Diego, June 26th. My key takeaways across the different offerings is that Qualcomm us using AI across its whole portfolio. Remarkable to other analyst summits was 50% of time being dedicated to demos / hands on exeriences.
2. Definition
•In computing, a data warehouse (DW or DWH),
also known as an enterprise data
warehouse (EDW), is a system used
for reporting and data analysis, and is considered a
core component of business intelligence. DWs are
central repositories of integrated data from one or
more disparate sources. They store current and
historical data in one single place that are used for
creating analytical reports for workers throughout the
enterprise.
3. The Concept of Data Warehouse
• The concept of data warehousing dates back to the late 1980s
• when IBM researchers Barry Devlin and Paul Murphy developed
the "business data warehouse". In essence, the data warehousing
concept was intended to provide an architectural model for the
flow of data from operational systems to decision support
system. The concept attempted to address the various problems
associated with this flow, mainly the high costs associated with it.
4. The Concept of Data Warehouse
• A data warehouse is a relational database that is
designed for query and analysis rather than for
transaction processing. It usually contains historical data
derived from transaction data, but it can include data
from other sources. It separates analysis workload from
transaction workload and enables an organization to
consolidate data from several sources.
• In addition to a relational database, a data warehouse
environment includes an extraction, transportation,
transformation, and loading (ETL) solution, an online
analytical processing (OLAP) engine, client analysis
tools, and other applications that manage the process of
gathering data and delivering it to business users.
5. Characteristics of a data warehouse
A common way of introducing data warehousing is to
refer to the characteristics of a data warehouse as set
forth by :
Subject Oriented
Integrated
Nonvolatile
Time Variant
6. • Subject Oriented
• Data warehouses are designed to help you analyze data. For example, to learn more
about your company's sales data, you can build a warehouse that concentrates on sales.
Using this warehouse, you can answer questions like "Who was our best customer for
this item last year?" This ability to define a data warehouse by subject matter, sales in
this case, makes the data warehouse subject oriented.
• Integrated
• Integration is closely related to subject orientation. Data warehouses must put data
from disparate sources into a consistent format. They must resolve such problems as
naming conflicts and inconsistencies among units of measure. When they achieve this,
they are said to be integrated.
• Nonvolatile
• Nonvolatile means that, once entered into the warehouse, data should not change. This
is logical because the purpose of a warehouse is to enable you to analyze what has
occurred.
• Time Variant
• In order to discover trends in business, analysts need large amounts of data. A data
warehouse's focus on change over time is what is meant by the term time variant.
7. Data Mart
• A data mart is the access layer of the data warehouse environment that is
used to get data out to the users. The data mart is a subset of the data
warehouse and is usually oriented to a specific business line or team.
• A data mart is basically a condensed and more focused version of a data
warehouse that reflects the regulations and process specifications of each
business unit within an organization. Each data mart is dedicated to a specific
business function or region. This subset of data may span across many or all
of an enterprise’s functional subject areas. It is common for multiple data
marts to be used in order to serve the needs of each individual business unit
(different data marts can be used to obtain specific information for various
enterprise departments, such as accounting, marketing, sales, etc.)
8. Types of Data Stored in a Data
Warehouse
• Historical Data
A data warehouse typically contains several years of historical data.
The amount of data that you decide to make available depends on
available disk space and the types of analysis that you want to
support. This data can come from your transactional database
archives or other sources.
9. • Metadata is "data [information] that provides information about
other data". Three distinct types of metadata exist: descriptive
metadata, structural metadata, and administrative metadata.
Descriptive metadata describes a resource for purposes such as
discovery and identification. It can include elements such as title,
abstract, author, and keywords.
Structural metadata is metadata about containers of data and
indicates how compound objects are put together, for example,
how pages are ordered to form chapters. It describes the types,
versions, relationships and other characteristics of digital
materials.
Administrative metadata provides information to help manage a
resource, such as when and how it was created, file type and
other technical information, and who can access it.
10. • Derived data
A derived data element is a data element derived from other data
elements using a mathematical, logical, or other type of
transformation, e.g. arithmetic formula, composition, aggregation
• Raw data
Also known as primary data, is data (e.g., numbers, instrument
readings, figures, etc.) collected from a source.
11.
12. Business Intelligence and Data Warehousing
One ultimate use of the data gathered and processed in the data life cycle is
for business intelligence.
Business intelligence generally involves the creation or use of a data
warehouse and/or data mart for storage of data, and the use of front-end
analytical tools such as Oracle’s Sales Analyzer and Financial Analyzer or
Micro Strategy’s Web.
Such tools can be employed by end users to access data, ask queries, request
ad hoc (special) reports, examine scenarios, create CRM activities, devise
pricing strategies, and much more.
More advanced applications of business intelligence include outputs such as:
• financial modeling
• budgeting
• resource allocation
• and competitive intelligence.
13. Data Warehouse Applications
oRetail Industry: Forecasting, Market research,
Merchandising etc.
oManufacturing and distribution : Sales history/trends,
Market demand projects etc.
oBanks : Spot market trends, Marketing, Credit cards etc.
oInsurance Companies : Property and casualty fraud etc.
oHealth Care Providers : Fraud detection, Patient
matching etc.
14. Data Warehouse Applications
o Government Agencies : Auditing tax records, information sharing
across different agencies etc.
o Internet Companies : Analyzing shopping behavior, CRM etc.
o Telecommunications : Telemarketing, Product development etc.
o Sports : Analyzing strategies, Winning player combinations etc.
15. Data Warehouse Sizes
Terabyte (10^12) - Walmart (24 TB)
Petabyte (10^15) - Geographic Information Systems
Exabyte (10^18) - National Medical Association
Zettabyte (10^21) - Weather Images
Zottabyte (10^24) - Intelligence Agency (Video)
Editor's Notes
في الحوسبة ، يعد مستودع البيانات (DW أو DWH) ، المعروف أيضًا باسم مستودع بيانات المؤسسة (EDW) ، نظامًا يستخدم في إعداد التقارير وتحليل البيانات ، ويعتبر مكونًا أساسيًا في ذكاء الأعمال. تعتبر DWs مستودعات مركزية للبيانات المتكاملة من واحد أو أكثر من المصادر المتباينة. تقوم بتخزين البيانات الحالية والتاريخية في مكان واحد تستخدم لإنشاء تقارير تحليلية للعمال في جميع أنحاء المؤسسة.
يعود مفهوم تخزين البيانات إلى أواخر الثمانينات
عندما طور باحثون IBM باري ديفلين وبول مورفي "مستودع بيانات الأعمال". في جوهره ، كان المقصود من مفهوم تخزين البيانات توفير نموذج معماري لتدفق البيانات من الأنظمة التشغيلية إلى نظام دعم القرار. لقد حاول المفهوم معالجة مختلف المشاكل المرتبطة بهذا التدفق ، خاصة التكاليف المرتفعة المرتبطة به.
مستودع البيانات عبارة عن قاعدة بيانات علائقية مصممة للاستعلام والتحليل بدلاً من معالجة المعاملات. عادةً ما تحتوي على بيانات تاريخية مشتقة من بيانات المعاملات ، ولكنها يمكن أن تتضمن بيانات من مصادر أخرى. يفصل عبء العمل التحليل من عبء العمل المعاملة وتمكين مؤسسة لتوحيد البيانات من مصادر متعددة.
بالإضافة إلى قاعدة البيانات العلائقية ، تشتمل بيئة مستودع البيانات على حل الاستخراج والنقل والتحويل والتحميل (ETL) ، ومحرك معالج تحليلي (OLAP) على الإنترنت ، وأدوات تحليل العميل ، وتطبيقات أخرى تدير عملية جمع البيانات و تقديمها لمستخدمي الأعمال.
من الطرق الشائعة لتقديم مستودعات البيانات الرجوع إلى خصائص مستودع البيانات كما هو محدد في:
الموضوع موجه
متكامل
غير متطاير
تغير الوقت
الموضوع موجه
تم تصميم مستودعات البيانات لمساعدتك على تحليل البيانات. على سبيل المثال ، لمعرفة المزيد عن بيانات مبيعات شركتك ، يمكنك إنشاء مستودع يركز على المبيعات. باستخدام هذا المستودع ، يمكنك الإجابة عن أسئلة مثل "من كان أفضل عميل لدينا لهذا البند في العام الماضي؟" هذه القدرة على تحديد مستودع البيانات حسب الموضوع ، والمبيعات في هذه الحالة ، يجعل موضوع مستودع البيانات موجهًا.
متكامل
يرتبط التكامل ارتباطًا وثيقًا بتوجيه الموضوع. يجب أن تضع مستودعات البيانات البيانات من مصادر متباينة في تنسيق ثابت. يجب أن تحل مشاكل مثل تسمية الصراعات وعدم التناسق بين وحدات القياس. عندما يحققون ذلك ، يقال إنهم متكاملون.
غير متطاير
تعني كلمة Nonvolatile أنه بمجرد إدخالها في المستودع ، يجب ألا تتغير البيانات. هذا أمر منطقي لأن الغرض من المستودع هو تمكينك من تحليل ما حدث.
تغير الوقت
من أجل اكتشاف الاتجاهات في مجال الأعمال ، يحتاج المحللون إلى كميات كبيرة من البيانات. إن تركيز مستودع البيانات على التغيير بمرور الوقت هو المقصود بالمصطلح "متغير الوقت".
سوق البيانات هو طبقة الوصول لبيئة مستودع البيانات التي يتم استخدامها للحصول على البيانات إلى المستخدمين. سوق البيانات هو مجموعة فرعية من مستودع البيانات وعادة ما يتم توجيهه إلى خط عمل أو فريق عمل محدد.
إن سوق البيانات هو في الأساس نسخة مكثفة وأكثر تركيزًا من مستودع البيانات الذي يعكس اللوائح ومواصفات العملية لكل وحدة أعمال داخل المؤسسة. يتم تخصيص كل سوق بيانات لوظيفة أو منطقة عمل محددة. قد تمتد هذه المجموعة الفرعية من البيانات عبر العديد من مجالات الموضوعات الوظيفية الخاصة بالمؤسسة أو كلها. من الشائع استخدام العديد من سجلات البيانات من أجل تلبية احتياجات كل وحدة أعمال فردية (يمكن استخدام سجلات البيانات المختلفة للحصول على معلومات محددة لمختلف إدارات المؤسسات ، مثل المحاسبة والتسويق والمبيعات ، إلخ.
البيانات التاريخية
عادةً ما يحتوي مستودع البيانات على عدة سنوات من البيانات التاريخية. يعتمد مقدار البيانات التي تقرر توفيرها على مساحة القرص المتوفرة وأنواع التحليل التي تريد دعمها. يمكن أن تأتي هذه البيانات من أرشيف قواعد بيانات المعاملات أو مصادر أخرى.
لبيانات الوصفية هي "بيانات [معلومات] توفر معلومات حول بيانات أخرى". توجد ثلاثة أنواع مميزة من البيانات الوصفية: البيانات الوصفية الوصفية والبيانات الوصفية الهيكلية والبيانات الوصفية الإدارية.
تصف البيانات الوصفية الوصفية أحد الموارد لأغراض مثل الاكتشاف والتعرف. يمكن أن تتضمن عناصر مثل العنوان والملخص والمؤلف والكلمات الرئيسية.
البيانات الوصفية الهيكلية عبارة عن بيانات وصفية عن حاويات البيانات وتشير إلى كيفية تجميع الكائنات المركبة ، على سبيل المثال ، كيفية ترتيب الصفحات لتشكيل فصول. فهو يصف الأنواع والإصدارات والعلاقات والخصائص الأخرى للمواد الرقمية.
توفر البيانات الوصفية الإدارية معلومات للمساعدة في إدارة أحد الموارد ، مثل وقت وكيفية إنشاء الملف ونوع الملف والمعلومات الفنية الأخرى ، ومن يمكنه الوصول إليه.
البيانات المشتقة
عنصر البيانات المشتقة هو عنصر بيانات مشتق من عناصر البيانات الأخرى باستخدام نوع رياضي أو منطقي أو أي نوع آخر من التحويل ، على سبيل المثال ، صيغة حسابية ، تكوين ، تجميع
مسودة بيانات
تُعرف أيضًا باسم البيانات الأساسية ، وهي البيانات (مثل الأرقام وقراءات الأدوات والأرقام وغير ذلك) التي يتم جمعها من المصدر.
احد الاستخدامات النهائية للبيانات التي تم جمعها ومعالجتها في دورة حياة البيانات هي استخبارات الأعمال.
يتضمن ذكاء الأعمال بشكل عام إنشاء أو استخدام مستودع البيانات و / أو سوق البيانات لتخزين البيانات ، واستخدام أدوات التحليل الأمامية مثل محلل مبيعات Oracle أو محلل مالي أو Web Strategy Strategy.
يمكن استخدام هذه الأدوات من قبل المستخدمين النهائيين للوصول إلى البيانات ، وطرح الاستفسارات ، وطلب تقارير مخصصة (خاصة) ، ودراسة السيناريوهات ، وإنشاء أنشطة CRM ، ووضع استراتيجيات التسعير ، وأكثر من ذلك بكثير.
تتضمن التطبيقات الأكثر تقدمًا لذكاء الأعمال نواتج مثل:
• النماذج المالية
• الميزنة
• تخصيص الموارد
• والذكاء التنافسي.
صناعة البيع بالتجزئة: التنبؤ ، أبحاث السوق ، تجارة الخ
التصنيع والتوزيع: تاريخ المبيعات / الاتجاهات ، ومشاريع الطلب في السوق إلخ.
البنوك: اتجاهات السوق الفورية ، التسويق ، بطاقات الائتمان ، إلخ.
شركات التأمين: الاحتيال في الممتلكات والاصابات الخ
مقدمي الرعاية الصحية: كشف الاحتيال ، مطابقة المرضى الخ
الوكالات الحكومية: تدقيق السجلات الضريبية وتبادل المعلومات بين الوكالات المختلفة إلخ.
شركات الإنترنت: تحليل سلوك التسوق ، CRM إلخ.
الاتصالات: التسويق عبر الهاتف ، وتطوير المنتجات ، إلخ.
الرياضة: تحليل الاستراتيجيات ، ومجموعات لاعب الفوز وما إلى ذلك.
تيرابايت (10 ^ 12) - وول مارت (24 تيرابايت)
Petabyte (10 ^ 15) - نظم المعلومات الجغرافية
Exabyte (10 ^ 18) - الجمعية الطبية الوطنية
Zettabyte (10 ^ 21) - صور الطقس
Zottabyte (10 ^ 24) - وكالة الاستخبارات (فيديو)