Data Governance and Metadata Management DATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
Data Governance and Metadata Management DATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Data Catalogues - Architecting for Collaboration & Self-ServiceDATAVERSITY
The interest in Data Catalogs is growing as more business & technical users are looking to gain insight from data using a self-service approach. Architectural techniques for Data Provisioning and Metadata Cataloging have evolved to cater to these new audiences and ways of working. This webinar provides concrete methods of architecting your Self-service BI & Analytics environment to foster collaboration while at the same time maintaining Data Quality and reducing risk.
The Zen of DataOps – AWS Lake Formation and the Data Supply Chain PipelineAmazon Web Services
Many organizations have adopted or are in the process of adopting DevOps methodologies in their quest to accelerate the delivery of software capabilities, features, and functionalities to support their organizational objectives. By applying the same practices, DataOps aims to provide the same level of agility in delivering data and information to the organization. AWS Lake Formation, in coordination with other AWS Services, enables DevOps methodologies to be realized through the Data Supply Chain Pipeline.
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.
LDM Slides: How Data Modeling Fits into an Overall Enterprise ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as it relates to data and its business impact across the organization.
Join this webinar for a discussion on how a data model can be combined with an overall enterprise architecture for enhanced business value and success.
Big data is a term that describes the large volume of data may be both structured and unstructured.
That inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Data Catalogues - Architecting for Collaboration & Self-ServiceDATAVERSITY
The interest in Data Catalogs is growing as more business & technical users are looking to gain insight from data using a self-service approach. Architectural techniques for Data Provisioning and Metadata Cataloging have evolved to cater to these new audiences and ways of working. This webinar provides concrete methods of architecting your Self-service BI & Analytics environment to foster collaboration while at the same time maintaining Data Quality and reducing risk.
The Zen of DataOps – AWS Lake Formation and the Data Supply Chain PipelineAmazon Web Services
Many organizations have adopted or are in the process of adopting DevOps methodologies in their quest to accelerate the delivery of software capabilities, features, and functionalities to support their organizational objectives. By applying the same practices, DataOps aims to provide the same level of agility in delivering data and information to the organization. AWS Lake Formation, in coordination with other AWS Services, enables DevOps methodologies to be realized through the Data Supply Chain Pipeline.
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.
LDM Slides: How Data Modeling Fits into an Overall Enterprise ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as it relates to data and its business impact across the organization.
Join this webinar for a discussion on how a data model can be combined with an overall enterprise architecture for enhanced business value and success.
Big data is a term that describes the large volume of data may be both structured and unstructured.
That inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
This is a slide deck that was assembled as a result of months of Project work at a Global Multinational. Collaboration with some incredibly smart people resulted in content that I wish I had come across prior to having to have assembled this.
DAS Slides: Metadata Management From Technical Architecture & Business Techni...DATAVERSITY
Metadata provides context for the “who, what, when, where, and why” of data, and is of critical interest in today’s data-driven business environment. Since metadata is created and used by both business and IT, architectural and organizational techniques need to encompass a holistic approach across the organization to address all audiences. This webinar provides practical ways to manage metadata in your organization using both technical architecture and business techniques.
I built this presentation for Informatica World in 2006. It is all about Data Administration, Data Quality and Data Management. It is NOT about the Informatica product. This presentation was a hit, with standing room only full of about 150 people. The content is still useful and applicable today. If you want to use my material, please put (C) Dan Linstedt, all rights reserved, http://LearnDataVault.com
Tool Comparison: Enterprise BI vs Self-Service Analytics: Choosing the Best T...Senturus
Benefits and drawbacks of enterprise BI platforms and self-service analytics tools. Includes a comparison matrix developed using our real-life experience working with both types of solutions. View the webinar video recording and download the deck at: http://www.senturus.com/resources/enterprise-bi-platforms-vs-self-service-analytics-tools-2017/.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Power BI Governance and Development Best Practices - Presentation at #MSBIFI ...Jouko Nyholm
Selected slides from presentation regarding Power BI Governance and Development Best Practices. Presentation was held at MS BI & Power BI User Group Finland event 12.6.2018 at Microsoft Flux, Helsinki.
Without the animations & hands-on demos the slides do not tell the whole story, but hopefully valuable to some nevertheless.
Business intelligence and analytics both refer to maximize the value of your data to make better decisions, ALTEN CAlsoft Labs helps
enterprises accelerate business intelligence by providing the most comprehensive, integrated and easy-to-use reporting and analytics features with its industry specific analytics solutions and best in-class technology.
LDM Webinar: Data Modeling & Business IntelligenceDATAVERSITY
Business Intelligence (BI) is a valuable way to use information to show the overall health and performance of the organization. At its core is quality, well-structured data that allows for successful reporting and analytics. A data model helps provide both the business definitions as well as the structural optimization needed for successful BI implementations.
Join this webinar to see how a data model underpins business intelligence and analytics in today’s organization.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
Bi 4
1. “Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
Chapter 5
BI Definitions and
Concepts
2. Learning Objectives and Learning Outcomes
Learning Objectives Learning Outcomes
BI Definitions & Concepts
1. BI Framework
2. Data Warehousing concepts and its role in
BI
3. BI Infrastructure Components – BI
Process
4. BI Technology
5. BI Roles & Responsibilities
6. Business Applications of BI
7. Best practices in BI/DW
a) To understand the BI
framework
b) To be able to apply best
practices in BI/DW
3. Session Plan
Lecture time : 90 minutes approx.
Q/A : 15 minutes
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
4. Agenda
• BI Framework
– Business Layer
– Administration and Operation layer
– Implementation layer
• Who is BI for?
– The growing Business Intelligence market
• Type of BI users
– Casual Users
– Power Users
• BI Applications
• BI roles and responsibilities
• BI DW Best practices
• Open source BI Tools
• Popular BI tools
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
5. BI Framework
Business Requirement
BI ArchitectureProgramManagement
DataResourceAdministration
Development
BI&DWOperations
Business Value
Business Applications
Data Sources
Data Acquisition, Cleaning & Integration
Data Stores
Information Delivery Business Analytics
Data Warehousing
Information Services
Source: TDWI
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
7. Business Layer
Business requirements: The requirements are a product of three steps of a
process that includes:
Business drivers (the impulses that initiate the need to act).
Examples: changing workforce, changing labor laws, changing
economy, changing technology, etc.
Business goals (the targets to be achieved in response to the business
drivers).
Examples: increased productivity, improved market share, improved
profit margins, improved customer satisfaction, cost reduction, etc.
Business strategies (the planned course of action that will help achieve
the set goals).
Examples: outsourcing, global delivery model, partnerships, customer
retention programs, employee retention programs, competitive pricing,
etc.
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
8. Business Layer
Business Value: Business value can be measured in terms of ROI (Return on
Investment), ROA (Return on Assets), TCO (Total Cost of Ownership),
TVO (Total Value of Ownership), etc.
Program management: It is the component that ensures people, projects and
priorities work in a manner in which individual processes are compatible
with each other; so as to ensure seamless integration and smooth
functioning of the entire program.
Development: The process of development consists of database/data-
warehouse development (consisting of ETL, data profiling, data cleansing
and database tools), data integration system development (consists of data
integration tools and data quality tools) and business analytics development
(about processes and various technologies used).
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
9. Explain
Explain the terms ROI, ROA, TCO and TVO giving appropriate examples
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
10. Administration and Operation Layer
BI ARCHITECTURE
BI AND DW
(DATA WAREHOUSE)
OPERATIONS
DATA RESOURCE
ADMINISTRATION
BUSINESS APPLICATIONS
ADMINISTRATION
AND
OPERATION LAYER
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
11. Administration and Operations Layer
• Should follow design standards
• Must have a logically apt data model
• Metadata should be of high standard
DATA
• Performed according to business semantics and rules
• During integration, certain processing standards have
to be followed
• Data must be consistent
INTEGRATION
• Information derived from data that has been
integrated should be usable, findable and as per the
requirements
INFORMATION
• Technology used for deriving information must be
accessible
• Also, it should have a good user-interface
• Should support analysis, decision support, data and
storage management
TECHNOLOGY
• Consists of different roles and responsibilities, like
management, development, support and usage roles
ORGANIZATION
BI Architecture
12. Administration and Operations Layer
BI and DW operations: Data warehouse administration requires the usage
of various tools to monitor the performance and usage of the warehouse, and
perform administrative tasks on it. Some of these tools would be:
• Backup and restore
• Security
• Configuration management
• Database management
Data resource administration: Involves data governance and metadata
management.
Data governance is a technique for controlling data quality, which is used to
assess, improve, manage and maintain information. It helps to define
standards that are required to maintain data quality. The distribution of roles
for governance of data is as follows:
• Data ownership
• Data stewardship
• Data custodianship “Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
13. Metadata management: Metadata is data about data.
Metadata can be divided into four groups:
– Business metadata
– Process metadata
– Technical metadata
– Application metadata
Administration and Operations Layer
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
14. Answer a Quick Question
Given your understanding of RDBMS, explain metadata with an example
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
15. 15
Data definitions
Metrics definitions
Subject models
Data models
Business rules
Data rules
Data owners/stewards, etc.
Source/target maps
Transformation rules
Data cleansing rules
Extract audit trail
Transform audit trail
Load audit trail
Data quality audit
etc.
Data locations
Data formats
Technical names
Data sizes
Data types
Indexing
Data structures
etc.
Data access history:
Who is accessing?
Frequency of access?
When accessed?
How accessed?
etc.
Business Metadata Process Metadata
Technical Metadata
Application Metadata
Metadata Management
Administration and Operations Layer
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
16. Explain
Explain the various types of metadata with appropriate examples
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
18. Implementation Layer
Data Source in New York
Data Source in Washington
Data Source in Philadelphia
Data Source in Chicago
Extract
Clean
Transform
Load
Refresh
DataWarehouse
Query/
Report/
Analysis
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
19. Implementation Layer
Information services:
• It is not only the process of producing information; rather, it involves
ensuring that the information produced is aligned with business
requirements and can be acted upon to produce value for the company.
• Information is delivered in the form of KPI’s, reports, charts, dashboards or
scorecards, etc., or in the form of analytics.
• Data mining is a practice used to increase the body of knowledge.
• Applied analytics is generally used to drive action and produce outcomes.
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
20. Answer a Quick Question
Is BI only for managers?
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
21. Who is BI for?
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
22. Types of BI Users
Type of user Casual users/
Information consumers
Power users/Information
producers
Example of
such users
Executives, managers,
customers, suppliers,
field/operation workers,
etc.
SAS, SPSS developers,
administrators, business
analysts, analytical
modelers, IT professionals,
etc.
Usage Information consumers Information producers
Data Access Tailor made to suit the
needs of their respective
role
Ad hoc/exploratory
Tools Pre-defined
reports/dashboards
Advanced Analytical/
Authoring tools
Sources Data warehouse/Data
Marts
Data Warehouse/Data
Marts (both internal and
external)
23. BI Applications
BI applications can be divided into:
• Technology solutions
– DSS
– EIS
– OLAP
– Managed Query and Reporting
– Data Mining
• Business Solutions
– Performance Analysis
– Customer Analysis
– Market Place Analysis
– Productivity Analysis
– Sales Channel Analysis
– Behavioral Analysis
– Supply Chain Analysis
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
24. Explain
Explain giving suitable examples:
“Performance analysis”, “Customer analysis”, “Marketplace analysis”,
“Productivity analysis” and “Sales Channel analysis”
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
25. BI Roles and Responsibilities
Program Roles Project Roles
Business Manager
BI Program Manager BI Business Specialist
BI Data Architect BI Project Manager
BI ETL Architect Business Requirements Analyst
BI Technical Architect Decision Support Analyst
Metadata Manager BI Designer
BI Administrator ETL Specialist
Data Administrator
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
26. BI DW Best Practices
The list of best practices is adapted from an article TDWI’s FlashPoint
e-newsletter of April 10, 2003.
• Practice “User First” Design
• Create New Value
• Attend to Human Impacts
• Focus on Information and Analytics
• Practice Active Data Stewardship
• Manage BI as a long term investment
• Reach out with BI/DW solutions
• Make BI a business Initiative
• Measure Results
• Attend to strategic Positioning
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
27. Do It exercise
Visit www.tdwi.org to read more about BI DW best practices
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
28. Open Source BI Tools
RDBMS MySQL, Firebird
ETL Tools
Pentaho Data Integration (formerly
called Kettle), SpagoBI
Analysis Tools Weka, RapidMiner, SpagoBI
Reporting Tools/Ad Hoc
Querying/Visualization
Pentaho, BIRT, Actuate, Jaspersoft
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
29. Popular BI Tools
MICROSOFT
SYBASE IQ
BUSINESS
OBJECTS 5.x
ORACLE
ORACLE 11G R2 HYPERION 11.1.3
NETEZZA 4.6, DB2 SPSS 9
MYSQL
PENTAHO
WEKA
DBMS
ETL, DATA
INTEGRATION
OLAP,
DATA WAREHOUSING
REPORTING,
AD HOC QUERYING
ANALYSIS
ANALYTICS,
VISUALIZATION,
MINING
Back End Front EndBI Functions
IBM
DATASTAGE 8.5 COGNOS v10
ORACLE WAREHOUSE BUILDER
SQL SERVER 2008 SSIS 2008 SSRS 2008 SSAS 2008
SAP
SIEBEL 8.1
NCR TERADATA 13
INFORMATICA 9
MICROSTRATEGY 9
SAS 9.2
SAP
AB INITIO 3.0.2 SPOTFIRE (TIBCO) 3.2.x
BIRT
RAPIDMINER
30. Ask a few participants of the learning program to summarize the lecture.
Summary please…
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
