This document provides sample requirements for a data warehousing project at a telecommunications company. It includes examples of business, data, query, and interface requirements. The business requirements sample outlines requirements for collecting and analyzing customer, organization, and individual data. The data requirements sample defines dimensions for party (customer) data and hierarchies. The performance measures sample defines a measure for vanilla rated call revenue amount.
03. Business Information Requirements TemplateAlan D. Duncan
A template for the clear and unambiguous definition of business data and information requirements. (cf. “Business Requirements Document”, “Functional Specification” or similar from standard SDLC processes). As such, the contents will typically form the basis for population and publication of a business glossary of information terms.
Presentation on Business Requirements gathering for Business Intelligence from our BI Practice Lead. Detailed instruction on how to maximize your time in gathering requirements and ensure you capture what is important to the user. Requirements gathering is critical to the success of a BI project.
Enterprise Architecture vs. Data 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 they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
03. Business Information Requirements TemplateAlan D. Duncan
A template for the clear and unambiguous definition of business data and information requirements. (cf. “Business Requirements Document”, “Functional Specification” or similar from standard SDLC processes). As such, the contents will typically form the basis for population and publication of a business glossary of information terms.
Presentation on Business Requirements gathering for Business Intelligence from our BI Practice Lead. Detailed instruction on how to maximize your time in gathering requirements and ensure you capture what is important to the user. Requirements gathering is critical to the success of a BI project.
Enterprise Architecture vs. Data 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 they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
A template to define an outline structure for the clear and unambiguous definition of the discreet component data elements (atomic items of Entity/Attribute/Relationship/Rule) within the Logical layer of an Enterprise Information Model (a.k.a. Canonical Model).
Gathering And Documenting Your Bi Business RequirementsWynyard Group
Business requirements are critical to any project. Recent studies show that 70% of organisations fail to gather business requirements well. What is worse is that poor requirements can lead a project to over spend its original budget by 95%.
Business Intelligence and Performance Management projects are no different. This session will provide a series of tips, techniques and ideas on how you can discover, analyse, understand and document your business requirements for your BI and PM projects. This session will also touch on specific issues, hurdles and obstacle that occur for a typical BI or PM project
• The importance of business requirements and a well defined business requirements process
• Understanding the difference between a “wish-list” or vision and business requirements
• The need and benefits of having a business traceability matrix
Start your BI projects on the right foot – understand your requirements
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
White Paper - Data Warehouse Documentation RoadmapDavid Walker
All projects need documentation and many companies provide templates as part of a methodology. This document describes the templates, tools and source documents used by Data Management & Warehousing. It serves two purposes:
• For projects using other methodologies or creating their own set of documents to use as a checklist. This allows the project to ensure that the documentation covers the essential areas for describing the data warehouse.
• To demonstrate our approach to our clients by describing the templates and deliverables that are produced.
Documentation, methodologies and templates are inherently both incomplete and flexible. Projects may wish to add, change, remove or ignore any part of any document. Some may also believe that aspects of one document would sit better in another. If this is the case then users of this document and these templates are encouraged to change them to fit their needs.
Data Management & Warehousing believes that the approach or methodology for building a data warehouse should be to use a series of guides and checklists. This ensures that small teams of relatively skilled resources developing the system can cover all aspects of the project whilst being free to deal with the specific issues of their environment to deliver exceptional solutions, rather than a rigid methodology that ensures that large teams of relatively unskilled staff can meet a minimum standard.
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help?
Given in Montreal on 14-Dec-2021
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
Data Architecture is foundational to an information-based operational environment. Without proper structure and efficiency in organization, data assets cannot be utilized to their full potential, which in turn harms bottom-line business value. When designed well and used effectively, however, a strong Data Architecture can be referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations.
The goal of this webinar is not to instruct you in being an outright Data Architect, but rather to enable you to envision a number of uses for Data Architectures that will maximize your organization’s competitive advantage. With that being said, we will:
Discuss Data Architecture’s guiding principles and best practices
Demonstrate how to utilize Data Architecture to address a broad variety of organizational challenges and support your overall business strategy
Illustrate how best to understand foundational Data Architecture concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
The Data Driven University - Automating Data Governance and Stewardship in Au...Pieter De Leenheer
Data Governance and Stewardship requires automation of business semantics management at its nucleus, in order to achieve data trust between business and IT communities in the organization. University divisions operate highly autonomously and decentralized, and are often geographically distributed. Hence, they benefit more from an collaborative and agile approach to Data Governance and Stewardship approach that adapts to its nature.
In this lecture, we start by reviewing 'C' in ICT and reflect on the dilemma: what is the most important quality of data being shared: truth or trust? We review the wide spectrum of business semantics. We visit the different phases of growing data pain as an organization expands, and we map each phase on this spectrum of semantics.
Next, we introduce our principles and framework for business semantics management to support Data Governance and Stewardship focusing on the structural (what), processual (how) and organizational (who) components. We illustrate with use cases from Stanford University, George Washington University and Public Science and Innovation Administrations.
07. Analytics & Reporting Requirements TemplateAlan D. Duncan
This document template defines an outline structure for the clear and unambiguous definition of analytics & reporting outputs (including standard reports, ad hoc queries, Business Intelligence, analytical models etc).
Building an Effective Data & Analytics Operating Model A Data Modernization G...Mark Hewitt
This is the age of analytics—information resulting from the systematic analysis of data.
Insights gained from applying data and analytics to business allows large and small organizations across diverse industries—be it healthcare, retail, manufacturing, financial, or others—to identify new opportunities, improve core processes, enable continuous learning and differentiation, remain competitive, and thrive in an increasingly challenging business environment.
The key to building a data-driven practice is a Data and Analytics Operating Model (D&AOM) which enables the organization to establish standards for data governance, controls for data flows (both within and outside the organization), and adoption of appropriate technological innovations.
Success measures of a data initiative may include:
• Creating a competitive advantage by fulfilling unmet needs,
• Driving adoption and engagement of the digital experience platform (DXP),
• Delivering industry standard data and metrics, and
• Reducing the lift on service teams.
This green paper lays out the framework for building and customizing an effective data and analytics operating model.
The Importance of Master Data ManagementDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and Master Data Management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI). To that end, attendees of this webinar will learn how to:
Structure their Data Management processes around these principles
Incorporate Data Quality engineering into the planning of reference and MDM
Understand why MDM is so critical to their organization’s overall data strategy
Discuss foundational MDM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
A Work of Zhamak Dehghani
Principal consultant
ThoughtWorks
https://martinfowler.com/articles/data-monolith-to-mesh.html
https://fast.wistia.net/embed/iframe/vys2juvzc3?videoFoam
How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh
Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
BI Dashboard Formula Methodology: How to make your first big data visualizati...BI Brainz
BI Dashboard Formula Methodology Webinar:
http://bidashboardformula.com
Learn how to with Mico Yuk:
Qualify your dashboard project before starting
Transform your KPIs into actionable KPIs
Tell a story with your KPI's and Data
Build mockups right the first time
Boost user adoption using our hacks
Build in any tool!
Capturing Business Requirements For Scorecards, Dashboards And ReportsJulian Rains
This paper helps Management Information and Business Intelligence related projects build a solid foundation for their reporting business requirements gathering. It defines the scope of the information needed to design and build dashboards, scorecards and other types of report.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
A template to define an outline structure for the clear and unambiguous definition of the discreet component data elements (atomic items of Entity/Attribute/Relationship/Rule) within the Logical layer of an Enterprise Information Model (a.k.a. Canonical Model).
Gathering And Documenting Your Bi Business RequirementsWynyard Group
Business requirements are critical to any project. Recent studies show that 70% of organisations fail to gather business requirements well. What is worse is that poor requirements can lead a project to over spend its original budget by 95%.
Business Intelligence and Performance Management projects are no different. This session will provide a series of tips, techniques and ideas on how you can discover, analyse, understand and document your business requirements for your BI and PM projects. This session will also touch on specific issues, hurdles and obstacle that occur for a typical BI or PM project
• The importance of business requirements and a well defined business requirements process
• Understanding the difference between a “wish-list” or vision and business requirements
• The need and benefits of having a business traceability matrix
Start your BI projects on the right foot – understand your requirements
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
White Paper - Data Warehouse Documentation RoadmapDavid Walker
All projects need documentation and many companies provide templates as part of a methodology. This document describes the templates, tools and source documents used by Data Management & Warehousing. It serves two purposes:
• For projects using other methodologies or creating their own set of documents to use as a checklist. This allows the project to ensure that the documentation covers the essential areas for describing the data warehouse.
• To demonstrate our approach to our clients by describing the templates and deliverables that are produced.
Documentation, methodologies and templates are inherently both incomplete and flexible. Projects may wish to add, change, remove or ignore any part of any document. Some may also believe that aspects of one document would sit better in another. If this is the case then users of this document and these templates are encouraged to change them to fit their needs.
Data Management & Warehousing believes that the approach or methodology for building a data warehouse should be to use a series of guides and checklists. This ensures that small teams of relatively skilled resources developing the system can cover all aspects of the project whilst being free to deal with the specific issues of their environment to deliver exceptional solutions, rather than a rigid methodology that ensures that large teams of relatively unskilled staff can meet a minimum standard.
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help?
Given in Montreal on 14-Dec-2021
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
Data Architecture is foundational to an information-based operational environment. Without proper structure and efficiency in organization, data assets cannot be utilized to their full potential, which in turn harms bottom-line business value. When designed well and used effectively, however, a strong Data Architecture can be referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations.
The goal of this webinar is not to instruct you in being an outright Data Architect, but rather to enable you to envision a number of uses for Data Architectures that will maximize your organization’s competitive advantage. With that being said, we will:
Discuss Data Architecture’s guiding principles and best practices
Demonstrate how to utilize Data Architecture to address a broad variety of organizational challenges and support your overall business strategy
Illustrate how best to understand foundational Data Architecture concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
The Data Driven University - Automating Data Governance and Stewardship in Au...Pieter De Leenheer
Data Governance and Stewardship requires automation of business semantics management at its nucleus, in order to achieve data trust between business and IT communities in the organization. University divisions operate highly autonomously and decentralized, and are often geographically distributed. Hence, they benefit more from an collaborative and agile approach to Data Governance and Stewardship approach that adapts to its nature.
In this lecture, we start by reviewing 'C' in ICT and reflect on the dilemma: what is the most important quality of data being shared: truth or trust? We review the wide spectrum of business semantics. We visit the different phases of growing data pain as an organization expands, and we map each phase on this spectrum of semantics.
Next, we introduce our principles and framework for business semantics management to support Data Governance and Stewardship focusing on the structural (what), processual (how) and organizational (who) components. We illustrate with use cases from Stanford University, George Washington University and Public Science and Innovation Administrations.
07. Analytics & Reporting Requirements TemplateAlan D. Duncan
This document template defines an outline structure for the clear and unambiguous definition of analytics & reporting outputs (including standard reports, ad hoc queries, Business Intelligence, analytical models etc).
Building an Effective Data & Analytics Operating Model A Data Modernization G...Mark Hewitt
This is the age of analytics—information resulting from the systematic analysis of data.
Insights gained from applying data and analytics to business allows large and small organizations across diverse industries—be it healthcare, retail, manufacturing, financial, or others—to identify new opportunities, improve core processes, enable continuous learning and differentiation, remain competitive, and thrive in an increasingly challenging business environment.
The key to building a data-driven practice is a Data and Analytics Operating Model (D&AOM) which enables the organization to establish standards for data governance, controls for data flows (both within and outside the organization), and adoption of appropriate technological innovations.
Success measures of a data initiative may include:
• Creating a competitive advantage by fulfilling unmet needs,
• Driving adoption and engagement of the digital experience platform (DXP),
• Delivering industry standard data and metrics, and
• Reducing the lift on service teams.
This green paper lays out the framework for building and customizing an effective data and analytics operating model.
The Importance of Master Data ManagementDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and Master Data Management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI). To that end, attendees of this webinar will learn how to:
Structure their Data Management processes around these principles
Incorporate Data Quality engineering into the planning of reference and MDM
Understand why MDM is so critical to their organization’s overall data strategy
Discuss foundational MDM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
A Work of Zhamak Dehghani
Principal consultant
ThoughtWorks
https://martinfowler.com/articles/data-monolith-to-mesh.html
https://fast.wistia.net/embed/iframe/vys2juvzc3?videoFoam
How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh
Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
BI Dashboard Formula Methodology: How to make your first big data visualizati...BI Brainz
BI Dashboard Formula Methodology Webinar:
http://bidashboardformula.com
Learn how to with Mico Yuk:
Qualify your dashboard project before starting
Transform your KPIs into actionable KPIs
Tell a story with your KPI's and Data
Build mockups right the first time
Boost user adoption using our hacks
Build in any tool!
Capturing Business Requirements For Scorecards, Dashboards And ReportsJulian Rains
This paper helps Management Information and Business Intelligence related projects build a solid foundation for their reporting business requirements gathering. It defines the scope of the information needed to design and build dashboards, scorecards and other types of report.
Brand Building in the Age of Big Data by Mr. Gavin Coombeswkwsci-research
Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
Telco Paper by Blueocean Market IntelligenceCourse5i
At Blueocean, we are committed to work with large telecom providers who want to go for omnichannel experience for their end consumers.
To learn more about our Digital Customer Experience solution and how it can integrate with your existing technology infrastructure, go through this Short Paper on Telco Industry Solutioning.
Layering Common Sense on Top of all that Rocket Science by Prof. Sharon Dunwoodywkwsci-research
Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
Words and More Words: Challenges of Big Data by Prof. Edie Rasmussenwkwsci-research
Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
Patient Powered Research with Big Data and Connected Communities by Assoc. P...wkwsci-research
Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
AWS re:Invent 2016: Predicting Customer Churn with Amazon Machine Learning (M...Amazon Web Services
In this session, we take a specific business problem—predicting Telco customer churn—and explore the practical aspects of building and evaluating an Amazon Machine Learning model. We explore considerations ranging from assigning a dollar value to applying the model using the relative cost of false positive and false negative errors. We discuss all aspects of putting Amazon ML to practical use, including how to build multiple models to choose from, put models into production, and update them. We also discuss using Amazon Redshift and Amazon S3 with Amazon ML.
Mobile Communication and Big Data by Prof. Richard Lingwkwsci-research
Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
A Big Data Telco Solution by Dr. Laura Wynterwkwsci-research
Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
Mr. Mayank Sahai presented at SAS Forum 2011 - one of the largest Analytics conference in India. He enlightened the audience on the role Analytics plays in Customer Management and organizations can maximize the value
Telcos are facing mounting pressure to dramatically increase speed to market and cut costs. But how?
What if you could go to market in half the time using prebuilt libraries of telco offerings—and leveraging the cloud to lower costs?
In this presentation, find out how Capgemini’s end-to-end solution for telcos uses a hybrid cloud and the Oracle Communications Rapid Offer Design and Order Delivery (Oracle Communications RODOD) stack to provide a competitive edge in today’s tough, dynamic environment.
Learn how to accelerate digital transformation, increase agility, and simplify business to better respond to customer expectations and address growth opportunities. See a concrete demonstration of the solution and its best-in-class CX capabilities, processes, and deployment and run services.
First presented at Oracle OpenWorld 2015.
La Dove Associates -- CRM/Customer Care Consulting Overview LaDove Associates
The deck represents a sample of work product from a selection of projects. Of course the real value of our consulting services are the decisions we help to inform.
More Information:
http://flevy.com/browse/flevypro/strengths-and-weaknesses-analysis-1716
Strengths & Weaknesses Analysis is the identification of an organization's strengths and weaknesses that impact its ability to implement a strategic option. This framework validates opportunities for developing a company. It also determines a strategic direction consistent with: Company's Capabilities and Industry Requirements.
This framework is part of the Complete Business Frameworks Reference Guide, a comprehensive collection of 50+ frameworks (spanning 350+ slides). This document is one of the most sold ones on the Flevy documents marketplace.
Got a question about the product? Email us at flevypro@flevy.com. If you cannot view the preview above this document description, go here to view the large preview instead.
Source: Strengths & Weaknesses Analysis PowerPoint document
ABOUT FLEVYPRO
FlevyPro is a subscription service for on-demand business frameworks and analysis tools. FlevyPro subscribers receive access to an exclusive library of curated business documents—business framework primers, presentation templates, Lean Six Sigma tools, and more—among other exclusive benefits.
In this presentation Mark T. Warren (Director of Decision Science) talks about Big Data with Barclaycard, the foundations they built for it and their goals in the long term for it. Warren also discusses Barclaycard's learnings from building the foundation and how they're using these learnings and coping with market change and other challenges that can affect their long term goals.
Commonality Unleashed Across Functions and IndustriesCognizant
Semantics aside, the commonality, or similarity, of processes and functions across industries and business sectors suggests that cross-pollination - or crossover - is a valid approach for addressing the talent gap many companies face.
Taming the regulatory tiger with jwg and smartlogicAnn Kelly
From CEOs to board members to operational managers, regulatory compliance is an ongoing concern. In a rapidly changing marketplace where complex regulations come from multiple regulatory bodies, the consequences of non-compliance can be costly to the enterprise in time, money and damage to their reputation.
JWG, a London think tank, has created RegDelta – a state-of-the-art regulatory change management platform - that allows individual stakeholders to quickly understand the impact of regulations and maintain a single source of truth for their regulatory obligations.
Hear Elliot Burgess, Head of Product and Client Services at JWG and Paul Gunstone, Sales Director at Smartlogic discuss the challenges organizations face identifying and complying with relevant regulations, JWG’s approach to taming the regulatory tiger with semantics and see a demo of the JWG RegDelta platform.
Big Data Week 2016 - Worldpay - Deploying Secure ClustersDavid Walker
A presentation from the Big Data Week conference in 2016 that looks how Worldpay, a major payments provider, deployed a secure Hadoop cluster in order to meet business requirements
Data Works Berlin 2018 - Worldpay - PCI ComplianceDavid Walker
A presentation from the Data Works conference in 2018 that looks how Worldpay, a major payments provider, deployed a secure Hadoop cluster in order to meet business requirements and in the process became on e of the few fully certified PCI compliance clusters in the world
Data Works Summit Munich 2017 - Worldpay - Multi Tenancy ClustersDavid Walker
A presentation from the Data Works Summit conference in 2017 that looks how Worldpay, a major payments provider, deployed a secure Hadoop cluster to support multiple business cases in a multi-tenancy cluster.
Big Data Analytics 2017 - Worldpay - Empowering PaymentsDavid Walker
A presentation from the Big Data Analytics conference in 2017 that looks how Worldpay, a major payments provider, uses data science and big data analytics to influence successful card payments.
A discussion on how insurance companies could use telematics data, social media and open data sources to analyse and better price policies for their customers
Data Driven Insurance Underwriting (Dutch Language Version)David Walker
A discussion on how insurance companies could use telematics data, social media and open data sources to analyse and better price policies for their customers
An introduction to data virtualization in business intelligenceDavid Walker
A brief description of what Data Virtualisation is and how it can be used to support business intelligence applications and development. Originally presented to the ETIS Conference in Riga, Latvia in October 2013
A presentation to the ETIS Business Intelligence & Data Warehousing Working Group in Brussels 22-Mar-13 discussing what Saas & Cloud means and how they will affect BI in Telcos
Business intelligence requirements are changing and business users are moving more and more from historical reporting into predictive analytics in an attempt to get both a better and deeper understanding of their data. Traditionally, building an analytical platform has required an expensive infrastructure and a considerable amount of time for setup and deployment. Here we look at a quick and simple alternative.
Using the right data model in a data martDavid Walker
A presentation describing how to choose the right data model design for your data mart. Discusses the pros and benefits of different data models with different rdbms technologies and tools
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.