Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
Data system integration challenge analysis
Understanding of a range of data system-integration technologies including
Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data warehousing & BI in support of business strategy
The Pivotal Business Data Lake provides a flexible blueprint to meet your business's future information and analytics needs while avoiding the pitfalls of typical EDW implementations. Pivotal’s products will help you overcome challenges like reconciling corporate and local needs, providing real-time access to all types of data, integrating data from multiple sources and in multiple formats, and supporting ad hoc analysis.
Modern Integrated Data Environment - Whitepaper | QuboleVasu S
A whit-paper is about building a modern data platform for data driven organisations with using cloud data warehouse with modern data platform architecture
https://www.qubole.com/resources/white-papers/modern-integrated-data-environment
The Business Data Lake is a new approach to information management, analytics and reporting that better matches the culture of business and better enables organizations to truly leverage the value of their information.
Enterprises are faced by information overload. Big data appears as an opportunity, but has no relevance until enterprises can put it in context of their activities, processes, and organizations, Applying MDM principles to Big Data is therefore an opportunity that enterprises should target.
This presentation covers the following topics :
- what is MDM and Information Management
- what is Big Data and what are the use cases
- why and how Big Data can take advantage of MDM ? why and how MDM can take advantage of Big Data ?
Traditional BI vs. Business Data Lake – A ComparisonCapgemini
Traditional Business Intelligence (BI) systems provide various levels and kinds of analyses on structured data but they are not designed to handle unstructured data.
For these systems Big Data brings big problems because the data that flows in may be either structured or unstructured. That makes them hugely limited when it comes to delivering Big Data benefits.
The way forward is a complete rethink of the way we use BI - in terms of how the data is ingested, stored and analyzed.
More information: http://www.capgemini.com/big-data-analytics/pivotal
The Pivotal Business Data Lake provides a flexible blueprint to meet your business's future information and analytics needs while avoiding the pitfalls of typical EDW implementations. Pivotal’s products will help you overcome challenges like reconciling corporate and local needs, providing real-time access to all types of data, integrating data from multiple sources and in multiple formats, and supporting ad hoc analysis.
Modern Integrated Data Environment - Whitepaper | QuboleVasu S
A whit-paper is about building a modern data platform for data driven organisations with using cloud data warehouse with modern data platform architecture
https://www.qubole.com/resources/white-papers/modern-integrated-data-environment
The Business Data Lake is a new approach to information management, analytics and reporting that better matches the culture of business and better enables organizations to truly leverage the value of their information.
Enterprises are faced by information overload. Big data appears as an opportunity, but has no relevance until enterprises can put it in context of their activities, processes, and organizations, Applying MDM principles to Big Data is therefore an opportunity that enterprises should target.
This presentation covers the following topics :
- what is MDM and Information Management
- what is Big Data and what are the use cases
- why and how Big Data can take advantage of MDM ? why and how MDM can take advantage of Big Data ?
Traditional BI vs. Business Data Lake – A ComparisonCapgemini
Traditional Business Intelligence (BI) systems provide various levels and kinds of analyses on structured data but they are not designed to handle unstructured data.
For these systems Big Data brings big problems because the data that flows in may be either structured or unstructured. That makes them hugely limited when it comes to delivering Big Data benefits.
The way forward is a complete rethink of the way we use BI - in terms of how the data is ingested, stored and analyzed.
More information: http://www.capgemini.com/big-data-analytics/pivotal
The opportunity of the business data lakeCapgemini
The Pivotal Business Data Lake is a new way to deliver information for the enterprise based around four simple principles:
- Store everything
- Encourage local
- Govern only the common
- Treat global as a local view
Principles that match the way business works today and now principles that can be delivered efficiently in technology using the Pivotal Business Data Lake and Capgemini's information governance and delivery methods.
Analyst Webinar: Discover how a logical data fabric helps organizations avoid...Denodo
Watch full webinar here: https://bit.ly/3zVUXWp
In this webinar, we’ll be tackling the question of where our data is and how we can avoid it falling into a black hole.
We’ll examine how data blackholes and silos come to be and the challenges these pose to organisations. We will also look at the impact of data silos as organisations adopt more complex multi-cloud setups. Finally, we will discuss the opportunities a logical data fabric poses to assist organisations to avoid data silos and manage data in a centrally governed and controlled environment.
Join us and Barc’s Jacqueline Bloemen on this webinar to get the answer and further insights on how to better avoid falling into a #datablackhole. Hope to see you connected!
THE FUTURE OF DATA: PROVISIONING ANALYTICS-READY DATA AT SPEEDwebwinkelvakdag
Data lakes & data warehouses, whether on-premises or in the cloud promise to provide a centralized, cost-effective and scalable foundation for modern analytics. However, organisations continue to struggle to deliver accurate, current and analytics-ready data sets in a timely fashion. Traditional ingestion tools weren’t designed to handle hundreds or even thousands of data sources and the lack of lineage forces data consumers to manually aggregate information from sources they trust. In this session, you’ll learn how to future-proof your modern data environment to meet the needs of the business for the long term. We'll examine how to overcome common challenges, the related must-have technology solutions in the data lake/ data warehousing world, using real-world success stories and even a few architecture tips from industry experts.
Business Intelligence (BI): Your Home Care Agency Guide to Reporting & InsightsAlayaCare
This 15-page document will inspire and guide you through WHAT business intelligence (BI) is, and WHY data analytics should be top of mind for your home care agency. Furthermore, this guide will help you answer HOW you know it’s time to consider looking into a BI tool, while providing you with a few tips and tricks to get started.
Rethink Your 2021 Data Management Strategy with Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/2O2r3NP
In the last several decades, BI has evolved from large, monolithic implementations controlled by IT to orchestrated sets of smaller, more agile capabilities that include visual-based data discovery and governance. These new capabilities provide more democratic analytics accessibility that is increasingly being controlled by business users. However, given the rapid advancements in emerging technologies such as cloud and big data systems and the fast changing business requirements, creating a future-proof data management strategy is an incredibly complex task.
Catch this on demand session to understand:
- BI program modernization challenges
- What is data virtualization and why is its adoption growing so quickly?
- How data virtualization works and how it compares to alternative approaches to data integration
- How modern data virtualization can significantly increase agility while reducing costs
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.
Agile Data Warehouse Design for Big Data PresentationVishal Kumar
Synopsis:
[Video link: http://www.youtube.com/watch?v=ZNrTxSU5IQ0 ]
Jim Stagnitto and John DiPietro of consulting firm a2c) will discuss Agile Data Warehouse Design - a step-by-step method for data warehousing / business intelligence (DW/BI) professionals to better collect and translate business intelligence requirements into successful dimensional data warehouse designs.
The method utilizes BEAM✲ (Business Event Analysis and Modeling) - an agile approach to dimensional data modeling that can be used throughout analysis and design to improve productivity and communication between DW designers and BI stakeholders. BEAM✲ builds upon the body of mature "best practice" dimensional DW design techniques, and collects "just enough" non-technical business process information from BI stakeholders to allow the modeler to slot their business needs directly and simply into proven DW design patterns.
BEAM✲ encourages DW/BI designers to move away from the keyboard and their entity relationship modeling tools and begin "white board" modeling interactively with BI stakeholders. With the right guidance, BI stakeholders can and should model their own BI data requirements, so that they can fully understand and govern what they will be able to report on and analyze.
The BEAM✲ method is fully described in
Agile Data Warehouse Design - a text co-written by Lawrence Corr and Jim Stagnitto.
About the speaker:
Jim Stagnitto Director of a2c Data Services Practice
Data Warehouse Architect: specializing in powerful designs that extract the maximum business benefit from Intelligence and Insight investments.
Master Data Management (MDM) and Customer Data Integration (CDI) strategist and architect.
Data Warehousing, Data Quality, and Data Integration thought-leader: co-author with Lawrence Corr of "Agile Data Warehouse Design", guest author of Ralph Kimball’s “Data Warehouse Designer” column, and contributing author to Ralph and Joe Caserta's latest book: “The DW ETL Toolkit”.
John DiPietro Chief Technology Officer at A2C IT Consulting
John DiPietro is the Chief Technology Officer for a2c. Mr. DiPietro is responsible
for setting the vision, strategy, delivery, and methodologies for a2c’s Solution
Practice Offerings for all national accounts. The a2c CTO brings with him an
expansive depth and breadth of specialized skills in his field.
Sponsor Note:
Thanks to:
Microsoft NERD for providing awesome venue for the event.
http://A2C.com IT Consulting for providing the food/drinks.
http://Cognizeus.com for providing book to give away as raffle.
Data Warehouse Design and Best PracticesIvo Andreev
A data warehouse is a database designed for query and analysis rather than for transaction processing. An appropriate design leads to scalable, balanced and flexible architecture that is capable to meet both present and long-term future needs. This session covers a comparison of the main data warehouse architectures together with best practices for the logical and physical design that support staging, load and querying.
Advanced Topics In Business Intelligenceguest1a9ef2
The blurring of the line between decision support systems and operational systems because of real-time warehousing, the use of Enterprise Information Integration (EII), and closed- loop business processes
Estimating the Total Costs of Your Cloud Analytics Platform DATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function Data Management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a worry-free experience with the architecture and its components.
A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $2M to $14M. Get this data point as you take the next steps on your journey.
How to Use a Semantic Layer on Big Data to Drive AI & BI ImpactDATAVERSITY
Learn about using a semantic layer to make data accessible and how to accelerate the business impact of AI and BI at your organization.
This session will offer practical advice on how to drive AI & BI business outcomes with an effective data strategy that leverages a semantic layer.
You will learn how to achieve quantifiable results by modernizing your data and analytics stack with a semantic layer that delivers an order of magnitude better query performance, increased data team productivity, lower query compute costs, and improved Speed-to-Insights.
Attend this session to learn about:
- Gaining business alignment and reducing data prep for your AI and BI teams.
- Making a consistent set of business metrics “analytics-ready” and accessible.
- Accelerating end-to-end query performance while optimizing cloud resources.
- Treating “data as a product” and how to drive business value for all consumers.
Data-Ed Webinar: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
•Data system integration challenge analysis
•Understanding of a range of data system-integration technologies including Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
•Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
•How to utilize data warehousing & BI in support of business strategy
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
The opportunity of the business data lakeCapgemini
The Pivotal Business Data Lake is a new way to deliver information for the enterprise based around four simple principles:
- Store everything
- Encourage local
- Govern only the common
- Treat global as a local view
Principles that match the way business works today and now principles that can be delivered efficiently in technology using the Pivotal Business Data Lake and Capgemini's information governance and delivery methods.
Analyst Webinar: Discover how a logical data fabric helps organizations avoid...Denodo
Watch full webinar here: https://bit.ly/3zVUXWp
In this webinar, we’ll be tackling the question of where our data is and how we can avoid it falling into a black hole.
We’ll examine how data blackholes and silos come to be and the challenges these pose to organisations. We will also look at the impact of data silos as organisations adopt more complex multi-cloud setups. Finally, we will discuss the opportunities a logical data fabric poses to assist organisations to avoid data silos and manage data in a centrally governed and controlled environment.
Join us and Barc’s Jacqueline Bloemen on this webinar to get the answer and further insights on how to better avoid falling into a #datablackhole. Hope to see you connected!
THE FUTURE OF DATA: PROVISIONING ANALYTICS-READY DATA AT SPEEDwebwinkelvakdag
Data lakes & data warehouses, whether on-premises or in the cloud promise to provide a centralized, cost-effective and scalable foundation for modern analytics. However, organisations continue to struggle to deliver accurate, current and analytics-ready data sets in a timely fashion. Traditional ingestion tools weren’t designed to handle hundreds or even thousands of data sources and the lack of lineage forces data consumers to manually aggregate information from sources they trust. In this session, you’ll learn how to future-proof your modern data environment to meet the needs of the business for the long term. We'll examine how to overcome common challenges, the related must-have technology solutions in the data lake/ data warehousing world, using real-world success stories and even a few architecture tips from industry experts.
Business Intelligence (BI): Your Home Care Agency Guide to Reporting & InsightsAlayaCare
This 15-page document will inspire and guide you through WHAT business intelligence (BI) is, and WHY data analytics should be top of mind for your home care agency. Furthermore, this guide will help you answer HOW you know it’s time to consider looking into a BI tool, while providing you with a few tips and tricks to get started.
Rethink Your 2021 Data Management Strategy with Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/2O2r3NP
In the last several decades, BI has evolved from large, monolithic implementations controlled by IT to orchestrated sets of smaller, more agile capabilities that include visual-based data discovery and governance. These new capabilities provide more democratic analytics accessibility that is increasingly being controlled by business users. However, given the rapid advancements in emerging technologies such as cloud and big data systems and the fast changing business requirements, creating a future-proof data management strategy is an incredibly complex task.
Catch this on demand session to understand:
- BI program modernization challenges
- What is data virtualization and why is its adoption growing so quickly?
- How data virtualization works and how it compares to alternative approaches to data integration
- How modern data virtualization can significantly increase agility while reducing costs
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.
Agile Data Warehouse Design for Big Data PresentationVishal Kumar
Synopsis:
[Video link: http://www.youtube.com/watch?v=ZNrTxSU5IQ0 ]
Jim Stagnitto and John DiPietro of consulting firm a2c) will discuss Agile Data Warehouse Design - a step-by-step method for data warehousing / business intelligence (DW/BI) professionals to better collect and translate business intelligence requirements into successful dimensional data warehouse designs.
The method utilizes BEAM✲ (Business Event Analysis and Modeling) - an agile approach to dimensional data modeling that can be used throughout analysis and design to improve productivity and communication between DW designers and BI stakeholders. BEAM✲ builds upon the body of mature "best practice" dimensional DW design techniques, and collects "just enough" non-technical business process information from BI stakeholders to allow the modeler to slot their business needs directly and simply into proven DW design patterns.
BEAM✲ encourages DW/BI designers to move away from the keyboard and their entity relationship modeling tools and begin "white board" modeling interactively with BI stakeholders. With the right guidance, BI stakeholders can and should model their own BI data requirements, so that they can fully understand and govern what they will be able to report on and analyze.
The BEAM✲ method is fully described in
Agile Data Warehouse Design - a text co-written by Lawrence Corr and Jim Stagnitto.
About the speaker:
Jim Stagnitto Director of a2c Data Services Practice
Data Warehouse Architect: specializing in powerful designs that extract the maximum business benefit from Intelligence and Insight investments.
Master Data Management (MDM) and Customer Data Integration (CDI) strategist and architect.
Data Warehousing, Data Quality, and Data Integration thought-leader: co-author with Lawrence Corr of "Agile Data Warehouse Design", guest author of Ralph Kimball’s “Data Warehouse Designer” column, and contributing author to Ralph and Joe Caserta's latest book: “The DW ETL Toolkit”.
John DiPietro Chief Technology Officer at A2C IT Consulting
John DiPietro is the Chief Technology Officer for a2c. Mr. DiPietro is responsible
for setting the vision, strategy, delivery, and methodologies for a2c’s Solution
Practice Offerings for all national accounts. The a2c CTO brings with him an
expansive depth and breadth of specialized skills in his field.
Sponsor Note:
Thanks to:
Microsoft NERD for providing awesome venue for the event.
http://A2C.com IT Consulting for providing the food/drinks.
http://Cognizeus.com for providing book to give away as raffle.
Data Warehouse Design and Best PracticesIvo Andreev
A data warehouse is a database designed for query and analysis rather than for transaction processing. An appropriate design leads to scalable, balanced and flexible architecture that is capable to meet both present and long-term future needs. This session covers a comparison of the main data warehouse architectures together with best practices for the logical and physical design that support staging, load and querying.
Advanced Topics In Business Intelligenceguest1a9ef2
The blurring of the line between decision support systems and operational systems because of real-time warehousing, the use of Enterprise Information Integration (EII), and closed- loop business processes
Estimating the Total Costs of Your Cloud Analytics Platform DATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function Data Management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a worry-free experience with the architecture and its components.
A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $2M to $14M. Get this data point as you take the next steps on your journey.
How to Use a Semantic Layer on Big Data to Drive AI & BI ImpactDATAVERSITY
Learn about using a semantic layer to make data accessible and how to accelerate the business impact of AI and BI at your organization.
This session will offer practical advice on how to drive AI & BI business outcomes with an effective data strategy that leverages a semantic layer.
You will learn how to achieve quantifiable results by modernizing your data and analytics stack with a semantic layer that delivers an order of magnitude better query performance, increased data team productivity, lower query compute costs, and improved Speed-to-Insights.
Attend this session to learn about:
- Gaining business alignment and reducing data prep for your AI and BI teams.
- Making a consistent set of business metrics “analytics-ready” and accessible.
- Accelerating end-to-end query performance while optimizing cloud resources.
- Treating “data as a product” and how to drive business value for all consumers.
Data-Ed Webinar: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
•Data system integration challenge analysis
•Understanding of a range of data system-integration technologies including Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
•Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
•How to utilize data warehousing & BI in support of business strategy
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
Expert data analytics prove to be highly transformative when applied in context to corporate business strategies.
This webinar covers various approaches and strategies that will give you a detailed insight into planning and executing your Data Analytics projects.
Data Systems Integration & Business Value PT. 3: Warehousing Data Blueprint
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered.
Data-Ed: Data Systems Integration & Business Value Pt. 3: WarehousingDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered.
Data-Ed Webinar: Data Governance StrategiesDATAVERSITY
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Takeaways:
Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
Guiding principles & lessons learned
Understanding foundational data governance concepts based on the DAMA DMBOK
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Check out more webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Lecture 1.13 & 1.14 &1.15_Business Profiles in Big Data.pptxRATISHKUMAR32
The presentation contain the business profiles in big data analytics. through this ppt user can learn about the different case studies such as facebook and walmart. This ppt contain the information and seven characteristics that are required to learn the basics of big data.
The content of the document, "Implementing Data Mesh: Six Ways That Can Improve the Odds of Your Success," is a whitepaper authored by Ranganath Ramakrishna from LTIMindtree. The whitepaper introduces the concept of Data Mesh, a socio-technical paradigm that aims to help organizations fully leverage the value of their analytical data.
The benefits of Hadoop for analytics make it a popular option for many companies looking to expand their analytics suite. However, adding Hadoop as an analytics platform to an existing environment based on more traditional data structures and methods poses several key challenges. Review these slides to understand key challenges and strategies to expanding the analytics suite to use Hadoop, such as: architectural integration with existing platforms, skills and organizational readiness, and the importance of a vision and a clear path forward.
Data-Ed Online Webinar: Data Governance StrategiesDATAVERSITY
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Takeaways:
Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
Guiding principles & lessons learned
Understanding foundational data governance concepts based on the DAMA DMBOK
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Find more of our Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
The Importance of MDM - Eternal Management of the Data MindDATAVERSITY
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
Data-Ed Webinar: Design & Manage Data Structures DATAVERSITY
Data structures enable you to store and organize data so that it can be used efficiently. But how do you know to apply the correct one? There is a difference between structuring master data, reference data and analytics data. This webinar will discuss the various data structures available and when to use each one. We will show how data structures should support your organizational data strategy and how each method can contribute to business value.
Takeaways:
Application of correct data structures to fit business needs
How different structures create different business value
Data-Ed Webinar: Monetizing Data Management - Show Me the MoneyDATAVERSITY
Practicality and profitability may share a page in the dictionary, but incorporating both into a data management plan can prove challenging. Many data professionals struggle to demonstrate tangible returns on data management investments, especially in industries such as healthcare where financial results aren’t necessarily an organization’s primary concern. The key to “monetizing” data management, therefore, is thinking about data in a different way: as an information solution rather than simply an IT one, using data to drive decision-making towards increased profits and potentially alternative returns on investment or value outcomes as well. Taking a broader view of data assets facilitates easier sharing of information across organizational silos, and allows for a wider understanding of the investment’s requirements and benefits.
In this webinar—designed to appeal to both business and IT attendees—your presenter will:
Describe multiple types of value produced through data-centric development and management practices
Expand on and beyond metrics meant for increasing revenues or decreasing costs—i.e. investments that directly impact an organization’s financial position
Detail how alternative statistics and valuations can be used to justify data management and quality initiatives
Similar to Data-Ed Online Presents: Data Warehouse Strategies (20)
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...DATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion.
In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership.
A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.
Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.
What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it.
In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen?
This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Uncover how your business can save money and find new revenue streams.
Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how?
By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability.
In this webinar, you'll learn:
- How your company can leverage data and AI to reduce spending and costs
- Ways you can monetize data and AI and uncover new growth strategies
- How different companies have implemented these strategies to achieve cost optimization benefits
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
Data Catalogs Are the Answer – What Is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
In this webinar, Bob will focus on:
-Selecting the appropriate metadata to govern
-The business and technical value of a data catalog
-Building the catalog into people’s routines
-Positioning the data catalog for success
-Questions the data catalog can answer
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends.
Specific learning objectives include:
- Understanding what types of challenges require data modeling to be part of the solution
- How automation requires standardization on derivable via data modeling techniques
- Why only a working partnership between data and the business can produce useful outcomes
Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like.
Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.
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.
Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there.
Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups:
- why it matters,
- what it means,
- what gets in the way,
- who needs it (and how much they need),
- what companies believe it will accomplish.
In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind.
In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving.
Topics that you will hear addressed in this webinar:
- Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends
- Best practices for key management and apply data governance to your day-to-day
- The next wave of Confidential Computing and how to get started, including a demo
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following:
- Your organization’s data
- The way your people use data
- The way your people use data to achieve your organizational strategy
This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why data strategy is necessary for effective data governance
- An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
Who Should Own Data Governance – IT or Business?DATAVERSITY
The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question.
Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more.
In this session Bob will share:
- What is meant by “the business” when it comes to owning Data Governance
- Why some people say that Data Governance in IT is destined to fail
- Examples of IT positioned Data Governance success
- Considerations for answering the question in your organization
- The final answer to the question of who should own Data Governance
It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand:
- Their current Data Management practices
- Strengths that should be leveraged
- Remediation opportunities
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
2. 2
Premise
Two types of listeners …
1. Interested in how to
approach the subject of
warehousing data
– Need to integrate disparate
Copyright 2014 by Data Blueprint
data
– Need more holistic view of
business operations
– Management just discovered
data warehouses and wants
you to "build one"
2. Have complex and/or
messy data warehouse
practices
– Want to improve them
3. Data Warehousing Strategies
3
Copyright 2014 by Data Blueprint
1. Warehousing data in the context of data
management
2. Motivation for integration technologies
(reporting->BI->Analytics)
3. Warehouse integration technologies
4. Three warehousing architecture foci
5. The use of meta models
6. Guiding principles & best practices
5. Data Management Practices Hierarchy
You can accomplish
Advanced Data Practices
without becoming proficient
in the Foundational Data
Management Practices
however this will:
• Take longer
• Cost more
• Deliver less
• Present
greater
risk
(with thanks to Tom DeMarco)
Technologies Capabilities
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Management Practices
5
Copyright 2014 by Data Blueprint
Data Governance Data Quality
Data Platform/Architecture
Data Management Strategy
Data Operations
6. ReUusesses
What is data management?
6
Copyright 2014 by Data Blueprint
Sources
Data Governance
Data
Engineering
Data
Delivery
Data
Storage
Specialized Team Skills
Understanding the current
and future data needs of an
enterprise and making that
data effective and efficient in
supporting
business activities
Aiken, P, Allen, M. D., Parker, B., Mattia, A.,
"Measuring Data Management's Maturity:
A Community's Self-Assessment"
IEEE Computer (research feature April 2007)
Data management practices connect
data sources and uses in an
organized and efficient manner
• Storage
• Engineering
• Delivery
• Governance
When executed,
engineering, storage, and
delivery implement governance
Note: does not well-depict data reuse
7. Manage data coherently
Maintain fit-for-purpose data,
efficiently and effectively
DMM℠ Structure of
5 Integrated
DM Practice Areas
7
Manage data assets professionally
Copyright 2014 by Data Blueprint
Data architecture
implementation
Data engineering
implementation
Organizational support
8. Data Management Body of Knowledge
8
Copyright 2014 by Data Blueprint
Data
Management
Functions
9. DAMA DM BoK & CDMP
9
Copyright 2014 by Data Blueprint
• Data Management Body of
Knowledge (DMBOK)
– Published by DAMA International, the
professional association for
Data Managers (40 chapters worldwide)
– Organized around primary data management
functions focused around data delivery to the
organization and several environmental
elements
• Certified Data Management
Professional (CDMP)
– Series of 3 exams by DAMA International and
ICCP
– Membership in a distinct group of
fellow professionals
– Recognition for specialized knowledge in a
choice of 17 specialty areas
– For more information, please visit:
• www.dama.org, www.iccp.org
10. Data Warehousing & Business Intelligence Management
10
Copyright 2014 by Data Blueprint
11. Warehousing data in the context of data management
11
Copyright 2014 by Data Blueprint
Assumes you have
• An overarching data strategy
• A strategy for becoming
familiar with "big data
technologies"
• Made a decision to not make
available (integrating or
storing) needed data
• Decided to increase (or
decrease) the complexity of
existing DM practices
• Decided to learn more about
this DM BoK slice
Sources ReUusesses
Data Governance
Data
Engineering
Data
Delivery
Data
Storage
Specialized Team Skills
12. Data Warehousing Strategies
12
Copyright 2014 by Data Blueprint
1. Warehousing data in the context of data
management
2. Motivation for integration technologies
(reporting->BI->Analytics)
3. Warehouse integration technologies
4. Three warehousing architecture foci
5. The use of meta models
6. Guiding principles & best practices
13. Typical System Evolution
Payroll Application
Payroll Data (3rd GL)
(database)
Finance
Data
(indexed)
R & D
Data
(raw) Mfg. Data
R& D Applications
(researcher supported, no documentation)
(home grown
database)
Finance Application
(3rd GL, batch
system, no source)
Mfg. Applications
(contractor supported)
Marketing Application
(4rd GL, query facilities,
no reporting, very large)
Marketing Data
(external database)
Personnel App.
(20 years old,
un-normalized data)
Personnel Data
(database)
13
Copyright 2014 by Data Blueprint
Multiple Sources of
(for example)
Customer Data
14. Payroll Data
(database)
R & D
Data
(raw)
Finance
Data
(indexed)
Mfg. Data
(home grown
database)
Marketing Data
(external database)
Personnel Data
(database)
... Then Integrate
14
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Organizational
Data
15. ... Then Re-architect
Payroll Data
(database)
R & D
Data
(raw)
Finance
Data
(indexed)
Mfg. Data
(home grown
database)
Marketing Data
(external database)
Personnel Data
(database)
15
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Organizational
Data
16. An organization's integration needs ...
... map between and across software packages
16
Data Architecture
Copyright 2014 by Data Blueprint
Software
Package 1
Software
Package 2
Software
Package 3
Software
Package 4
Software
Package 5
Software
Package 6
18. Hemophilia Management Analytics
Descriptive
Ask: What happened? What is happening?
Find: Structured data
Show: Profiles, Bar/pie charts, Narrative
Predictive
Ask: What will happen? Why will it happen?
Find: Structured/unstructured data
Show: Risk Profiles, Pros/Cons, Care Recs
Prescriptive
Ask: What should I do? Why should I do it?
Find: Unstructured/structured data
Show: Strategic Goals, Support Recs
18
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19. Target Isn't Just Predicting Pregnancies
http://rmportal.performedia.com/node/1373 and http://www.predictiveanalyticsworld.com/patimes/target-really-predict-teens-pregnancy-inside-story/ http://rmportal.performedia.com/rm/paw10/gallery_01#1373
19
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20. Basics
• Summaries to
transaction-level
detail
20
• Users can
"drill"
anywhere
• Entire collection
"cube" is
accessible
Copyright 2014 by Data Blueprint
21. Sample questions …
21
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Cancer patient
revenue across
all facilities
Revenue for diseases
this year versus last
year in the NE region
Total costs and revenue at
top 10 facilities
• Emphasis on the
"cube"
– N dimensions
• Permits different
users to "slice and
dice" subsets of data
• Viewing from different
perspectives
23. Portfolio Analysis
23
• Bank accounts are of
varying value and risk
• Cube by
– Social status
– Geographical location
– Net value, etc.
• Strategy or goal:
balance return on the loan with risk of default
• How to evaluate the portfolio as a whole?
– Least risk loan may be to the very wealthy, but there are a very
Copyright 2014 by Data Blueprint
limited number
– Many poor customers, but greater risk
• Solution may combine types of analyses
– When to lend, interest rate charged
24. 15 years ago, CarMax started as a way to make the car buying experience simple, fair, and fun. Today CarMax is a FORTUNE 500 retailer and one of FORTUNE’s “100 Best Companies to
Work For.” And we are hiring talented individuals who are interested in:
--solving original, wide-ranging, and open-ended business problems
--not only discovering new insights, but successfully implementing them
--making a significant mark on a growing company
--developing the fundamental skills for a rewarding business career
If that sounds like you, the Strategy Analyst position is the unique opportunity you’ve been looking for. The strategy team at CarMax currently consists of over 40 analysts, many of whom
are recent college graduates from top schools with a variety of academic backgrounds (computer science, economics, English, engineering, journalism, math, political science). These
analysts lead advances and decisions in several key business areas:-Inventory and pricing—what is the optimal selection of inventory, how do we acquire it, what should we pay for it, what
should we price it for?
-Expansion planning—which markets should we enter and how do we store those markets? Will each $10-30 million store investment generate a sufficient economic return?
-Credit strategy—how can our bank (CarMax Auto Finance) approve more customers for loans and convert more approvals to sales?
-Marketing and consumer insight—how do we reach our customers, increase traffic to our stores, and best use the internet to drive sales and build our brand
-Industry and competitive research—what middle- and long-term risks are we exposed to, and how best do we prepare to respond?
-Production—how do we increase vehicle reconditioning quality while reducing cost and production time?
-Sales process and workforce—what is the best way to serve customers in our stores, and how do we manage, motivate and compensate our sales team?
Even early in your career at CarMax, you will have the responsibility to own an area of the business and will be expected to improve it. For example, one undergraduate recruit used data
analysis to reformulate our retail pricing strategy, pitched and sold his idea to the senior executive team, and implemented a new system nationwide in his first 6 months with the company.
That is the kind of impact you can make at CarMax. And as you do this, you will work closely with the senior executives and analytical managers to develop the fundamental and advanced
skills that underpin a successful career in business. In fact, most of our managers in the strategy group started at CarMax as analysts, and our VP of Strategy and Analysis started his
career here through our undergraduate recruiting program. While an MBA is not required to advance or contribute at CarMax, analysts who have chosen to pursue a business degree have
enjoyed superior acceptance rates at their first choice schools, including Harvard, Chicago, UVa, Columbia, and Duke.
Your opportunities to develop, contribute, and lead as an analyst at CarMax are as great as the company’s opportunity to grow. While CarMax is already the largest used car retailer in the
country (with over $8 billion in sales and over 90 superstores across the country), we have only 2% of the 1 to 6-year-old used car market, which, at $280 billion annually, is bigger than the
home improvement or consumer electronics industries. CarMax is already growing at 15% a year, and over the next 10 years plans to have 250-300 stores and achieve $25+ billion in
annual sales. As an analyst, you can be an integral part of that growth, all while enjoying a casual and friendly environment, a diverse group of talented associates, a healthy work-life
balance, and excellent compensation and benefits.
An ideal candidate will have
--Demonstrated top caliber analytic and problem solving skills --History of achievement demonstrated by top 15% GPA, with a quantitative major(s), and/or other recognition such as
scholarships, awards, honor societies
-- Passion for business and desire to develop into a strong business leader
We encourage you to apply. For more information, please visit us at the career fair, on our website (www.carmax.com/collegerecruiting), or email us at college_recruiting@carmax.com
- datablueprint.com
CarMax Example Job Posting
--solving original, wide-ranging, and open-ended business problems
--not only discovering new insights, but successfully implementing them
--making a significant mark on a growing company
--developing the fundamental skills for a rewarding business career
24
Copyright 2014 by Data Blueprint
24
own an area of the business and will be expected to improve it
25. Polling Question #1
25
Copyright 2014 by Data Blueprint
• Do you have/have
you started data
warehousing, marts
and/or other
warehousing forms
of integration?
a. Last year (2014)
b. This year (2015)
c. Next Year (2016)
d. Nope
26. Data Warehousing Strategies
26
Copyright 2014 by Data Blueprint
1. Warehousing data in the context of data
management
2. Motivation for integration technologies
(reporting->BI->Analytics)
3. Warehouse integration technologies
4. Three warehousing architecture foci
5. The use of meta models
6. Guiding principles & best practices
28. Warehousing Definitions
28
• Inmon:
– "A subject oriented, integrated, time variant, and non-volatile
collection of summary and detailed historical data used to support
the strategic decision-making processes of the organization."
• Kimball:
– "A copy of transaction data specifically structured for query and
Copyright 2014 by Data Blueprint
analysis."
• Key concepts focus on:
– Subjects
– Transactions
– Non-volatility
– Restructuring
32. MetaMatrix Integration Example
• EII Enterprise Information
Integration
– between ETL and EAI - delivers
tailored views of information to
users at the time that it is
required
32
Copyright 2014 by Data Blueprint
33. Linked Data
33
Linked Data is about using the Web to connect related data
that wasn't previously linked, or using the Web to lower the
barriers to linking data currently linked using other methods.
More specifically, Wikipedia defines Linked Data as "a term
used to describe a recommended best practice for exposing,
sharing, and connecting pieces of data, information, and
knowledge on the Semantic Web using URIs and RDF."
Copyright 2014 by Data Blueprint
linkeddata.org
34. Health Care Provider Data Warehouse
34
"A roomful of MBAs
can accomplish this
analysis faster!"
Copyright 2014 by Data Blueprint
• 1.8 million members
• 1.4 million providers
• 800,000 providers no key
• 29% prov_ssn ≠ 9 digits
• 2.2% prov_number = 9 digits (required)
• 1 User
• $30 million
37. Reframing the question
37
Copyright 2014 by Data Blueprint
• From: How shall we build this data
warehouse?
– (Worse) … What should go into this warehouse?
• To: How can warehousing capabilities
solve this specific business challenge?
– (Better still) … How can warehousing capabilities
solve this class of business challenges?
• Other examples
– Are you ready for a data warehouse?
✓ Foundational practices
– Will you get it right the first time?
✓ Is the business environment constantly evolving?
✓ Do you have an agreed upon enterprise-wide vocabulary?
– Is your data warehouse intended to be the enterprise
audit-able system of record?
✓ Extract, transform and load requirements
✓ Data transformation requirements
– How fast do you need results?
✓ Performance of inserts vs reads
✓ Project deliverables
38. Data Warehousing Strategies
38
Copyright 2014 by Data Blueprint
1. Warehousing data in the context of data
management
2. Motivation for integration technologies
(reporting->BI->Analytics)
3. Warehouse integration technologies
4. Three warehousing architecture foci
5. The use of meta models
6. Guiding principles & best practices
39. Copyright 2013 by Data Blueprint
Inmon Implementation/3NF
39
OPERATIONAL SYSTEM
OPERATIONAL SYSTEM
FLAT FILES
METADATA
SUMMARY
DATA RAW DATA
PURCHASING
SALES
INVENTORY
ANALYSIS
REPORTING
MINING
40. Third Normal Form
40
• Each attribute in the relationship is a fact about a key
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– Highly normalized structure
• Use Cases
– Transactional Systems
– Operational Data Stores
41. Third Normal Form: Pros and Cons
41
Copyright 2014 by Data Blueprint
Neo4j.com
• Pros
– Easily understood by business and end users
– Reduced data redundancy
– Enforced referential integrity
– Indexed attributes/flexible querying
• Cons
– Joins can be expensive
– Does not scale
43. Star Schema
• Comprised of “fact tables” that contain quantitative data, and any
number of adjoining “dimension” tables
• Optimized for business reporting
• Use Cases
– OLAP (Online Analytic Processing)
– BI
43
Copyright 2014 by Data Blueprint
Wikipedia
44. Star Schema Pros and Cons
44
Copyright 2014 by Data Blueprint
• Pros
– Simple Design
– Fast Queries
– Most major DBMS are optimized for Star Schema Designs
• Cons
– Questions must be
built into the design
– Data marts are
often centralized
on one fact table
46. Data Vault
46
• Designed to facilitate long-term historical storage, focusing on
ease of implementation
• Retains data lineage information (source/date)
• “All the data, all the time” - hybrid approach of Inmon and Kimball.
• Comprised of Hubs (which contain a list of business keys that do
not change often), Links (Associations/transactions between
hubs), and Satellites (descriptive attributes associated with hubs
and links)
• Use Cases
– Data Warehousing
– Complete Audit-ability
Copyright 2014 by Data Blueprint
Bukhantsov.org
47. Data Vault Pros and Cons
47
Copyright 2014 by Data Blueprint
• Pros
– Simple integration
– Houses immense amounts of
data with excellent
performance
– Full data lineage captured
• Cons
– Complication is pushed to the
“back end”
– Can be difficult to setup for
many data workers
– No widespread support for ETL
tools yet
49. Polling Question #2
49
Copyright 2014 by Data Blueprint
• Do you have?
a. A single enterprise data warehouse
b. Coordinated data marts
c. Both
d. Uncoordinated efforts
e. None
50. Data Warehousing Strategies
50
Copyright 2014 by Data Blueprint
1. Warehousing data in the context of data
management
2. Motivation for integration technologies
(reporting->BI->Analytics)
3. Warehouse integration technologies
4. Three warehousing architecture foci
5. The use of meta models
6. Guiding principles & best practices
51. Meta Data Models
Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission
51
Copyright 2014 by Data Blueprint
52. Metadata Data Model
SCREEN
ELEMENT
screen element id #
data item id #
screen element descr.
INTERFACE
ELEMENT
interface element id #
data item id #
interface element descr.
INPUT
ELEMENT
input element id #
data item id #
input element descr.
OUTPUT
ELEMENT
output element id #
data item id #
output element descr.
MODEL
VIEW
model view element id #
data item id #
model view element des.
DEPENDENCY
dependency elem id #
data item id #
process id #
dependency description
CODE
code id #
data item id #
stored data item #
code location
INFORMATION
information id #
data item id #
information descr.
information request
PROCESS
process id #
data item id #
process description
PRINTOUT
ELEMENT
printout element id #
data item id #
printout element descr.
LOCATION
location id #
information id #
printout element id #
process id #
stored data items id #
user type id #
location description
USER TYPE
user type id #
data item id #
information id #
user type description
STORED DATA ITEM
stored data item id #
data item id #
location id #
stored data description
DATA ITEM
data item id #
data item description
52
Copyright 2014 by Data Blueprint
53. Warehouse
Process
Warehouse
Opera.on
Transforma.on
XML
Record-‐
Oriented
Mul.
Dimensional
Rela.onal
Business
Informa.on
So@ware
Deployment
ObjectModel
(Core, Behavioral, Rela.onships, Instance)
Warehouse
Management
Analysis
Resources
Object-‐
Oriented
(ObjectModel)
Foundation
OLAP
Data
Mining
Informa.on
Visualiza.on
Business
Nomenclature
Data
Types
Expressions
Keys
Index
Type
Mapping
Overview of CWM Metamodel
http://www.omg.org/technology/documents/modeling_spec_catalog.htm
53
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54. Marco & Jennings's Complete Meta Data Model
54
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Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission
55. Data Warehousing Strategies
55
Copyright 2014 by Data Blueprint
1. Warehousing data in the context of data
management
2. Motivation for integration technologies
(reporting->BI->Analytics)
3. Warehouse integration technologies
4. Three warehousing architecture foci
5. The use of meta models
6. Guiding principles & best practices
57. Data Reengineering Leverage
57
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Data Management Practices
"Warehoused" Data
Duplicated but ETLed Data
(quality & transformations applied)
Learning/
Feedback
Marts
Analytics Practices
58. 58
Copyright 2014 by Data Blueprint
Data Warehousing Strategies
1. Warehousing data in the context of data
management
2. Motivation for integration technologies
(reporting->BI->Analytics)
3. Warehouse integration technologies
4. Three warehousing architecture foci
5. The use of meta models
6. Guiding principles & best practices
59. Data Warehousing & Business Intelligence Management
59
Copyright 2014 by Data Blueprint
60. Questions?
60
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It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter and Steven now.
61. Upcoming Events
January Webinar:
Developing a Data-centric Strategy & Roadmap
January 13, 2015 @ 2:00 PM – 3:30 PM ET
(11:00 AM-12:30 PM PT)
February Webinar:
Unlocking Business Value through Reference and Master Data Management
February 10, 2015 @ 2:00 PM – 3:30 PM ET
(11:00 AM-12:30 PM PT)
Sign up here:
• www.datablueprint.com/webinar-schedule
• www.Dataversity.net
Brought to you by:
61
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67. 6 Best Practices for Data Warehousing
1.Do some initial architecture
envisioning.
2.Model the details just in time (JIT).
3.Prove the architecture early.
4.Focus on usage.
5.Organize your work by requirements.
6.Active stakeholder participation.
67
http://www.agiledata.org/essays/dataWarehousingBestPractices.html
Copyright 2014 by Data Blueprint
69. http://www.cio.com/article/150450/Five_Key_Business_Intelligence_Trends_You_Need_to_Know?page=2&taxonomyId=3002
5 Key Business Intelligence Trends
69
1. There's so much data, but too little
insight. More data translates to a
greater need to manage it and make
it actionable.
2.Market consolidation means fewer
choices for business intelligence users.
3. Business Intelligence expands from the Board Room to the
front lines. Increasingly, business intelligence tools will be
available at all levels of the corporation
4. The convergence of structured and unstructured data Will
create better business intelligence.
5. Applications will provide new views of business intelligence
data. The next generation of business intelligence
applications is moving beyond the pie charts and bar charts
into more visual depictions of data and trends.
Copyright 2014 by Data Blueprint