Data Center Infrastructure Management Powerpoint Presentation SlidesSlideTeam
Introducing Data Center Infrastructure Management PowerPoint Presentation Slides. You can present key drivers for sustainable infrastructure by using our readily available PPT slide deck. The PowerPoint slide deck helps to illustrate the market size, key funding areas of infrastructure, key technology trends in infrastructure in a presentable manner. Further, the slideshow also showcases the asset management process with lifecycle and framework. Utilize our visually-attention-grabbing asset infrastructure PowerPoint templates to mention inventory assets for manufacturing companies. Showcase the deterioration modeling and the types of deterioration modeling such as asset and risk assessment deterioration modeling with the help of asset infrastructure PPT visual. With this infrastructure management PPT slide deck, explain each deterioration modeling in detail using this PPT slideshow. It is possible to depict asset management decision journey, and performance and cost functions by downloading this PPT presentation. https://bit.ly/3c8cORj
In our smartphone-dominated world, developers need to make HMI screens and applications that will look great on small, medium, and large devices. Are you familiar with the mobile-responsive layout strategies that make this possible?
Conceptual vs. Logical vs. Physical Data ModelingDATAVERSITY
A model is developed for a purpose. Understanding the strengths of each of the three Data Modeling types will prepare you with a more robust analyst toolkit. The program will describe modeling characteristics shared by each modeling type. Using the context of a reverse engineering exercise, delegates will be able to trace model components as they are used in a common data reengineering exercise that is also tied to a Data Governance exercise.
Learning objectives:
-Understanding the role played by models
-Differentiate appropriate use among conceptual, logical, and physical data models
- Understand the rigor of the round-trip data reengineering analyses
- Apply appropriate use of various Data Modeling types
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Data Center Infrastructure Management Powerpoint Presentation SlidesSlideTeam
Introducing Data Center Infrastructure Management PowerPoint Presentation Slides. You can present key drivers for sustainable infrastructure by using our readily available PPT slide deck. The PowerPoint slide deck helps to illustrate the market size, key funding areas of infrastructure, key technology trends in infrastructure in a presentable manner. Further, the slideshow also showcases the asset management process with lifecycle and framework. Utilize our visually-attention-grabbing asset infrastructure PowerPoint templates to mention inventory assets for manufacturing companies. Showcase the deterioration modeling and the types of deterioration modeling such as asset and risk assessment deterioration modeling with the help of asset infrastructure PPT visual. With this infrastructure management PPT slide deck, explain each deterioration modeling in detail using this PPT slideshow. It is possible to depict asset management decision journey, and performance and cost functions by downloading this PPT presentation. https://bit.ly/3c8cORj
In our smartphone-dominated world, developers need to make HMI screens and applications that will look great on small, medium, and large devices. Are you familiar with the mobile-responsive layout strategies that make this possible?
Conceptual vs. Logical vs. Physical Data ModelingDATAVERSITY
A model is developed for a purpose. Understanding the strengths of each of the three Data Modeling types will prepare you with a more robust analyst toolkit. The program will describe modeling characteristics shared by each modeling type. Using the context of a reverse engineering exercise, delegates will be able to trace model components as they are used in a common data reengineering exercise that is also tied to a Data Governance exercise.
Learning objectives:
-Understanding the role played by models
-Differentiate appropriate use among conceptual, logical, and physical data models
- Understand the rigor of the round-trip data reengineering analyses
- Apply appropriate use of various Data Modeling types
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
A simple guide to learn what EA is, why it’s important and how you can be using it to help your enterprise.
For more information: info@boc-group.com
Try ADOIT for EA:
https://www.boc-group.com/adoit/#test-it
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...Alan McSweeney
Your data has value to your organisation and to relevant data sharing partners. It has been expensively obtained. It represents a valuable asset on which a return must be generated. To achieve the value inherent in the data you need to be able to make it appropriately available to others, both within and outside the organisation.
Organisations are frequently data rich and information poor, lacking the skills, experience and resources to convert raw data into value.
These notes outline technology approaches to achieving compliance with data privacy regulations and legislation while providing access to data.
There are different routes to making data accessible and shareable within and outside the organisation without compromising compliance with data protection legislation and regulations and removing the risk associated with allowing access to personal data:
• Differential Privacy – source data is summarised and individual personal references are removed. The one-to-one correspondence between original and transformed data has been removed
• Anonymisation – identifying data is destroyed and cannot be recovered so individual cannot be identified. There is still a one-to-one correspondence between original and transformed data
• Pseudonymisation – identifying data is encrypted and recovery data/token is stored securely elsewhere. There is still a one-to-one correspondence between original and transformed data
These technologies and approaches are not mutually exclusive – each is appropriate to differing data sharing and data access use cases
The data privacy regulatory and legislative landscape is complex and getting even more complex so an approach to data access and sharing that embeds compliance as a matter of course is required.
Appropriate technology appropriately implemented and operated is a means of managing and reducing risks of re-identification by making the time, skills, resources and money necessary to achieve this unrealistic.
Technology is part of a risk management approach to data privacy. There is wider operational data sharing and data privacy framework that includes technology aspects, among other key areas. Using these technologies will embed such compliance by design into your data sharing and access facilities. This will allow you to realise value from your data successfully.
In the past few years, the term "data lake" has leaked into our lexicon. But what exactly IS a data lake? Some IT managers confuse data lakes with data warehouses. Some people think data lakes replace data warehouses. Both of these conclusions are false. Their is room in your data architecture for both data lakes and data warehouses. They both have different use cases and those use cases can be complementary.
Todd Reichmuth, Solutions Engineer with Snowflake Computing, has spent the past 18 years in the world of Data Warehousing and Big Data. He spent that time at Netezza and then later at IBM Data. Earlier in 2018 making the jump to the cloud at Snowflake Computing.
Mike Myer, Sales Director with Snowflake Computing, has spent the past 6 years in the world of Security and looking to drive awareness to better Data Warehousing and Big Data solutions available! Was previously at local tech companies FireMon and Lockpath and decided to join Snowflake due to the disruptive technology that's truly helping folks in the Big Data world on a day to day basis.
Data Mesh in Practice - How Europe's Leading Online Platform for Fashion Goes...Dr. Arif Wider
A talk presented by Max Schultze from Zalando and Arif Wider from ThoughtWorks at NDC Oslo 2020.
Abstract:
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
At Zalando - europe’s biggest online fashion retailer - we realised that accessibility and availability at scale can only be guaranteed when moving more responsibilities to those who pick up the data and have the respective domain knowledge - the data owners - while keeping only data governance and metadata information central. Such a decentralized and domain focused approach has recently been coined a Data Mesh.
The Data Mesh paradigm promotes the concept of Data Products which go beyond sharing of files and towards guarantees of quality and acknowledgement of data ownership.
This talk will take you on a journey of how we went from a centralized Data Lake to embrace a distributed Data Mesh architecture and will outline the ongoing efforts to make creation of data products as simple as applying a template.
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...Alan McSweeney
These notes describe a generalised data integration architecture framework and set of capabilities.
With many organisations, data integration tends to have evolved over time with many solution-specific tactical approaches implemented. The consequence of this is that there is frequently a mixed, inconsistent data integration topography. Data integrations are often poorly understood, undocumented and difficult to support, maintain and enhance.
Data interoperability and solution interoperability are closely related – you cannot have effective solution interoperability without data interoperability.
Data integration has multiple meanings and multiple ways of being used such as:
- Integration in terms of handling data transfers, exchanges, requests for information using a variety of information movement technologies
- Integration in terms of migrating data from a source to a target system and/or loading data into a target system
- Integration in terms of aggregating data from multiple sources and creating one source, with possibly date and time dimensions added to the integrated data, for reporting and analytics
- Integration in terms of synchronising two data sources or regularly extracting data from one data sources to update a target
- Integration in terms of service orientation and API management to provide access to raw data or the results of processing
There are two aspects to data integration:
1. Operational Integration – allow data to move from one operational system and its data store to another
2. Analytic Integration – move data from operational systems and their data stores into a common structure for analysis
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
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
As part of this session, I will be giving an introduction to Data Engineering and Big Data. It covers up to date trends.
* Introduction to Data Engineering
* Role of Big Data in Data Engineering
* Key Skills related to Data Engineering
* Role of Big Data in Data Engineering
* Overview of Data Engineering Certifications
* Free Content and ITVersity Paid Resources
Don't worry if you miss the video - you can click on the below link to go through the video after the schedule.
https://youtu.be/dj565kgP1Ss
* Upcoming Live Session - Overview of Big Data Certifications (Spark Based) - https://www.meetup.com/itversityin/events/271739702/
Relevant Playlists:
* Apache Spark using Python for Certifications - https://www.youtube.com/playlist?list=PLf0swTFhTI8rMmW7GZv1-z4iu_-TAv3bi
* Free Data Engineering Bootcamp - https://www.youtube.com/playlist?list=PLf0swTFhTI8pBe2Vr2neQV7shh9Rus8rl
* Join our Meetup group - https://www.meetup.com/itversityin/
* Enroll for our labs - https://labs.itversity.com/plans
* Subscribe to our YouTube Channel for Videos - http://youtube.com/itversityin/?sub_confirmation=1
* Access Content via our GitHub - https://github.com/dgadiraju/itversity-books
* Lab and Content Support using Slack
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
Product-thinking is making a big impact in the data world with the rise of Data Products, Data Product Managers, data mesh, and treating “Data as a Product.” But Honest, No-BS: What is a Data Product? And what key questions should we ask ourselves while developing them? Tim Gasper (VP of Product, data.world), will walk through the Data Product ABCs as a way to make treating data as a product way simpler: Accountability, Boundaries, Contracts and Expectations, Downstream Consumers, and Explicit Knowledge.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
Learn how Parasoft service virtualization helps teams test earlier, faster, and more completely. Covers service virtualization for Agile development, service virtualization for load/performance testing, service virtualization for eliminating test constraints.
Designed to address more mature programs, this tutorial covers the issues and approaches to sustaining Data Governance and value creation over time, amongst a changing business and personnel environment.
Part of the reason many companies launch a Data Governance program again and again is that over time, it is challenging to maintain the enthusiasm and excitement that accompanies a newly initiated program.
Learn about:
• Typical obstacles to sustainable Data Governance
• Re-energizing your program after a key player (or two) leave and other personnel challenges
• Staying relevant to the company as the business evolves over time
• Understanding the role of metrics and why they are critical
• Leveraging Communication and Stakeholder Management practices to maintain commitment
• Embedding Data Governance into the operations of the company
Data Profiling, Data Catalogs and Metadata HarmonisationAlan McSweeney
These notes discuss the related topics of Data Profiling, Data Catalogs and Metadata Harmonisation. It describes a detailed structure for data profiling activities. It identifies various open source and commercial tools and data profiling algorithms. Data profiling is a necessary pre-requisite activity in order to construct a data catalog. A data catalog makes an organisation’s data more discoverable. The data collected during data profiling forms the metadata contained in the data catalog. This assists with ensuring data quality. It is also a necessary activity for Master Data Management initiatives. These notes describe a metadata structure and provide details on metadata standards and sources.
apidays LIVE Helsinki & North - Product data ecosystem in the digital dental ...apidays
apidays LIVE Helsinki & North 2021 - APIs, Platforms, And Ecosystems - Transforming Industries And Experiences
March 15 & 16, 2021
Product data ecosystem in the digital dental industry
Sujoy Kumar Saha, Data Architect at 3Shape
A simple guide to learn what EA is, why it’s important and how you can be using it to help your enterprise.
For more information: info@boc-group.com
Try ADOIT for EA:
https://www.boc-group.com/adoit/#test-it
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...Alan McSweeney
Your data has value to your organisation and to relevant data sharing partners. It has been expensively obtained. It represents a valuable asset on which a return must be generated. To achieve the value inherent in the data you need to be able to make it appropriately available to others, both within and outside the organisation.
Organisations are frequently data rich and information poor, lacking the skills, experience and resources to convert raw data into value.
These notes outline technology approaches to achieving compliance with data privacy regulations and legislation while providing access to data.
There are different routes to making data accessible and shareable within and outside the organisation without compromising compliance with data protection legislation and regulations and removing the risk associated with allowing access to personal data:
• Differential Privacy – source data is summarised and individual personal references are removed. The one-to-one correspondence between original and transformed data has been removed
• Anonymisation – identifying data is destroyed and cannot be recovered so individual cannot be identified. There is still a one-to-one correspondence between original and transformed data
• Pseudonymisation – identifying data is encrypted and recovery data/token is stored securely elsewhere. There is still a one-to-one correspondence between original and transformed data
These technologies and approaches are not mutually exclusive – each is appropriate to differing data sharing and data access use cases
The data privacy regulatory and legislative landscape is complex and getting even more complex so an approach to data access and sharing that embeds compliance as a matter of course is required.
Appropriate technology appropriately implemented and operated is a means of managing and reducing risks of re-identification by making the time, skills, resources and money necessary to achieve this unrealistic.
Technology is part of a risk management approach to data privacy. There is wider operational data sharing and data privacy framework that includes technology aspects, among other key areas. Using these technologies will embed such compliance by design into your data sharing and access facilities. This will allow you to realise value from your data successfully.
In the past few years, the term "data lake" has leaked into our lexicon. But what exactly IS a data lake? Some IT managers confuse data lakes with data warehouses. Some people think data lakes replace data warehouses. Both of these conclusions are false. Their is room in your data architecture for both data lakes and data warehouses. They both have different use cases and those use cases can be complementary.
Todd Reichmuth, Solutions Engineer with Snowflake Computing, has spent the past 18 years in the world of Data Warehousing and Big Data. He spent that time at Netezza and then later at IBM Data. Earlier in 2018 making the jump to the cloud at Snowflake Computing.
Mike Myer, Sales Director with Snowflake Computing, has spent the past 6 years in the world of Security and looking to drive awareness to better Data Warehousing and Big Data solutions available! Was previously at local tech companies FireMon and Lockpath and decided to join Snowflake due to the disruptive technology that's truly helping folks in the Big Data world on a day to day basis.
Data Mesh in Practice - How Europe's Leading Online Platform for Fashion Goes...Dr. Arif Wider
A talk presented by Max Schultze from Zalando and Arif Wider from ThoughtWorks at NDC Oslo 2020.
Abstract:
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
At Zalando - europe’s biggest online fashion retailer - we realised that accessibility and availability at scale can only be guaranteed when moving more responsibilities to those who pick up the data and have the respective domain knowledge - the data owners - while keeping only data governance and metadata information central. Such a decentralized and domain focused approach has recently been coined a Data Mesh.
The Data Mesh paradigm promotes the concept of Data Products which go beyond sharing of files and towards guarantees of quality and acknowledgement of data ownership.
This talk will take you on a journey of how we went from a centralized Data Lake to embrace a distributed Data Mesh architecture and will outline the ongoing efforts to make creation of data products as simple as applying a template.
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...Alan McSweeney
These notes describe a generalised data integration architecture framework and set of capabilities.
With many organisations, data integration tends to have evolved over time with many solution-specific tactical approaches implemented. The consequence of this is that there is frequently a mixed, inconsistent data integration topography. Data integrations are often poorly understood, undocumented and difficult to support, maintain and enhance.
Data interoperability and solution interoperability are closely related – you cannot have effective solution interoperability without data interoperability.
Data integration has multiple meanings and multiple ways of being used such as:
- Integration in terms of handling data transfers, exchanges, requests for information using a variety of information movement technologies
- Integration in terms of migrating data from a source to a target system and/or loading data into a target system
- Integration in terms of aggregating data from multiple sources and creating one source, with possibly date and time dimensions added to the integrated data, for reporting and analytics
- Integration in terms of synchronising two data sources or regularly extracting data from one data sources to update a target
- Integration in terms of service orientation and API management to provide access to raw data or the results of processing
There are two aspects to data integration:
1. Operational Integration – allow data to move from one operational system and its data store to another
2. Analytic Integration – move data from operational systems and their data stores into a common structure for analysis
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
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
As part of this session, I will be giving an introduction to Data Engineering and Big Data. It covers up to date trends.
* Introduction to Data Engineering
* Role of Big Data in Data Engineering
* Key Skills related to Data Engineering
* Role of Big Data in Data Engineering
* Overview of Data Engineering Certifications
* Free Content and ITVersity Paid Resources
Don't worry if you miss the video - you can click on the below link to go through the video after the schedule.
https://youtu.be/dj565kgP1Ss
* Upcoming Live Session - Overview of Big Data Certifications (Spark Based) - https://www.meetup.com/itversityin/events/271739702/
Relevant Playlists:
* Apache Spark using Python for Certifications - https://www.youtube.com/playlist?list=PLf0swTFhTI8rMmW7GZv1-z4iu_-TAv3bi
* Free Data Engineering Bootcamp - https://www.youtube.com/playlist?list=PLf0swTFhTI8pBe2Vr2neQV7shh9Rus8rl
* Join our Meetup group - https://www.meetup.com/itversityin/
* Enroll for our labs - https://labs.itversity.com/plans
* Subscribe to our YouTube Channel for Videos - http://youtube.com/itversityin/?sub_confirmation=1
* Access Content via our GitHub - https://github.com/dgadiraju/itversity-books
* Lab and Content Support using Slack
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
Product-thinking is making a big impact in the data world with the rise of Data Products, Data Product Managers, data mesh, and treating “Data as a Product.” But Honest, No-BS: What is a Data Product? And what key questions should we ask ourselves while developing them? Tim Gasper (VP of Product, data.world), will walk through the Data Product ABCs as a way to make treating data as a product way simpler: Accountability, Boundaries, Contracts and Expectations, Downstream Consumers, and Explicit Knowledge.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
Learn how Parasoft service virtualization helps teams test earlier, faster, and more completely. Covers service virtualization for Agile development, service virtualization for load/performance testing, service virtualization for eliminating test constraints.
Designed to address more mature programs, this tutorial covers the issues and approaches to sustaining Data Governance and value creation over time, amongst a changing business and personnel environment.
Part of the reason many companies launch a Data Governance program again and again is that over time, it is challenging to maintain the enthusiasm and excitement that accompanies a newly initiated program.
Learn about:
• Typical obstacles to sustainable Data Governance
• Re-energizing your program after a key player (or two) leave and other personnel challenges
• Staying relevant to the company as the business evolves over time
• Understanding the role of metrics and why they are critical
• Leveraging Communication and Stakeholder Management practices to maintain commitment
• Embedding Data Governance into the operations of the company
Data Profiling, Data Catalogs and Metadata HarmonisationAlan McSweeney
These notes discuss the related topics of Data Profiling, Data Catalogs and Metadata Harmonisation. It describes a detailed structure for data profiling activities. It identifies various open source and commercial tools and data profiling algorithms. Data profiling is a necessary pre-requisite activity in order to construct a data catalog. A data catalog makes an organisation’s data more discoverable. The data collected during data profiling forms the metadata contained in the data catalog. This assists with ensuring data quality. It is also a necessary activity for Master Data Management initiatives. These notes describe a metadata structure and provide details on metadata standards and sources.
apidays LIVE Helsinki & North - Product data ecosystem in the digital dental ...apidays
apidays LIVE Helsinki & North 2021 - APIs, Platforms, And Ecosystems - Transforming Industries And Experiences
March 15 & 16, 2021
Product data ecosystem in the digital dental industry
Sujoy Kumar Saha, Data Architect at 3Shape
Increase your ROI with Hadoop in Six Months - Presented by Dell, Cloudera and...Cloudera, Inc.
Are you struggling to validate the added costs of a Hadoop implementation? Are you struggling to manage your growing data?
The costs of implementing Hadoop may be more beneficial than you anticipate. Dell and Intel recently commissioned a study with Forrester Research to determine the Total Economic Impact of the Dell | Cloudera Apache Hadoop Solution, accelerated by Intel. The study determined customers can see a 6-month payback when implementing the Dell | Cloudera solution.
Join Dell, Intel and Cloudera, three big data market leaders, to understand how to begin a simplified and cost-effective big data journey and to hear case studies that demonstrate how users have benefited from the Dell | Cloudera Apache Hadoop Solution.
17 Must-Do's to Create a Product-Centric IT OrganizationCognizant
Tightening IT-business alignment and embracing Agile, DevOps and Lean Startup principles, while transcending traditional project management disciplines by incorporating product engineering rigor, are critical to creating an effective, digitally enhanced business.
The digital transformation of CPG and manufacturingCloudera, Inc.
Both CPG (Consumer Packaged Goods) and manufacturing industries face similar challenges: in order to differentiate and innovate, they need to draw insight from the one thing they both have in abundance - data. The source of the data may be different; the opportunity is always innovation and differentiation. For CPG, forever changing buyer expectations must flow into product development and sales. For both CPG and manufacturing, Industry 4.0 promises improved efficiency, lower costs, and higher revenues. Becoming data-driven is the key for both industries and requires clever combination of machine learning, analytics and cloud for success.
In this webinar, business strategist Frank Vullers will discuss how Cloudera's platform is central to this century’s industrial revolution: the digital transformation of the CPG and manufacturing industries.
Analyst Webinar: Best Practices In Enabling Data-Driven Decision MakingDenodo
Watch full webinar here: https://bit.ly/37YkgN4
This presentation looks at the trends that are emerging from companies on their journeys to becoming data-driven enterprises.
These trends are taken from a survey of 500 companies and highlight critical success factors, what companies are doing, their progress so far and their plans going forward. It also looks at the role that data virtualization has within the data driven enterprise.
During the session we'll address:
- What is a data-driven enterprise?
- What are the critical success factors?
- What are companies doing to create a data-driven enterprise and why?
- What progress are they making?
- What are the plans on people, process and technologies?
- Why is data virtualization central to provisioning and accessing data in a data-driven enterprise?
- How should you get started?
In this deck from the 2019 UK HPC Conference, Glyn Bowden from HPE presents: The Eco-System of AI and How to Use It.
"This presentation walks through HPE's current view on AI applications, where it is driving outcomes and innovation, and where the challenges lay. We look at the eco-system that sits around an AI project and look at ways this can impact the success of the endeavor."
Watch the video: https://wp.me/p3RLHQ-kVS
Learn more: https://www.hpe.com/us/en/solutions/artificial-intelligence.html
and
http://hpcadvisorycouncil.com/events/2019/uk-conference/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
Entry Points – How to Get Rolling with Big Data AnalyticsInside Analysis
The Briefing Room with Robin Bloor and IBM
Live Webcast Sept. 24, 2013
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?AT=pb&SP=EC&rID=7501927&rKey=664935ceb7de1aec
Where to begin? That question remains prominent for many organizations who are trying to leverage the value of big data analytics. Most sources of big data are quite different than traditional enterprise data systems. This requires new skill sets, both for the granular integration work, as well as the strategic business perspective required to design useful solutions.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains the pain points associated with modern data volumes and types. He will be briefed by Rick Clements of IBM, who will tout IBM's big data platform, specifically InfoSphere BigInsights, InfoSphere Streams and InfoSphere Data Explorer. He will also present specific use cases that demonstrate how IT and the line of business can springboard over existing challenges, gain insight and improve operational performance.
Visit InsideAnalysis.com for more information
Denodo DataFest 2016: ROI Justification in Data VirtualizationDenodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/eB3lOM
There are two sides to the ROI coin. One is TCO and the other is business impact. In this session, we will explain how to justify and measure the ROI for data virtualization, and share examples of authentic business benefits realized by our key customers. If you need help justifying the investment, don't miss this session!
In this session, you will learn:
• How data virtualization is used to leverage data as a strategic asset, and to monetize data
• How to justify and measure ROI for data virtualization solutions
• Examples of business benefits realized by our key customers
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
ARIS User Group: NRB brings your business to the next levelNRB
In this short presentation, we identified the challenges and the opportunities that every single business is facing today. NRB makes IT drive your business into the Digital Era. Our verticalized portfolio focuses on the right solutions to help your business grow and reach the next level.
Integrated Lifecycle Management A Solution for the Digital AgeDavid G Sherburne
Originally presented at PI 2017 in Fort Worth Texas this presentation discusses the challenges companies face in becoming agile enough to compete in a software and services driven world. Its critical that enterprises integrate applications to allow control of baselines "concept to customer" and release software in a rapid and controlled manner. Please contribute to my LinkedIn groups ILM-Integrated Lifecycle Management and IoT-Selling Things to Selling Services.
It strategy for life sciences david royleDavid Royle
An Information Technology strategy for contract research organisations in Life Sciences. A layered approach to building an Information Technology platform.
How to Capitalize on Big Data with Oracle Analytics CloudPerficient, Inc.
The average age of a company listed on the S&P 500 has fallen from almost 60 years old in the 1950s to less than 20 years old today. Innovative companies that are willing to embrace transformative technologies make the list today, while businesses that are hesitant to embrace change risk becoming obsolete.
Innovators use big data solutions as a competitive advantage to increase revenue, reduce cost, and improve cash flow. Turn big data into actionable insights with Oracle Analytics Cloud.
We identified the big data opportunities in front of you and how to take advantage of them:
-Big data and its architecture
-Why a big data strategy is imperative to remaining relevant
-How Oracle Analytics Cloud can help you connect people, places, data, and systems to fundamentally change how you analyze, understand, and act on information
Next Generation Data Center - IT TransformationDamian Hamilton
Computerworld CIO Event in Hong Kong sponsored by Dimension Data, EMC & Cisco.
Insights into Dimension Data's DC strategy and recent Client engagements
Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...Denodo
This content was presented during the Smart Data Summit Dubai 2015 in the UAE on May 25, 2015, by Jesus Barrasa, Senior Solutions Architect at Denodo Technologies.
In the era of Big Data, IoT, Cloud and Social Media, Information Architects are forced to rethink how to tackle data management and integration in the enterprise. Traditional approaches based on data replication and rigid information models lack the flexibility to deal with this new hybrid reality. New data sources and an increasing variety of consuming applications, like mobile apps and SaaS, add more complexity to the problem of delivering the right data, in the right format, and at the right time to the business. Data Virtualization emerges in this new scenario as the key enabler of agile, maintainable and future-proof data architectures.
Denodo DataFest 2016: Enterprise View of Data with Semantic Data LayerDenodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/kPmzWU
Gaining an enterprise view of the data across different independent lines of businesses is difficult when the operations, systems, and data are inherently siloed. VSP Global is a conglomerate operating different businesses across eyewear insurance, manufacturing, and retail. They are integrating the silos using a semantic data layer.
In this presentation, the Enterprise Data Architect at VSP Global, Tim Fredricks will present:
• The challenges associated with data siloed across different LOBs
• How to build a semantic data layer using data virtualization
• Centralizing business rules in the data virtualization layer
This session also includes a panel discussion with:
• Tim Fredricks, Enterprise Data Architect at VSP Global
• Rick Hart, Director of Global Technology Solutions at BioStorage Technologies
• Jeff Veis, VP Big Data Platform Marketing at HPE
• Mike Litzkow, Sales Director at Denodo (as moderator)
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
Building the Artificially Intelligent EnterpriseDatabricks
This session looks at where we are today with data and analytics and what is needed to transition to the Artificially Intelligent Enterprise.
How do you mobilise developers to exploit what data scientists and business analysts have built? How do you align it all with business strategy to maximise business outcomes? How do you combine BI, predictive and prescriptive analytics, automation and reinforcement learning to get maximum value across the enterprise? What is the blueprint for building the artificially intelligent enterprise?
•Data and analytics – Where are we?
•Why is the journey only half-way done?
•2021 and beyond – The new era of AI usage and not just build
•The requirement – event-driven, on-demand and automated analytics
•Operationalising what you build – DataOps, MLOps and RPA
•Mobilising the masses to integrate AI into processes – what needs to be done?
•Business strategy alignment – the guiding light to AI utilisation for high reward
•Agility step change – the shift to no-code integration of AI by citizen developers
•Recording decisions, and analysing business impact
•Reinforcement-learning – transitioning to continuous reward
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
2. WHO AM I
• Big Data / Analytics / BI & Cloud Solutions Specialist
• http://www.linkedin.com/in/JulioPhilippe
• Skills
Architecture
Business Intelligence
IT Transformation
Cloud Computing
IT Solutions
Management
Mentoring
Big Data
Analytics
Business Development
Hadoop
Datacenter
Optimization
Data Warehousing
2
Designing an IT Solution
3. WHY IT TRANSFORMATION
Observations
Next Steps
Misrepresented as solution or service
Product approach
Solution approach based on a
framework
-
Development is reactive
-
Expose the pains without offering
solution
Don’t survive to maturity
Crisis control management
Never present an “Enterprise” view
Don’t measure the IT performance and
value
3
-
Business Model
IT Model
IT Maturity Model
IT as a Service
Reusable and repeatable solutions
Business value management
Continuous change management
Measuring IT performance and value
Designing an IT Solution
4. SOLUTION APPROACH VS. PRODUCT APPROACH
Business Value
Added Value
Business Stakes
4
Designing an IT Solution
5. FROM BUSINESS TO INFRASTRUCTURE
Objective
Information
Business
Strategy
Process
Flow
Procedure
Organization
User
Function
Application
Tool
Technology
Data
5
Hardware
Designing an IT Solution
6. ARCHITECTURE ON 3 LAYERS
•
Business Architecture
•
•
•
•
Application Architecture
•
•
•
Software
Applications
Technical Architecture
•
•
6
Business needs
Data model
Flows diagram
Hardware
Tools
Designing an IT Solution
7. BUSINESS DRIVERS - EXAMPLES
Telecommunications
Manufacturing
•
Resources Consolidation
•
•
Manufacturer / Supplier collaboration
•
Reengineering sales
•
Re engineering distribution
•
Intensified focus on customer
•
•
•
•
•
•
Costs Reduction
•
Eco Responsibility
•
•
•
7
Provide direct information access
Citizen services enhancements
Public security
Provide timely access to decision support
information
Accomplish more work with fewer
resources
Recognize and adapt to frequent business
process changes
Eco Responsibility
•
•
•
•
•
•
•
•
•
Retail
Government
•
•
•
•
Convergence audio/video,
fixed/mobile
Internationalization
Outsourcing
Customize Services
Costs Reduction
Eco Responsibility
Banking & Insurance
•
•
•
•
•
•
•
•
•
Need to manage profitability and control
expenses
Increased competition from providers
Need to mitigate current and emerging risk
Increased regulatory pressures
Industry consolidation
Consumers adoption of electronic channels
and payments
Consumers concerns about security and
privacy
Costs Reduction
Eco Responsibility
Designing an IT Solution
Increase banking and financial transaction
Critical size on the market
International development
Banking and financial regulations
observance
Inheritance optimization and
management
Activities diversification
Increase risks management
Eco Responsibility
Costs Reduction
Media & Entertainment
•
•
•
•
•
•
Emerging media environments
Evolving consumer behaviors
On-going technological innovation
Increasing scarcity of consumer
attention
Costs Reduction
Eco Responsibility
8. BUSINESS DRIVERS - EXAMPLES
Education & Research
Healthcare
•
•
•
•
•
•
•
Accelerating Employers
Led Initiatives - Consumerism Entering
Healthcare
Accelerating IT Adoption Among Providers
New Consumers-centric Technologies
Disintermediation of Care
Patient Security
Costs Reduction
Eco Responsibilityy
•
•
•
•
•
•
•
•
•
Pharmaceutical
Energy
•
•
•
•
•
•
•
•
8
Increase refining capacity in traditional
petroleum
Grid Connected Power improvement
Investments in the renewable energy
sector
Energy education
Carbon emissions reduction
Power consumption reduction
Energy cost reduction
Security power plan
Enable Anytime, Anywhere Access
Create Intelligent Buildings
Protect School Records and Information
Protect Safety Incidents on School Campus
Creating a New Form of Collaborative
Education
Expanding the Boundaries of Knowledge
Costs Reduction
Eco Responsibility
•
•
•
•
•
•
•
•
Improved R&D efficiency leveraging
collaboration capabilities
Improved collaboration between public
and private labs and partners
Improved lifecycle management of products
Enhanced information dissemination
Improved relations with key stakeholders
such as doctors and consumers
Focus on products that have the best chance
of getting to market and accelerated time to
market
Costs Reduction
Eco Responsibility
Designing an IT Solution
Transportation & Travel
•
•
•
•
•
•
•
•
•
Social Responsibility
Technology, exposure to other cultures
Collecting and Sharing experiences
Consolidation for buying power
Business Travel online Adoption
Increase Safety
Green Initiatives
Costs Reduction
Eco Responsibility
Consumers Packaged Goods
•
•
•
•
•
•
•
Explore applications for Boomer
positioning
Simplify and purify the products
New private label food and beverage
introductions
New products development
Customers in control
Discount brands
Eco Label
9. BUSINESS MODEL
Industry
-
Ex : Personalize and individualize services, Adaptation
for regulatory constraints…
Ex : Sales, Supply, Billing…
KPI
Ex : Convergence audio/video, fixed/mobile phone.,
Costs Reduction…
Business Drivers
Market Trends
– Ex : Increasing transaction volumes, shrinking
margins. Managing costs and risks…
Business KPI
-
9
Objective
Industry
Business Drivers
-
Strategy
Process
-
Ex : Innovation, Customer satisfaction...
Objective
-
Market Trends
Strategy
-
Ex : Manufacturing., Banking, Telecommunications,
Government...
Ex : Sales, Gross margin…
Designing an IT Solution
Process
10. IT MODEL
-
Infrastructure Virtualization...
Availability
Time To Repair, IT Costs Reduction…
Cloud computing, Storage mass memory…
IT KPI
-
10
Flexibility
KPI
IT Trends
-
Clustering, Components redundancy...
IT Drivers
-
Data Access, Encryption...
Flexibility
-
ECO
Scalability
Availability
-
Costs saving , Ecology, Economy
Security
-
Horizontal, Vertical, Increase Data Volume...
ECO
-
IT Trends
Scalability
Availability ratio, TCO…
Designing an IT Solution
IT Drivers
Security
11. IT KEY PERFORMANCE INDICATORS
Technical Performance Indicators
Financial Performance Indicators
TCO, ROI
Depreciation...
Ecological Performance Indicators
11
I/O, SAPS, SpecInt, TPC-H
Availability Ratio, Time To Repair
Data Loss Ratio...
Space, Watt
CO2, RoHS Ratio
WEEE Ratio...
Designing an IT Solution
12. IT SOLUTION
Software
-
-
Big Data, BI
Cloud
HPC
ERP
Web
-
-
Servers
-
-
Applications development
IT Transformation
Installation
Deployment
Tuning
Support
Training
-
CPU, RAM
Disk, I/O
Connectivity
Storage
-
Services
-
12
Disk, I/O
SAN, NAS, DAS
Connectivity
Network
-
Designing an IT Solution
Ethernet
Infiniband
Connectivity
13. FROM BUSINESS TO IT SOLUTION
Business Model
Applications
Apps
IT Model
Strategy
ECO
Scalability
Objective
Industry
KPI
Market
Trends
Business
Drivers
KPI
Flexibility
Process
Governance
Availability
IT Solution
13
Security
KPI
Designing an IT Solution
Software
Servers
Storage
Network
Services
IT Trends
IT Drivers
KPI
15. THINK ECONOMY AND ECOLOGY
Total Cost of Ownership
Return On Investment
Footprint reduction
Electrical consumption
reduction
Open Source HW/SW
Number of Servers,
Storage, Network reduction
Global Financial Services
15
Designing an IT Solution
Scalability
ECO
Security
Flexibility
Availability
16. THINK SECURITY
Data Replication
Data Encryption
Data De-duplication
Backup of data
Data Retention
Fire Wall, DMZ
RAID Factor
O.S. Security
File System
16
Designing an IT Solution
Scalability
ECO
Security
Flexibility
Availability
17. THINK AVAILABILITY
Cluster
Time To Repair
Support Service
Disaster Recovery
Components
Redundancy
Grid Computing
On-Site Support
17
Scalability
ECO
Security
Flexibility
Availability
Designing an IT Solution
19. FROM BUSINESS TO INFRASTRUCTURE
Business Driver
IT Driver
Drivers
1,n
Drivers
1,n
1,n
Aligned with
IT KPI
Indicators
1,n
Aligned with
1 Business Driver → 1 to “n” IT Drivers
1 IT Driver → 1 to “n” KPIs
Banking & Finance industry Example
IT Drivers
Business Drivers
Increase data volume
Increase banking and
financial transactions
IT KPIs
RPO
Conservation Time
Data processing
and simulation performance
Nb of calculation
Nb of Gflops
19
Designing an IT Solution
20. ROAD TO DYNAMIC INFRASTRUCTURE
Value
Infrastructure
as a Services
Infrastructure
Managed Services
Infrastructure
Solutions
Infrastructure
Products
Time
Dynamic Infrastructure
Infrastructure
as a Services
Cut your IT costs down leveraging economies of scale by accessing a
centrally shared infrastructure
Infrastructure
Managed
Services
Delegate the resposability
to run your IT
Utilize your expert support services for the operation of your dynamic
infrastructure
Infrastructure
Solutions
Use pre-tested
infrastructure solutions
Infrastructure
Products
20
Run your applications on
a shared environment
Use building blocks
design for dynamism
Benefit from evaluation and integration experience out of many
projects
Products and services designed for virtualized and automated
environments
Designing an IT Solution
21. INFRASTRUCTURE OPTIMIZATION MODEL
From a basic unmanaged cost center toward an automated,
dynamic, and strategic asset.
Organizations
21
Designing an IT Solution
22. ROAD TO BUSINESS VALUE
Maturity
Governance
Sustain value
IT processes performance
Drive eco-responsibility
Risk management
Relationship management
Level 5
Business Value
Management
Level 4
IT Service
Management
Capacity on-Demand
As a service
Pay as you go
Appliance
Automation
Provisioning
Deployment
Dynamic workload
Level 3
IT Operation
Management
Virtualization
Level 2
IT Component
Management
Level 1
Crisis Control
Industrialization
vSphere, Hyper-V, Xen, KVM
Standardization
Server, storage, network
Value
22
Designing an IT Solution
23. STANDARDIZATION
Standards : O.S, Servers, Storage, Network
Building and sizing rules
Best Practices and Methodologies : ITIL,
COBIT, TOGAF…
Components : servers, storage, network and
services
Infrastructure consolidation : reduce number
of servers, storage and power consumption
23
Designing an IT Solution
24. VIRTUALIZATION
Abstraction of IT resources
Full virtualization
Hardware-assisted virtualization
Partial virtualization
Paravirtualization
Operating system-level virtualization
Hardware virtualization
24
Designing an IT Solution
25. AUTOMATION
Automation : a process which may once have been
performed manually but has been altered in some
way which allows a machine or computer to either
wholly or partially manipulate the process to save
time
Infrastructure Automation : the basic services
necessary for your infrastructure to operate largely
without the aid of a keeper
-
25
Discovering, Provisioning, Updating, Monitoring
Measuring, Capacity Planning, Deployment
Designing an IT Solution
26. INDUSTRIALIZATION
Repeatability of the solutions
Reusability of the solutions
Appliances
Homogeneity of the solutions
Capacity Units Models
With Agility (Cloud Computing)
-
-
-
26
Software as a Service : Applications offered on demand over the
network
(salesforce.com, iTunes Store...)
Platform as a Service : Developer platform with built-in services
(Google App Engine, Microsoft Azure...)
Infrastructure as a Service : Basic storage and compute capabilities
offered as a service (Amazon Web Services, Mosso...)
Designing an IT Solution
27. GOVERNANCE
Sustain value objectives
Increase IT processes performance
and their customers orientations
Drive economy and ecology
Develop IT solutions and
competencies for new generations
Manage risks
Manage relationship
27
Designing an IT Solution
29. DYNAMIC ARCHITECTURE
TCP/IP
• Open Stack, Hadoop, NoSQL DB…
• Virtualization, Automation
Ethernet Switches
Applications Servers
29
Compute & Storage Nodes
Designing an IT Solution
30. WHAT IS BUSINESS VALUE
• Focus on Industry
- Bank/Finance, Government, Retail,
Telco, Manufacturing...
• Business Value is Large
- Stakeholder Value, Customer Value,
Employee Value
- Partner Value, Supplier Value,
Managerial Value, Societal Value
• Key Business Indicators
- Profitability, Revenue Growth,
- Customer Satisfaction, Market Share,
- Cross-Sell Ratio, Marketing Campaign
Response Rates
- Relationship Duration...
• Common Language Management
30
Designing an IT Solution
31. WHAT IS “IT” VALUE
• Business/IT Alignment
• Intellectual Properties
• IT Process Automation
• IT Performance
• Innovation
• Community
• Know-How
• Expertise
• Service Level Agreement
• IT Portfolio and Maturity
31
Designing an IT Solution
32. BUSINESS VALUE PROPOSITION
IT Drivers
Company
Strategy/Objectives
IT Value Proposition
Technical and Eco
Performance Indicators
Key Business Drivers
Promise
Technology/Methodology
Business and Eco
References
Performance Indicators
Added Value Services
Partners
IT Trends
Commitment
32
Designing an IT Solution
34. Hig
h
Pe
rfo
rm
anc
We
b2
eC
.0
om
pu
tin
Sta
g
nd
ard
ize
Tec
De
skt
hn
ica
op
lB
Vi r
asi
tua
Inf
s
liza
ras
tio
trc
n
utu
re
Ide
Vi r
nti
tua
ty M
liza
ana
tio
n
Arc
gem
hiv
ent
ing
Bu
sin
ess
Ap
pli
Da
cat
ta P
i on
rot
Inf
ect
ras
i on
Se
tru
cur
ctu
ity
re
Dis
ast
er
Re
cov
Op
ery
en
So
urc
e
TELECOMMUNICATIONS EXAMPLE
Convergence audo/video, fixed/mobile
Resources consolidation
Internationalisation
Outsourcing
Customize Services
Costs Reduction
ECO Responsibility
•
•
34
To manage complex network
evolution towards all-IP and
converged networks, many
operators are now looking to
contract fewer providers to
evolve and optimize their
networks.
Business growth is a focus
area for operators who
understand the need to
differentiate themselves with
new user segments, revenue
streams, and services.
•
Outsourcing
•
Customize Services
•
Costs Reduction
•
ECO Responsibility
Maximizing efficiency and
reducing operational costs
have never been so
important to operators,
which must review core and
non-core activities and
increasingly consider
outsourcing network
operations.
Designing an IT Solution
Business intelligence
•
IT Security
•
Business continuity
•
Web 2.0
Speed up deployment new services
Service Level Agreement
High Performance Computing
Open Source componants
applications
IT Costs reduction
•
Internationalization
•
•
•
Virtualization technologies (server,
storage, network, services and
desktop)
•
Resources Consolidation
•
•
•
Standardization and technical basis,
Infrastructure consolidation
•
Convergence
•
•
•
IT DRIVERS
•
BUSINESS DRIVERS
MARKET TRENDS
PROMISE
Business Drivers
Green IT
35. MEASURING IT VALUE
How to prove that IT solutions
create Business Value ?
« You Can’t Manage
What You Don’t Measure ! »
« IT value benefits are beyond costs
reduction, contributing to increase
company's profitability »
35
Designing an IT Solution
36. MEASURING IT VALUE
• Key Performance Indicators (KPIs)
• Technical Performance Indicators
- CPU, I/O, SAPS, SpecInt, TPC-H
- Availability Ratio, Time To Repair
• Financial Performance Indicators
- TCO, ROI
- Depreciation...
• Ecological Performance Indicators
- Space, Watt
- CO2, RoHS Ratio
- Waste Electrical and Electronic Equipment Recycling Ratio...
•
•
Define IT Drivers
•
36
Define KBD
Define KPI
Implement
Infrastructure
Measure
Baseline
Define
Metrics
•
Build
measurement
framework
•
•
Define
Infrastructure
Build
Infrastructure
Designing an IT Solution
Measure
Infrastructure
•
Monitor
•
Analyze
•
Optimize
Business
Value
•
Determine Value
with TCO/ROI
methodology
37. MEASURING IT VALUE
Put technical sensors in different points
of the infrastructure
Collect data from sensors
Calculate the indicators value
Integrate the indicators values into the
CMDB and calculate the complex
indicators
Analyze the IT infrastructure
performance
37
Information Gathering
Ecological KPI
Technical KPI
CMDB
Analyze the results with reporting tool
Compare the results obtained to the awaited
results
Design dashboard for CIO,
IT Decision-maker, Manager
Optimize the IT Infrastructure on
Technical, Financial and Ecological sides
Designing an IT Solution
Dashboard & Report
Data analysis
& visualization
Financial KPI
38. INCREASING IT PERFORMANCE
• Performance Lever is a
specific Key Performance
Indicator
• Lever increases the system
performance
• Lever interacts with Key
Performance Indicators
Ex. Increasing The Speed of The Car
- The Gear Lever is changing up
- The Speed Indicator is increasing
38
Designing an IT Solution
39. INCREASING IT PERFORMANCE
Example
• #Concurrent Users Indicator is a Performance Lever
• #CPU and #I/O are Key Performance Indicators
• #CPU = f(#Concurrent users)
• #I/O = f(#Concurrent users)
• Start a Provisioning Process automatically with #CPU and #I/O
Values
-
Integrate a new Web server with IT Automation Software
-
39
Activate #CPU and #I/O Cards with Capacity on Demand Process
Testing
Designing an IT Solution
40. INFRASTRUCTURE SIZING METHODOLOGY
Data Volume
Concurrent Users
Extract volume
Complexity
Concurrent Requests
Data Processing
Complexity
S/W Components
Batch
Time Range
DB Structure
Users
Time Range
Costs
Benchmarks
Results
A balanced configuration from CPUs to Disks
CPUs
40
Memory
I/O
Disks
Designing an IT Solution
42. BUSINESS MODEL EXAMPLE
Manufacturing company has gradually
extended its sphere of operations around
the world
Ability to innovate and invent for the
day-to-day life in tomorrow’s world
Objective
Strategy
Industry
Customer Satisfaction
Innovation
Better sales management across the world
The system must be on top for users and
data integration activities in every time
zones
KPI
•
•
Margin per country
•
Customer satisfaction rate per country
•
Strategy
Sales per country
Product quality per country
Objective
Industry
KPI
Process
Business Drivers
42
Financial consolidation of the company
Designing an IT Solution
Increase sales
Reduce costs
Gain market shares
Customers satisfaction
Process
Sales Management
Reduce time to market
43. IT MODEL EXAMPLE
Scalability
X86 Servers
with 100% scalability
Eco
Very large database
Production, qualification, and
development environments
KPI
Increase data volume
•
•
Availability ratio = 99.999
Time to deploy a new country : 4h
•
Availability
•
Flexibility and dynamic integration
for new users and data integration
43
Virtualization of Infrastructure
Financing, leasing
Costs reduction
Security
Data replication
IT Security
Availability
IT Drivers
High performance
Return On Investment : 24 months
Flexibility
Reduce footprint
Mean Time To Repair : 4h - 6h
•
Volume of data integration/day = 4GB
•
Flexibility
Security
Low electric consumption
Volume of unstructured data = 150TB
•
Volume of structured data = 1TB
•
KPI
Data integration time = 6h
•
ECO
Scalability
Number of concurrent users = 100
Standard configurations
High availability
System agility
Designing an IT Solution
Clustering
24/7 Support
Right information at the
right time
44. IT SOLUTION EXAMPLE
2 x AppServers
Big Data Application
Data Warehouse
Solution
DHW Solution (1TB - raw data - structured data)
2 x Servers with 4 CPU quad-core / 64GB RAM. connected to 5TB on SAN
Storage Array System
All Application Servers per Time Zone and country are integrated in
different virtual machines for guarantying and increasing the performance
of the solution
2 x Master Nodes 48GB / 6 x 600GB 15RPM Disks
2 x Database
Servers
Hadoop Solution (150TB - raw data – unstructured data)
2 x Network
Switches
23 x Data Nodes 32GB / 12 x 2TB 7200 RPM Disks
Storage Array
SAN
Switch
An integrated model, built on the basis architectures,
flexible and extensible technologies allows to address the
change and business evolutions, reduced costs and
preserve the investment
2 x AppServers
The infrastructure is based on standards
Competency, expertise and support are adapted to
Service Level Agreement,
2 x Network
Switches
Hardware installation, professional services for data
integration, 24/7 support and leasing for enhancing the
TCO/ROI of the solution
2 x Master
Nodes
44
Hadoop Solution
Designing an IT Solution
23 x Data
Nodes