Building Value - Understanding the TCO and ROI of Apache Kafka & Confluentconfluent
For a product or service to be cost effective, it must be considered to be good value, where the benefits are worth at least what is paid for them. But how do we measure this, to prove the case? Given that value can be intangible, it can be hard to quantify and may have little relationship to cost. Added to this, the open source nature of Apache Kafka means that many companies skip the requirement to build a business case for it, until it has become mission critical and demands financial and human resources.
In this presentation, Lyndon Hedderly, Team Lead of Business Value Consulting at Confluent, will cover how Confluent works with customers to measure the business value of data streaming.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Straight Talk to Demystify Data LineageDATAVERSITY
Are you sure you trust the data you just used for that $10 million decision? To trust data authenticity we must first understand its lineage. However, the term "Data Lineage" itself is ambiguous since it is used in different contexts. "Business Lineage" links metadata constructs to specific terms in a business glossary. This approach is used by numerous Data Governance solutions. This approach alone comes up short, since it doesn't trace the real flow of information through an organization. "Technical Lineage" traces data's journey through different systems and data stores, providing an audit trail of the changes along the way. True "Data Lineage" combines both aspects, providing context to fully understand the data life cycle. Every step in data's journey is a potential source for introduction of error that could compromise Data Quality, and hence, business decisions. In this session, Ron Huizenga offers a comprehensive discussion of data lineage and associated Data Quality remediation approaches that are essential to build a foundation for Data Governance.
Enterprise Data Governance Framework With Change ManagementSlideTeam
“You can download this product from SlideTeam.net”
Presenting this set of slides with name Enterprise Data Governance Framework With Change Management. The topics discussed in these slides are Strategy, Organization, Management. This is a completely editable PowerPoint presentation and is available for immediate download. Download now and impress your audience. https://bit.ly/3b4VcEH
Future Proofing Your IT Operating Model for DigitalDavid Favelle
Having worked with Operating Model for over 10 years, Dave has new adopted DevOps, IT4IT and Continuous Delivery alongside traditional frameworks. The concept of the value stream is central to the thinking. The presentation was delivered as a Keynote at the Open Group in Amsterdam October 2017 -https://www.youtube.com/watch?v=Y7yH1JJKvqc&t=1969s
Note that Dave and the ValueFlow team deliver Operating Model on the ServiceNow platform.
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
Building Value - Understanding the TCO and ROI of Apache Kafka & Confluentconfluent
For a product or service to be cost effective, it must be considered to be good value, where the benefits are worth at least what is paid for them. But how do we measure this, to prove the case? Given that value can be intangible, it can be hard to quantify and may have little relationship to cost. Added to this, the open source nature of Apache Kafka means that many companies skip the requirement to build a business case for it, until it has become mission critical and demands financial and human resources.
In this presentation, Lyndon Hedderly, Team Lead of Business Value Consulting at Confluent, will cover how Confluent works with customers to measure the business value of data streaming.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Straight Talk to Demystify Data LineageDATAVERSITY
Are you sure you trust the data you just used for that $10 million decision? To trust data authenticity we must first understand its lineage. However, the term "Data Lineage" itself is ambiguous since it is used in different contexts. "Business Lineage" links metadata constructs to specific terms in a business glossary. This approach is used by numerous Data Governance solutions. This approach alone comes up short, since it doesn't trace the real flow of information through an organization. "Technical Lineage" traces data's journey through different systems and data stores, providing an audit trail of the changes along the way. True "Data Lineage" combines both aspects, providing context to fully understand the data life cycle. Every step in data's journey is a potential source for introduction of error that could compromise Data Quality, and hence, business decisions. In this session, Ron Huizenga offers a comprehensive discussion of data lineage and associated Data Quality remediation approaches that are essential to build a foundation for Data Governance.
Enterprise Data Governance Framework With Change ManagementSlideTeam
“You can download this product from SlideTeam.net”
Presenting this set of slides with name Enterprise Data Governance Framework With Change Management. The topics discussed in these slides are Strategy, Organization, Management. This is a completely editable PowerPoint presentation and is available for immediate download. Download now and impress your audience. https://bit.ly/3b4VcEH
Future Proofing Your IT Operating Model for DigitalDavid Favelle
Having worked with Operating Model for over 10 years, Dave has new adopted DevOps, IT4IT and Continuous Delivery alongside traditional frameworks. The concept of the value stream is central to the thinking. The presentation was delivered as a Keynote at the Open Group in Amsterdam October 2017 -https://www.youtube.com/watch?v=Y7yH1JJKvqc&t=1969s
Note that Dave and the ValueFlow team deliver Operating Model on the ServiceNow platform.
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help?
Given in Montreal on 14-Dec-2021
Effective Strategy Execution with Capability-Based Planning, Enterprise Arch...Iver Band
The difficulty of strategy execution should not be underestimated
Capability-based planning helps make strategy concrete
Enterprise architecture closes the remainder of this gap, and ensures alignment and coherence
Enterprise portfolio management allows managing large enterprise landscapes based on business value
ArchiMate models tie it all together, providing a clear line of sight from strategy definition to realization
Powerful tool support makes this a strong combination!
Running the Business of IT on ServiceNow using IT4ITcccamericas
In this presentation, Michael Fulton, President of CC&C Americas, shares his perspective on the new IT4IT industry standard and how you can use a combination of IT4IT and ServiceNow to transform how you run the business of IT.
The Path to Data and Analytics ModernizationAnalytics8
Learn about the business demands driving modernization, the benefits of doing so, and how to get started.
Can your data and analytics solutions handle today’s challenges?
To stay competitive in today’s market, companies must be able to use their data to make better decisions. However, we are living in a world flooded by data, new technologies, and demands from the business for better and more advanced analytics. Most companies do not have the modern technologies and processes in place to keep up with these growing demands. They need to modernize how they collect, analyze, use, and share their data.
In this webinar, we discuss how you can build modern data and analytics solutions that are future ready, scalable, real-time, high speed, and agile and that can enable better use of data throughout your company.
We cover:
-The business demands and industry shifts that are impacting the need to modernize
-The benefits of data and analytics modernization
-How to approach data and analytics modernization- steps you need to take and how to get it right
-The pillars of modern data management
-Tips for migrating from legacy analytics tools to modern, next-gen platforms
-Lessons learned from companies that have gone through the modernization process
Request to Fulfill Presentation (IT4IT)Rob Akershoek
The Request to Fulfill (R2F) value stream presentation. R2F is one of the four value streams of the IT4IT Reference Architecture of The Open Group.
How to manage your IT organization as a professional IT shop? Provide a self service portal for end-users and IT staff to order IT services and IT resources. Automate the entire process from request to actual deployment and provisioning.
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Customer-Centric Data Management for Better Customer ExperiencesInformatica
With consumer and business buyer expectations growing exponentially, more businesses are competing on the basis of customer experience. But executing preferred customer experiences requires data about who your customers are today and what will they likely need in the future. Every business can benefit from an AI-powered master data management platform to supply this information to line-of-business owners so they can execute great experiences at scale. This same need is true from an internal business process perspective as well. For example, many businesses require better data management practices to deliver preferred employee experiences. Informatica provides an MDM platform to solve for these examples and more.
Data Quality Management - Data Issue Management & Resolutionn / Practical App...Burak S. Arikan
One of the key stepping stones to turn the theoretical Data Governance concept to reality is the implementation of data issue management and resolution (IMR) process which includes tools, processes, governance and most importantly persistence to get to the bottom of the each data quality issue.
This presentation lays down the basic components of IMR process and tries to guide practitioners. This process was applied along with an in-house configured SharePoint management tool with workflows.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
History of IT Service Management Practices and StandardsRob Akershoek
Evolution of IT service management practices and standards from Top Gun 1 (around 1990) to Top Gun Maverick (2022)
How did the IT management evolve since 1990? When were key standards and practices introduced?
The IT management market has significantly evolved over the last few years e.g. introducing DevOps, Continuous Delivery, Agile Development, SRE and IT4IT. Managing this new multi-vendor ecosystem consisting of cloud, containers and micro-services.
Managing this new digital reality requires you to combine various practices into one integrated Digital Operating Model, to optimize end-to-end IT value streams.
Amidst an industry cloud of confusion about what “AIOps” is and what it can do, these slides--based on the webinar from EMA research--delineates a clear path to victory for business and IT stakeholders seeking to use machine learning to optimize the performance of critical business services.
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Feature Store as a Data Foundation for Machine LearningProvectus
Looking to design and build a centralized, scalable Feature Store for your Data Science & Machine Learning teams to take advantage of? Come and learn from experts of Provectus and Amazon Web Services (AWS) how to!
Feature Store is a key component of the ML stack and data infrastructure, which enables feature engineering and management. By having a Feature Store, organizations can save massive amounts of resources, innovate faster, and drive ML processes at scale. In this webinar, you will learn how to build a Feature Store with a data mesh pattern and see how to achieve consistency between real-time and training features, to improve reproducibility with time-traveling for data.
Agenda
- Modern Data Lakes & Modern ML Infrastructure
- Existing and Emerging Architectural Shifts
- Feature Store: Overview and Reference Architecture
- AWS Perspective on Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data architects & analysts, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Gandhi Raketla, Senior Solutions Architect, AWS
- German Osin, Senior Solutions Architect, Provectus
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-feature-store-as-data-foundation-for-ml-nov-2020/
This presentation will describe the analytics-to-cloud migration initiative underway at Fannie Mae. The goal of this effort is threefold: (1) build a sustainable process for data lake hydration on the cloud and (2) modernize the Fannie Mae enterprise data warehouse infrastructure and (3) retire Netezza.
Fannie Mae partnered with Impetus for modernization of its Netezza legacy analytics platform. This involved the use of the Impetus Workload Migration solution—a sophisticated translation engine that automated the migration of their complex Netezza stored procedures, shell and scheduler scripts to Apache Spark compatible scripts. This delivered substantial savings in time, effort and cost, while reducing overall project risk.
Included in the scope of the automation project was an automated assessment capability to perform detailed profiling of the current workloads. The output from the assessment stage was a data-driven offloading blueprint and roadmap for which workloads to migrate. A hybrid cloud-based big data solution was designed based on that. In addition to fulfilling the essential requirement of historical (and incremental) data migration and automated logic translation, the solution also recommends optimal storage formats for the data in the cloud, performing SCD Type 1 and Type 2 for mission-critical parameters and reloading the transformed data back for reporting/analytical consumption.
This will include the following topics:
i. Fannie Mae analytics overview
ii. Why cloud migration for analytics?
iii. Approach, major challenges, lessons learned
Speaker
Kevin Bates, Vice President for Enterprise Data Strategy Execution, Fannie Mae
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help?
Given in Montreal on 14-Dec-2021
Effective Strategy Execution with Capability-Based Planning, Enterprise Arch...Iver Band
The difficulty of strategy execution should not be underestimated
Capability-based planning helps make strategy concrete
Enterprise architecture closes the remainder of this gap, and ensures alignment and coherence
Enterprise portfolio management allows managing large enterprise landscapes based on business value
ArchiMate models tie it all together, providing a clear line of sight from strategy definition to realization
Powerful tool support makes this a strong combination!
Running the Business of IT on ServiceNow using IT4ITcccamericas
In this presentation, Michael Fulton, President of CC&C Americas, shares his perspective on the new IT4IT industry standard and how you can use a combination of IT4IT and ServiceNow to transform how you run the business of IT.
The Path to Data and Analytics ModernizationAnalytics8
Learn about the business demands driving modernization, the benefits of doing so, and how to get started.
Can your data and analytics solutions handle today’s challenges?
To stay competitive in today’s market, companies must be able to use their data to make better decisions. However, we are living in a world flooded by data, new technologies, and demands from the business for better and more advanced analytics. Most companies do not have the modern technologies and processes in place to keep up with these growing demands. They need to modernize how they collect, analyze, use, and share their data.
In this webinar, we discuss how you can build modern data and analytics solutions that are future ready, scalable, real-time, high speed, and agile and that can enable better use of data throughout your company.
We cover:
-The business demands and industry shifts that are impacting the need to modernize
-The benefits of data and analytics modernization
-How to approach data and analytics modernization- steps you need to take and how to get it right
-The pillars of modern data management
-Tips for migrating from legacy analytics tools to modern, next-gen platforms
-Lessons learned from companies that have gone through the modernization process
Request to Fulfill Presentation (IT4IT)Rob Akershoek
The Request to Fulfill (R2F) value stream presentation. R2F is one of the four value streams of the IT4IT Reference Architecture of The Open Group.
How to manage your IT organization as a professional IT shop? Provide a self service portal for end-users and IT staff to order IT services and IT resources. Automate the entire process from request to actual deployment and provisioning.
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Customer-Centric Data Management for Better Customer ExperiencesInformatica
With consumer and business buyer expectations growing exponentially, more businesses are competing on the basis of customer experience. But executing preferred customer experiences requires data about who your customers are today and what will they likely need in the future. Every business can benefit from an AI-powered master data management platform to supply this information to line-of-business owners so they can execute great experiences at scale. This same need is true from an internal business process perspective as well. For example, many businesses require better data management practices to deliver preferred employee experiences. Informatica provides an MDM platform to solve for these examples and more.
Data Quality Management - Data Issue Management & Resolutionn / Practical App...Burak S. Arikan
One of the key stepping stones to turn the theoretical Data Governance concept to reality is the implementation of data issue management and resolution (IMR) process which includes tools, processes, governance and most importantly persistence to get to the bottom of the each data quality issue.
This presentation lays down the basic components of IMR process and tries to guide practitioners. This process was applied along with an in-house configured SharePoint management tool with workflows.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
History of IT Service Management Practices and StandardsRob Akershoek
Evolution of IT service management practices and standards from Top Gun 1 (around 1990) to Top Gun Maverick (2022)
How did the IT management evolve since 1990? When were key standards and practices introduced?
The IT management market has significantly evolved over the last few years e.g. introducing DevOps, Continuous Delivery, Agile Development, SRE and IT4IT. Managing this new multi-vendor ecosystem consisting of cloud, containers and micro-services.
Managing this new digital reality requires you to combine various practices into one integrated Digital Operating Model, to optimize end-to-end IT value streams.
Amidst an industry cloud of confusion about what “AIOps” is and what it can do, these slides--based on the webinar from EMA research--delineates a clear path to victory for business and IT stakeholders seeking to use machine learning to optimize the performance of critical business services.
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Feature Store as a Data Foundation for Machine LearningProvectus
Looking to design and build a centralized, scalable Feature Store for your Data Science & Machine Learning teams to take advantage of? Come and learn from experts of Provectus and Amazon Web Services (AWS) how to!
Feature Store is a key component of the ML stack and data infrastructure, which enables feature engineering and management. By having a Feature Store, organizations can save massive amounts of resources, innovate faster, and drive ML processes at scale. In this webinar, you will learn how to build a Feature Store with a data mesh pattern and see how to achieve consistency between real-time and training features, to improve reproducibility with time-traveling for data.
Agenda
- Modern Data Lakes & Modern ML Infrastructure
- Existing and Emerging Architectural Shifts
- Feature Store: Overview and Reference Architecture
- AWS Perspective on Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data architects & analysts, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Gandhi Raketla, Senior Solutions Architect, AWS
- German Osin, Senior Solutions Architect, Provectus
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-feature-store-as-data-foundation-for-ml-nov-2020/
This presentation will describe the analytics-to-cloud migration initiative underway at Fannie Mae. The goal of this effort is threefold: (1) build a sustainable process for data lake hydration on the cloud and (2) modernize the Fannie Mae enterprise data warehouse infrastructure and (3) retire Netezza.
Fannie Mae partnered with Impetus for modernization of its Netezza legacy analytics platform. This involved the use of the Impetus Workload Migration solution—a sophisticated translation engine that automated the migration of their complex Netezza stored procedures, shell and scheduler scripts to Apache Spark compatible scripts. This delivered substantial savings in time, effort and cost, while reducing overall project risk.
Included in the scope of the automation project was an automated assessment capability to perform detailed profiling of the current workloads. The output from the assessment stage was a data-driven offloading blueprint and roadmap for which workloads to migrate. A hybrid cloud-based big data solution was designed based on that. In addition to fulfilling the essential requirement of historical (and incremental) data migration and automated logic translation, the solution also recommends optimal storage formats for the data in the cloud, performing SCD Type 1 and Type 2 for mission-critical parameters and reloading the transformed data back for reporting/analytical consumption.
This will include the following topics:
i. Fannie Mae analytics overview
ii. Why cloud migration for analytics?
iii. Approach, major challenges, lessons learned
Speaker
Kevin Bates, Vice President for Enterprise Data Strategy Execution, Fannie Mae
This presentation is for Analytic and Business Intelligence leads as well as IT leads who manage analytics. In addition, existing Oracle Business Intelligence and Analytic Customers will find it valuable to understand how they can leverage their existing investments along with Oracle Analytics Cloud.
Better Total Value of Ownership (TVO) for Complex Analytic Workflows with the...ModusOptimum
Customers are looking for ways to streamline analytic decisioning, looking for quicker deployments, faster time to value, lower risks of failure and higher revenues/profits. The IBM & Hortonworks solution delivers on these customer needs.
https://event.on24.com/eventRegistration/EventLobbyServlet?target=reg20.jsp&eventid=1789452&sessionid=1&eventid=1789452&sessionid=1&mode=preview&key=E0F94DE1191C59223B6522A075023215
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. 1
2
3
4
5
Contents
6
7
8
9
10
11
12
13
14
15
Ghadi Group Overview
Proposal Overview
High-level Architecture Diagram
Migration Approach (Roadmap)
Current System Analysis
Migration Details
Training Plan
Benefits and ROI
Risk Assessment
Testing Plan
Resources and Budget
Case Studies
What Our Customers Say
Partners
Our Team
GHADI X SHOONYA
SHOONYA Confidential
3. Ghadi group Overview
Ghadi Group, a distinguished global manufacturing entity
operating across France, the UK, Germany, the Netherlands, and
the USA, stands at the forefront of innovation in the watch
industry.
Currently, Ghadi Group's data architecture integrates Oracle EBS
ERP, Data Warehouse, Informatica ETL, and OBIEE reporting for
its global operations.
However, recognizing dynamic technology shifts, Ghadi eyes
data modernization through cloud adoption, notably with
Snowflake. Overcoming scalability issues, enhancing agility, and
integrating emerging technologies are priorities.
This move positions Ghadi to leverage the latest Generative AI/ML
use cases, transforming their data architecture into an
innovation catalyst. Embracing cloud solutions promises
increased insights and agility in data analytics and artificial
intelligence.
GHADI X SHOONYA
SHOONYA Confidential
4. Key Metrics
Warehouse size: 1 TB 30+ FACT Tables ~10M Records 400 ETL Jobs
45 DIM Tables
600 Reports in OBIEE 20+ Dashboards
Current System Analysis
Known
Challenges
1. Long-running reports in OBIEE
2. Data refresh exceeding 12
hours daily
3. Limitations in building
Generative AI Use Cases
Other Challenges
Oracle EBS
ERP
Informatica
ETL
Oracle DW
Dashboards
Reports
OBIEE
Outdated Technology Stack: Unable to meet today’s business
user’s needs, such as unlimited concurrency and performance.
1.
Limited Scalability: Challenges in scaling with growing data
volumes and increasing user loads.
2.
Integration Complexity: Integrating with newer data sources,
applications, or cloud environments require customized
solutions.
3.
Inflexibility in Data Formats: Legacy systems struggle with
challenging to adapt to the variety of data sources available
today.
4.
Limited Support for Real-time Processing: Not be well-suited
for real-time data processing and analytics, impacting the ability
to make timely business decisions.
5.
Security Vulnerabilities: Outdated security protocols and
features may expose legacy architectures to potential
cybersecurity risks and compliance issues.
6.
High Maintenance Costs: Needs specialized skills familiar with
outdated technologies.
7.
Business
Requirements
Data migration to Snowflake
1.
Data Warehouse Modernization
2.
Integration of AI Solutions
3.
GHADI X SHOONYA
SHOONYA Confidential
5. 3. Power smart manufacturing initiatives:
The Manufacturing Data Cloud
enables the ingestion and
convergence of IT and OT data—a key
requirement for smart
manufacturing. You can leverage
Snowflake’s powerful analytics
and AI/ML capabilities to generate
insights and predictions to improve
production quality and efficiency,
reduce waste and downtime, and
automate processes.
Adopting A Unified Data Management Platform
with the new
Snowflake Manufacturing Data Cloud
A single, fully managed, multi-cloud platform for data consolidation, governance, and
performance.
Build a secure, scalable data foundation:
1.
Easily incorporate both IT (Information
Technology) and OT (Operational
Technology) data from various sources,
such as ERP systems, sensors, machines,
and cloud services, into a single source
of truth
2. Boost supply chain performance:
Near real-time visibility into the
operations and performance of your
end-to-end supply chain, data to
identify potential bottlenecks and
risks, and insights to optimize
inventory and logistics
4. Collaborate with suppliers, and customers:
Improve supply chain performance,
product quality and factory efficiency
The Snowflake Manufacturing Data Cloud is designed to help you deliver improved supply chain performance and embrace Industry 4.0 with data-driven innovation and agility.
DATA ANALYTICS USE CASES
PREDICTIVE
MAINTENANCE
BIG DATA
ANALYSIS
MAXIMIZING
THROUGHPUT
SUPPLY CHAIN
OPTIMIZATION
ACCURATE
DEMAND
FORECAST
WAREHOUSE
MANAGEMENT
GHADI X SHOONYA
SHOONYA Confidential
7. Migration Approach (ROADMAP)
Discovery Phase:
Comprehensive understanding of existing
systems and challenges.
Activities:
Stakeholder interviews for insights.
In-depth analysis of current data
architecture.
Identify key pain points and
opportunities.
Data Migration Planning:
Strategize seamless transition to Snowflake.
Activities:
Assess data volume, complexity,
and dependencies.
Develop a phased migration plan.
Define data validation and quality
assurance measures.
Snowflake Implementation:
Establish a robust foundation for
modernized data warehousing.
Activities:
Deploy Snowflake architecture.
Migrate data according to the
planned phases.
Verify and validate data
integrity.
ETL Refactoring for Snowflake:
Optimize ETL processes for Snowflake
compatibility.
Activities:
Review and enhance existing
ETL workflows.
Integrate Snowflake-specific
optimizations.
Conduct rigorous testing to
ensure efficiency.
Power BI Integration:
Enhance reporting capabilities and user
experience.
Activities:
Assess existing OBIEE reports for
migration.
Modify and optimize reports using
Power BI.
Implement user training for Power BI
adoption.
AI Integration Strategy:
Incorporate AI solutions for
advanced analytics.
Activities:
Identify AI use cases
aligned with business
goals.
Assess data readiness
for AI integration.
Implement and test AI
models for forecasting
and insights.
User Training and Adoption:
Ensure seamless transition and user proficiency.
Activities:
Develop comprehensive training
materials.
Conduct user training sessions.
Provide ongoing support and resources.
Monitoring and Optimization:
Continuous improvement and
performance monitoring.
Activities:
Establish monitoring
tools for Snowflake
and AI solutions.
Conduct regular
performance reviews.
Implement
optimization
strategies as needed.
Documentation and Knowledge Transfer:
Document and transfer knowledge for long-term
sustainability.
Activities:
Create comprehensive documentation
for the new system.
Facilitate knowledge transfer sessions.
Ensure documentation is accessible for
future reference.
Post-Implementation Review:
Evaluate project success and
gather feedback.
Activities:
Conduct a thorough
review of the entire
implementation.
Collect feedback from
end-users and
stakeholders.
Identify lessons learned
and areas for further
enhancement.
1
2
3
4
5
6
7
8
9
10
GHADI X SHOONYA
SHOONYA Confidential
8. Migration Details
MIGRATE
SCHEMA
MIGRATE
DATA
BUILD DATA
PIPELINE
BUILD
METADATA
CATALOG
MIGRATE
USERS
Scalable
compute to
power data
transformation
Role-based
security
Pay-as-you-
go model
Easy schema
migration
Automated
query
optimization
STEP 1: Load initial data sets
STEP 2: Test the process end-to-end with a subset of data
STEP 3: Migrate the data and check performance
STEP 4: Run the Oracle and Snowflake Systems in parallel
STEP 5: Redirect tools to Snowflake
STEP 6: Cut over to Snowflake
STEP 7: Use Power BI’s Native Snowflake Connector for BI purposes (using Composite Model for both Fact
and DIM tables)
STEP 8: Create Data Model (STAR Schema)
STEP 9: Set up Azure AD SSO to Snowflake for data to use the security rules configured in Snowflake
GHADI X SHOONYA
SHOONYA Confidential
9. Project Phase Objective Responsibility Levels of Testing Testing Activities Number of Days Prerequisites
1. Discovery Phase
Understand current systems, identify
potential issues, and define scope.
Project Manager, Data Analysts System Testing, Acceptance Testing - Stakeholder interviews for insights. 5 Project documentation
- Analysis of current data architecture.
2. Data Migration Planning
Develop a detailed plan for a seamless
transition to Snowflake.
Data Migration Specialist Integration Testing, System Testing
- Assess data volume, complexity, and
dependencies.
10 Completed Discovery Phase
- Define data validation and quality
assurance measures.
3. Snowflake Implementation
Establish Snowflake architecture and migrate
data accordingly.
Database Administrator System Testing, Performance Testing, Security Testing - Deploy Snowflake architecture. 15 Completed Data Migration Planning
Data Migration Specialist
- Migrate data according to the planned
phases.
4. ETL Refactoring
Optimize ETL processes for compatibility
with Snowflake.
ETL Specialist Integration Testing, System Testing
- Review and enhance existing ETL
workflows.
10 Completed Snowflake Implementation
- Integrate Snowflake-specific
optimizations.
5. Power BI Integration
Modify and optimize reports for enhanced
reporting capabilities.
Reporting Specialist System Testing, User Acceptance Testing (UAT)
- Assess existing OBIEE reports for
migration.
7 Completed ETL Refactoring
- Modify and optimize reports using Power
BI.
6. AI Integration Strategy
Implement and test AI models for advanced
analytics.
AI Specialist
System Testing, Performance Testing, User
Acceptance Testing
- Identify AI use cases aligned with business
goals.
12 Completed Power BI Integration
- Assess data readiness for AI integration.
7. User Training and Adoption
Ensure users are proficient in using the new
system.
Training Specialist User Acceptance Testing (UAT) - Develop comprehensive training materials. 8 Completed AI Integration Strategy
- Conduct user training sessions.
8. Monitoring and Optimization
Continuous improvement and performance
monitoring.
System Administrator Performance Testing, Security Testing
- Establish monitoring tools for Snowflake
and AI solutions.
7 Completed User Training and Adoption
- Conduct regular performance reviews.
Testing Plan
GHADI X SHOONYA
SHOONYA Confidential
10. Training Phase Objective Training Activities Deliverables
1. Project Overview Ensure stakeholders understand the project scope and goals. - Conduct a kickoff meeting to present the project overview and objectives. Kickoff meeting presentation
- Distribute project documentation for stakeholders to review. Project documentation distributed
- Q&A session to address any initial questions or concerns. Q&A session conducted
2. Technology Training Familiarize stakeholders with the new technologies used. - Provide hands-on training sessions on Snowflake, Power BI, and AI integration. Hands-on training sessions completed
- Conduct workshops for practical application and problem-solving. Workshops conducted
3. Data Migration Training Train stakeholders on data migration processes. - Demonstrate the data migration process using Snowflake. Data migration demonstration completed
- Provide guidelines on data validation and quality assurance. Guidelines on data validation shared
- Conduct hands-on exercises for data migration practices. Hands-on exercises completed
4. Reporting and Analytics Train users on creating reports and utilizing analytics. - Conduct Power BI training sessions for report creation. Power BI training sessions completed
- Guide users on interpreting and utilizing AI-driven insights. AI insights interpretation training completed
- Provide access to training datasets for practical exercises. Access to training datasets granted
5. System Monitoring Educate stakeholders on monitoring system performance. - Explain the monitoring tools and how to interpret performance metrics. Monitoring tools explained
- Conduct training sessions on system performance reviews. Training on system performance reviews completed
6. Troubleshooting Equip stakeholders with basic troubleshooting skills. - Outline common issues and their resolutions. Troubleshooting guidelines shared
- Conduct Q&A sessions for specific concerns and issues. Q&A sessions for troubleshooting completed
7. Feedback and Improvement Encourage stakeholders to provide feedback for refinement. - Set up a feedback mechanism for continuous improvement. Feedback mechanism established
- Plan for periodic refresher training based on feedback. Refresher training plan developed
Training Plan
GHADI X SHOONYA
SHOONYA Confidential
11. Accelerated Decision-Making
Real-time insights enable
swift decision-making,
enhancing overall
business agility
Dynamic and Interactive
Reporting
Power BI implementation offers
dynamic, visually appealing
dashboards, fostering a more
engaging and insightful
reporting experience
Benefits And ROI
Cost Savings and Operational
Efficiency
Optimized ETL workflows
reduce costs and streamline
data processing, maximizing
operational efficiency
Improved Data Refresh
Timelines
Streamlined processes ensure
timely data updates, providing
up-to-the-minute information
for strategic planning
Empowered AI-Driven
Insights
Integrated AI technologies
unlock advanced analytics,
offering predictive insights for
informed decision-making
Enhanced User Productivity Future-Proofing and
Scalability
Competitive Edge and
Strategic Value
Enhanced Customer
Experience
Measurable Return on
Investment (ROI)
Faster query performance
and responsive reporting
empower users, boosting
overall productivity
Snowflake integration
provides a scalable and
future-ready architecture,
ensuring adaptability to
evolving business needs
AI integration positions
the organization at the
forefront, adding strategic
value and staying ahead in
the competitive landscape
Access to real-time
customer insights enables
personalized services,
improving overall
customer satisfaction
Reduced operational costs and
streamlined processes
Improved user productivity and
faster decision-making translate
into tangible returns.
AI-driven insights add strategic
value, providing a long-term return
on investment.
GHADI X SHOONYA
SHOONYA Confidential
12. Risk Assessment
Data Security and Privacy Concerns: Unauthorized access or data breaches during the
migration process.
1.
Mitigation: Implement robust security measures, encryption, and access controls. Conduct
thorough security audits.
Data Integrity Issues: Data corruption or loss during the migration process.
2.
Mitigation: Implement data validation checks, conduct pilot migrations, and maintain backups.
Integration Challenges: Compatibility issues between Snowflake, Power BI, and existing
systems.
3.
Mitigation: Thoroughly test integrations, involve vendor support, and have a contingency plan
for any unexpected issues.
4. ETL Refactoring Complexity: Challenges in refactoring existing ETL processes for Snowflake.
Mitigation: Conduct a detailed analysis of existing ETL workflows, involve ETL specialists, and
perform incremental refactoring.
5. User Resistance and Training Adoption: Resistance from users to adapt to new reporting tools
or AI integration.
Mitigation: Provide comprehensive training, communicate benefits clearly, and address user
concerns through change management.
6. Project Scope Creep: Expanding the project scope beyond the initial requirements.
Mitigation: Clearly define project scope, establish change control procedures, and obtain
stakeholder approvals for any scope changes.
7. Dependency on External Systems: Delays or issues arising from dependencies on external
systems or vendors.
Mitigation: Clearly define dependencies, communicate effectively with external partners, and
have contingency plans for potential delays.
8. Performance Issues in Production: Unforeseen performance bottlenecks or issues in the live
environment.
Mitigation: Conduct thorough performance testing, simulate real-world scenarios, and have
rollback plans in case of issues.
9. AI Model Accuracy and Interpretability: Challenges in achieving accurate AI model predictions or
difficulty in interpreting results.
Mitigation: Use high-quality training data, validate AI models rigorously, and involve domain
experts in interpreting results.
10. Lack of Stakeholder Involvement: Insufficient engagement and feedback from stakeholders.
Mitigation: Establish clear communication channels, conduct regular progress reviews, and
involve stakeholders in key decision-making processes.
11. Regulatory Compliance Issues: Failure to comply with data protection regulations during
migration.
Mitigation: Conduct a thorough compliance audit, ensure adherence to data protection laws, and
seek legal advice if needed.
12. Unforeseen Technical Challenges: Discovery of unexpected technical challenges during
implementation.
Mitigation: Conduct thorough technical assessments, engage with subject matter experts, and
be prepared with contingency plans.
GHADI X SHOONYA
SHOONYA Confidential
13. Resources And Budget
Project Manager
Data Migration
Specialist
Database Administrator
ETL Specialist
Reporting Specialist
AI Specialist
System Administrator
Training Specialist
Documentation
Specialist
Technical Support
Personnel
Snowflake subscription
or licensing costs
Power BI licensing
costs
Microsoft Azure
services (if applicable)
AI tools and
frameworks (e.g., Azure
Machine Learning,
TensorFlow)
Security and
monitoring tools
ETL tools (e.g.,
Informatica)
Development and
testing environments
Collaboration tools
(e.g., project
management software,
communication tools)
External training
programs for personnel
Documentation and
training material
development
Hardware for testing
and development
environments
Cloud infrastructure
costs (compute,
storage, etc.)
Reserve for unforeseen
circumstances or
additional
requirements
Personnel Costs:
$XXX,XXX
Snowflake
Subscription:
$XX,XXX
Power BI Licensing:
$XX,XXX
Azure Services:
$XX,XXX
Hardware and
Cloud Services:
$XX,XXX
Contingency
Reserve (10% of
Total Budget):
$X,XXX
External Training
Programs: $X,XXX
Documentation
Development:
$X,XXX
Software Costs:
$XXX,XXX
Personnel
Technology
Tools
And
Software
Infrastructure
Training
Contingency
GHADI X SHOONYA
SHOONYA Confidential
14. OUR TEAM
Alex Morgan
Data Migration Specialist
Jordan Taylor
Cloud Architecture Lead
Cameron Reed
ETL Optimization Expert
Riley Parker
AI Integration Strategist
15+ years executing 50+
flawless migrations.
Successfully led projects
in manufacturing,
finance, and healthcare
sectors.
Trained 200+ team
members globally.
Trusted by Fortune 500
clients.
18+ years optimizing
ETL for 15+ industry
accolades.
Scaled frameworks for
Fortune 100 giants.
Collaborated on 20+
global projects.
Trusted by leading
tech enterprises.
Pioneer with 10+ years
in AI.
Delivered patent-worthy
applications for retail
and logistics.
Aligned AI with revenue
goals, boosting profits
by 30%.
Improved models for
15+ satisfied clients.
Triple cloud-certified
with 12+ years.
Award-winning
architectures for 10+
global projects.
Optimized costs, saving
$2 million annually.
Trusted by top-tier
multinational clients.
GHADI X SHOONYA
SHOONYA Confidential
15. case studies
READ MORE... READ MORE... READ MORE...
In a transformative collaboration, Shoonya overhauled a
U.S. management consulting firm's revenue management
system. Tasked with harmonizing 1 million monthly billing
records, Shoonya utilized Microsoft Azure to centralize data
silos. Addressing decentralization challenges, incomplete
records, and manual workflows, Shoonya established a
centralized data platform, automating reporting. The result:
amplified financial reporting cycles, predictive analytics,
and standardized reporting. Shoonya’s Azure proficiency
maximized operational reporting, reducing risk, setting the
stage for a self-serving reporting platform. This success
underscores Shoonya’s prowess in transforming intricate
data landscapes, paving the way for data-driven business
excellence.
Cloud Optimization for Global Manufacturing
Powerhouse
Tasked with taming data sprawl across multiple global
sites, a Fortune 500 manufacturing giant partnered
with Shoonya for a cloud optimization overhaul.
Implementing Azure's robust suite, Shoonya seamlessly
centralized data from disparate sources, significantly
improving data accessibility and actionable insights.
The results were transformative: streamlined global
operations, a remarkable 30% reduction in operational
costs, and fortified data security. The manufacturing
powerhouse now thrives on real-time analytics, a
testament to Shoonya’s unparalleled expertise in
harnessing cloud solutions for a resilient and scalable
global expansion.
AI-Driven Customer Engagement Revolution in
E-commerce
In the competitive e-commerce arena, a leading
player collaborated with Shoonya to revolutionize
customer engagement through AI. Leveraging Azure's
advanced AI capabilities, Shoonya implemented a
dynamic personalized recommendation engine and an
efficient chatbot system. The outcome was nothing
short of remarkable: a substantial 20% surge in
customer satisfaction, a notable 15% increase in
sales conversion rates, and the seamless optimization
of customer support operations. This success story
underlines Shoonya’s exceptional skill in harnessing AI
for customer-centric solutions, elevating the e-
commerce experience to new heights.
Data Revolution: Shoonya's Azure-Led
Transformation in Management Consulting
GHADI X SHOONYA
SHOONYA Confidential
16. What Our Customers Say
The Data Engineering team has been crucial in cultivating a data-driven
culture within our organization by constructing robust pipelines,
automating workflows, and deploying advanced analytics tools. These
initiatives have fundamentally transformed our business operations. We
eagerly anticipate the exciting possibilities ahead as we continue on this
data-driven journey.
Director - Analytics
Industry: Life Sciences
The Salesforce Practice team has played a vital role in maximizing the
capabilities of Salesforce for us. Their platform expertise and skill in
tailoring solutions to our specific requirements have proven
invaluable. We appreciate their partnership and eagerly anticipate
ongoing collaboration.
Sr. Vice President
Industry: Digital Engineering Services
The invaluable contingent workforce support provided by the Shoonya team has
not only assisted us in scaling our business but has also significantly enhanced
our operational efficiency. Their proactive approach to identifying and
onboarding top-tier professionals has streamlined our talent acquisition
process, ensuring that we have the right people in place to meet the dynamic
demands of our industry.
Moreover, the collaborative synergy with Shoonya's team has not only met but
exceeded our expectations. Their commitment to understanding our unique
business needs has resulted in a tailored approach, fostering a seamless
integration of their professionals into our organizational culture.
As we look ahead, we are not just optimistic but enthusiastic about the positive
impact that the continued partnership with Shoonya's talented professionals will
have on our company's trajectory. With their support, we anticipate not only
achieving our current goals but also unlocking new opportunities for innovation
and sustained success.
Sr. Manager Talent Acquisition
Industry: Retail & E-Commerce
GHADI X SHOONYA
SHOONYA Confidential
18. Disclaimer
This record comprises confidential data and is meant solely for the
privileged utilization by SHOONYA. Every detail enclosed herein must be
treated with discretion and is prohibited from being shared with any
external entity without the explicit written approval from SHOONYA.
Unauthorized duplication will be deemed a violation of copyright.
GHADI X SHOONYA
SHOONYA Confidential
19. THANK YOU
City:
Address Line 1
Address Line 2
City:
Address Line 1
Address Line 2
https://shoonya.co
sales@shoonya.co
GHADI X SHOONYA
SHOONYA Confidential
City:
Address Line 1
Address Line 2
City:
Address Line 1
Address Line 2
