Resources.

Join us as we delve into the journey of making enterprise data truly GenAI-ready with insights from Ronen Schwartz, CEO of K2view! In this discussion, we’ll explore the future of enterprise data and how it can power the next wave of Generative AI applications. Drawing on his extensive expertise, Ronen will offer strategies for maintaining data that is always accurate, up-to-date, and meets regulatory standards—ensuring it’s fully optimized for any GenAI initiative. Key topics we’ll cover:

1️⃣ Defining enterprise data and understanding the challenges of integrating it with LLM-driven solutions like chatbots

2️⃣ How companies are currently bridging enterprise data with Generative AI

3️⃣ Critical success factors, including safeguarding security & privacy, enabling real-time responsiveness, providing immediate data access, and ensuring scalability, reliability, and cost-effectiveness

With Ronen’s deep experience from K2view, NetApp, and Informatica, this episode is packed with practical guidance for anyone looking to harness the full potential of enterprise data for GenAI. Be sure to tune in!

In the age of Generative AI and retrieval augmented generation (RAG) the spotlight is on enterprise data. The data can reside in operational systems, data lakes / data warehouses, or on a data management platform, and it is an essential piece required for GenAI app to provide much more personalized and accurate information. 

Large Language Models (LLMs) can access structured enterprise data via RAG and SQL statements. For all the undeniable advantages of using an LLM to generate SQL queries, there are also a number of issues that accompany this approach.  

 Join 2 industry leaders as they discuss:  

* What is retrieval augmented generation (RAG) and how does it work? 
* The pros and cons of generating SQL with AI  
* Considerations for ensuring your enterprise data is GenAI-Ready  
* Closing the GenAI data gap

Will GenAI take your QA job? (Spoiler Alert: It won't, but it will change it!)

In just 30 minutes, join industry experts Noa Segol (PractiTest) and Amitai Richman (K2view) as they take you on a journey into the future of AI-powered testing. You'll discover:

* The current state of AI in testing: Uncover real-world applications of AI that are transforming test management and test data management today.
* Emerging trends and technologies: Explore the latest advancements in AI for test automation, optimization, analytics, PII discovery, synthetic data generation, and more.
* A glimpse into the future: Discover what's on the horizon for AI in testing and how you can prepare your team for the changes ahead.
* Practical strategies for leveraging AI: Gain actionable advice on how to implement AI-powered solutions to improve your testing processes.

Don't miss this opportunity to separate hype from reality and equip yourself with the knowledge you need to navigate the future of AI in software testing.

Chain-of-thought prompting is an innovative technique that trains GenAI models to use step-by-step reasoning to handle complex tasks with greater accuracy and agility. 

Join us to explore chain-of-thought prompting 101 and examine its potential for enhancing enterprise LLM applications across a range of use cases.  

Join 2 industry leaders as they discuss:  
* What is chain-of-thought prompting and how does it work? 
* The benefits of chain-of-thought prompting 
* Case study: How K2view uses chain-of-thought prompting to optimize customer support

Join us for an insightful fireside chat with Jean-Luc Chantain, former Global CTO of AI at Accenture.

We'll discuss techniques and best practices for leveraging the power of enterprise data for Generative AI use cases.

Jean Luc will unveil solutions to conquer critical enterprise data challenges in unleashing the power of GenAI

Join us for an insightful fireside chat with Jean-Luc Chantain, former Global CTO of AI at Accenture.

We'll discuss techniques and best practices for leveraging the power of enterprise data for Generative AI use cases.

Jean Luc will unveil solutions to conquer critical enterprise data challenges in unleashing the power of GenAI

Join Daniel Howard, Sr. Analyst @ Bloor Research, and Amitai Richman, Dir. Product Marketing @ K2view, as they dispel persistent TDM myths in an eye-opening webinar, "Test Data Management: Separating Fact from Fiction."
We'll cut through the noise and equip you with actionable knowledge to overcome test data challenges like lengthy and error-prone test data provisioning, low test coverage, and compliance risks.

Watch K2view CEO, Ronen Schwartz and Hod Rotem. Chief Product Evangelist explain how GenAI can help your business in real time at the Gartner D&A Summit 2024 in Orlando

Test data management (TDM) is a crucial aspect of software development and testing, but it also poses many challenges and complexities. Join us to learn how to overcome these challenges and optimize your TDM processes.

Join us as we delve into synthetic data generation methods, focusing on the 4 leading data generation techniques, including generative AI and rule-based data generation. 

Don't miss this opportunity to expand your knowledge of synthetic data generation, learn when to use each method and learn about the life cycle of synthetic data.

Join us for a deep dive into the world of synthetic data generation: uncovering its powerful techniques, versatile use cases, and the myriad of benefits it brings to businesses. Synthetic data generation has emerged as a game-changing solution for organizations seeking to maximize the potential of their data without compromising privacy or security.

Join us to learn about the transformative power of Test Data Management (TDM) in driving cost savings, enhancing productivity, and accelerating software delivery for your organization. 

We’ll share a proven methodology for quantifying the business benefits of TDM, and an ROI model to use in building your financial justification for a TDM tool.

We’ll delve into the concrete benefits of DevOps TDM and provide practical approaches to achieve these outcomes. Real-world case studies and best practices will be shared to illustrate the tangible impact of TDM on eliminating test data bottlenecks and accelerating the testing and release cycles.

Despite the adoption of Lean-Agile thinking, modern engineering, and DevOps practices - test data management has lagged, leading to bottlenecks in productivity and reduced test coverage.

Are you struggling to manage your test data efficiently and effectively? Are you looking for new ways to improve your software testing processes while ensuring data privacy compliance?
 
If so, you won't want to miss our webinar:
Test Data Management: Leading Approaches, Best Practices, and Strategies.

Learn how Vodafone Germany has successfully implemented a Customer Data Hub (CDH), which unifies customer interactions, transactions, and master data, across dozens of data sources, to serve both operational and analytical workloads.

Learn how Cellcom, a leading telco, took a data product approach to implement a real-time Customer Data Hub (CDH), with bi-directional integration to Salesforce CRM, in just 3 months.

Watch this interactive session to learn how to build, configure, and expose RESTful web services in minutes – using Graphit, our no-code/low-code, visual web-service designer.

Introduce a transformational approach to complete your current Customer 360 initiative in weeks, using a data product approach, and while leveraging your existing investments.

Focus on evaluating test data management strategy and tech stack for greater efficiency in 2022 and beyond. 

Learn how data mesh, data fabric, and data hub architectures are transforming data management. 

Learn about the critical components of a modern TDM strategy, and how they align with today's TDM trends.

Vodafone implemented K2View Fabric for Customer Data Hub and Test Data Management, to deliver strategic business outcomes across its operations.

Learn best practices for data masking of structured and unstructured data.

Learn why data fabric has emerged as the leading cloud data integration architecture for the enterprise, and the common challenges it resolves.

Learn about a revolutionary, data product approach to data tokenization. We’ll cover data tokenization concepts and methods, the business requirements of a data tokenization solution, and the limitations of traditional data tokenization tools. We’ll introduce a transformational approach to data tokenization that leverages data products to enable high-scale, real-time protection of sensitive data for operational and analytical systems.

We discussed the emerging approach to data management, founded on data products.

We discussed how the K2View Data Product Platform is used in real-life to unify globally dispersed data, while federating data ownership and fulfilling data residency and data privacy requirements. Live demo: Creating and delivering data products in a Data Mesh

K2View Fabric brings a business-centric, data product approach to data management, that delivers a 360-degree view of key data in real time. Learn more.

Enterprises strive to make every customer experience personal, contextual, consistent, and profitable. K2View Fabric does just that, with a 360 customer view enriched with operational insights.

Download this whitepaper to see the performance metrics for K2View Data Fabric under two test cases.

RAG is a framework for improving the accuracy of large language models using relevant data from internal sources.

Learn how LLM agents use LLMs to perform tasks, make decisions, and interact with users autonomously.  

An AI hallucination is an AI-generated output that’s factually incorrect, nonsensical, or inconsistent, due to bad training data or misidentified patterns.

Entity-based test data management tools give DevOps teams the ability to quickly generate relevant and accurate test data to accelerate software delivery.

Test data is a compliant dataset used by development and QA teams to ensure that
software applications perform as expected while maximizing test coverage. 

Data masking techniques and best practices ensure your sensitive data can’t be identified or reverse-engineered, while retaining relational integrity and usability.

Data anonymization obscures Personally Identifiable Information (PII) in datasets, to comply with privacy laws and allow for use in research and analysis.

Synthetic data generation is used to protect personal data, test applications in development, train machine learning models, and validate systems at scale.

Data tokenization substitutes sensitive data with a non-sensitive equivalent, or token, that maps back to the original data via a tokenization system.

Customer 360 is a trusted, fresh, integrated dataset, containing all the information about a particular customer, accessible to authorized data consumers.

A Customer Data Platform/Hub provisions transactional and interactional data to any consuming application in real time with full governance and consent.

Data migration is the process of transferring data between data formats, data stores, or computing environments, based on business or technology needs.

Legacy application modernization is a continuous, pragmatic approach to digital transformation, based on a data mesh architecture and operational model. 

A data product is a reusable data asset corresponding to a business entity (customer, order, device, etc.) engineered with a particular purpose in mind.

Data fabric is a centralized architecture that delivers integrated, enriched data – in real time, and at scale – for analytical and operational use cases.

Data mesh, a decentralized data architecture, relies on 4 principles: Data products, domain ownership, instant access, and federated governance.

Cloud data integration standardizes the integration of applications and data,
and automates business processes and data sharing across applications.

Data integration tools unify data from different sources into a single view, improving operational efficiency and data quality, for more valuable insights.

Learn how entity-based ETL (eETL) overcomes the limitations of traditional ETL and ELT, to deliver clean, ready-to-use data to your business analysts.

A data pipeline transforms and enriches data as it moves from source to target stores, so that it can be analyzed, and used to generate business insights.

Data governance refers to the policies, processes, and strategies put in place to assure that data is accessible, compliant, precise, reliable, and secure.

Data virtualization is achieved through a semantic data layer that integrates data
across multiple systems, while allowing for centralized data governance.

Data fabric is the ideal architecture for managing enterprise data at scale. Read this market guide for an overview of the top data fabric vendors.

Data mesh is the go-to architecture for distributed data management platforms. Read this guide before selecting a data mesh vendor for your enterprise.

"We’re on a journey to modernize our apps and to realize the benefits of embracing a DevOps methodology. But while implementing a DevOps methodology and moving to agile is great in theory, you hit a roadblock if you don’t have realistic data to test against. K2View Test Data Management provides a self-service approach for our teams to provision test data on demand – without impacting production source systems."
VP of Network Services and Shared Platform, AT&T

TDM case study on 1 of the 10 largest banks in the US showing a 40% increase in productivity and an 80% decrease in staff, both for test data provisioning.

"To continue providing excellent customer care with the merger, we needed near real-time data for eligibility checks, benefit provisioning for converged customers with both fixed and mobile subscriptions. We’ve gone from 24+ hours for data updates to having data available in simply minutes or even seconds, boosting the customer experience and better supporting our customer care agents. We’re happy with how it is performing."

Marc Schmeetz, Program Manager, VodafoneZiggo

K2View Data Integration tools enable a Fortune 1000 financial services enterprise to integrate multiple systems, while storing data in individually encrypted micro-databases. Learn how.

Learn how a major telco used K2View Fabric to modernize its legacy applications, reorganize its data using a data product/micro-database approach, and improve performance – while saving millions.

Understand the story behind K2View technology and how our unique data-centric approach is changing the way organizations manage and access data.

Learn how a North American wireless company, after acquiring 2 of its competitors in a $5B deal, used K2View Fabric to integrate the data of 15 million users into a single system in less than 3 weeks.

"We needed to integrate and align customer data from across three distinct and different companies into a single customer data hub to power our Salesforce CRM. With its unique approach to data management and rich feature set, K2View Customer Data Hub simplified the complexity of integrating and unifying the data from our disparate systems, allowing us to deliver world-class customer experiences."

Ronen Horowitz SVP & CIO, Pelephone, Yes, and Bezeq International

"Victor Malca Our work with K2View has enabled Cellcom to meet and exceed the expectations of our customers. We’re happy with what we have been able to accomplish with K2View at the core of our systems, and its ability to provide real-time information to all of our different data consumers. I am confident in our partnership and look forward to further innovations with K2View."

VP & CIO, Cellcom

VodafoneZiggo wanted to be able to access fresh
customer data from across its new enterprise, and deliver it in less than a second – with no down time to the business (e.g., customer support center, selfservice portal, mobile app, and more). To do so, it selected the K2View Data Product Platform, and was able to implement a Customer Data Hub (CDH)
in just a few weeks.

Learn how data mesh, data fabric, and data hub architectures are transforming data management. 

"We’re on a journey to modernize our apps and to realize the benefits of embracing a DevOps methodology. But while implementing a DevOps methodology and moving to agile is great in theory, you hit a roadblock if you don’t have realistic data to test against. K2View Test Data Management provides a self-service approach for our teams to provision test data on demand – without impacting production source systems."
VP of Network Services and Shared Platform, AT&T

TDM case study on 1 of the 10 largest banks in the US showing a 40% increase in productivity and an 80% decrease in staff, both for test data provisioning.

"To continue providing excellent customer care with the merger, we needed near real-time data for eligibility checks, benefit provisioning for converged customers with both fixed and mobile subscriptions. We’ve gone from 24+ hours for data updates to having data available in simply minutes or even seconds, boosting the customer experience and better supporting our customer care agents. We’re happy with how it is performing."

Marc Schmeetz, Program Manager, VodafoneZiggo

K2View Data Integration tools enable a Fortune 1000 financial services enterprise to integrate multiple systems, while storing data in individually encrypted micro-databases. Learn how.

Learn how a major telco used K2View Fabric to modernize its legacy applications, reorganize its data using a data product/micro-database approach, and improve performance – while saving millions.

Understand the story behind K2View technology and how our unique data-centric approach is changing the way organizations manage and access data.

Learn how a North American wireless company, after acquiring 2 of its competitors in a $5B deal, used K2View Fabric to integrate the data of 15 million users into a single system in less than 3 weeks.

"We needed to integrate and align customer data from across three distinct and different companies into a single customer data hub to power our Salesforce CRM. With its unique approach to data management and rich feature set, K2View Customer Data Hub simplified the complexity of integrating and unifying the data from our disparate systems, allowing us to deliver world-class customer experiences."

Ronen Horowitz SVP & CIO, Pelephone, Yes, and Bezeq International

"Victor Malca Our work with K2View has enabled Cellcom to meet and exceed the expectations of our customers. We’re happy with what we have been able to accomplish with K2View at the core of our systems, and its ability to provide real-time information to all of our different data consumers. I am confident in our partnership and look forward to further innovations with K2View."

VP & CIO, Cellcom

VodafoneZiggo wanted to be able to access fresh
customer data from across its new enterprise, and deliver it in less than a second – with no down time to the business (e.g., customer support center, selfservice portal, mobile app, and more). To do so, it selected the K2View Data Product Platform, and was able to implement a Customer Data Hub (CDH)
in just a few weeks.

Learn how data mesh, data fabric, and data hub architectures are transforming data management. 

RAG is a framework for improving the accuracy of large language models using relevant data from internal sources.

Learn how LLM agents use LLMs to perform tasks, make decisions, and interact with users autonomously.  

An AI hallucination is an AI-generated output that’s factually incorrect, nonsensical, or inconsistent, due to bad training data or misidentified patterns.

Entity-based test data management tools give DevOps teams the ability to quickly generate relevant and accurate test data to accelerate software delivery.

Test data is a compliant dataset used by development and QA teams to ensure that
software applications perform as expected while maximizing test coverage. 

Data masking techniques and best practices ensure your sensitive data can’t be identified or reverse-engineered, while retaining relational integrity and usability.

Data anonymization obscures Personally Identifiable Information (PII) in datasets, to comply with privacy laws and allow for use in research and analysis.

Synthetic data generation is used to protect personal data, test applications in development, train machine learning models, and validate systems at scale.

Data tokenization substitutes sensitive data with a non-sensitive equivalent, or token, that maps back to the original data via a tokenization system.

Customer 360 is a trusted, fresh, integrated dataset, containing all the information about a particular customer, accessible to authorized data consumers.

A Customer Data Platform/Hub provisions transactional and interactional data to any consuming application in real time with full governance and consent.

Data migration is the process of transferring data between data formats, data stores, or computing environments, based on business or technology needs.

Legacy application modernization is a continuous, pragmatic approach to digital transformation, based on a data mesh architecture and operational model. 

A data product is a reusable data asset corresponding to a business entity (customer, order, device, etc.) engineered with a particular purpose in mind.

Data fabric is a centralized architecture that delivers integrated, enriched data – in real time, and at scale – for analytical and operational use cases.

Data mesh, a decentralized data architecture, relies on 4 principles: Data products, domain ownership, instant access, and federated governance.

Cloud data integration standardizes the integration of applications and data,
and automates business processes and data sharing across applications.

Data integration tools unify data from different sources into a single view, improving operational efficiency and data quality, for more valuable insights.

Learn how entity-based ETL (eETL) overcomes the limitations of traditional ETL and ELT, to deliver clean, ready-to-use data to your business analysts.

A data pipeline transforms and enriches data as it moves from source to target stores, so that it can be analyzed, and used to generate business insights.

Data governance refers to the policies, processes, and strategies put in place to assure that data is accessible, compliant, precise, reliable, and secure.

Data virtualization is achieved through a semantic data layer that integrates data
across multiple systems, while allowing for centralized data governance.

Data fabric is the ideal architecture for managing enterprise data at scale. Read this market guide for an overview of the top data fabric vendors.

Data mesh is the go-to architecture for distributed data management platforms. Read this guide before selecting a data mesh vendor for your enterprise.

K2View Fabric brings a business-centric, data product approach to data management, that delivers a 360-degree view of key data in real time. Learn more.

Enterprises strive to make every customer experience personal, contextual, consistent, and profitable. K2View Fabric does just that, with a 360 customer view enriched with operational insights.

Download this whitepaper to see the performance metrics for K2View Data Fabric under two test cases.

Entity-based test data management tools give DevOps teams the ability to generate relevant and accurate test data – easily, quickly, and securely – to enhance business agility.

Learn how data mesh, data fabric, and data hub architectures are transforming data management. 

This paper provides a business framework that organizations can use to justify an investment in a test data management solution. It examines the benefits and costs of implementing the K2view solution for test data
management (K2tdm), and quantifies the expected ROI in 4 dimensions.

This paper explores today’s data masking techniques, the problems they pose for enterprises, and a novel approach that answers these challenges comprehensively.

Learn how data mesh, data fabric, and data hub architectures are transforming data management. 

Learn how data mesh, data fabric, and data hub architectures are transforming data management. 

Protecting sensitive data is a top priority for the enterprise. Tokenization is one of the best data protection methods – and a data product approach makes it even better.

This paper covers the market drivers and benefits of Customer 360, the shortcomings of traditional solutions, and a unique approach to achieving the 360-degree customer view.

This paper outlines Customer Data Hub, the new, single, real-time source of customer data for CRM, campaign management, data privacy, customer analytics, and more.

Learn all the aspects of data migration including the market need, migration strategies, considerations, and a new entity-based approach to data migration that covers all the bases.

This paper investigates the needs, methods and drivers of application modernization, and a new, continuous approach, based on a data mesh architecture and operational model.

Data products are reusable data assets that democratize trusted data access across the enterprise. This practical guide discusses data products and product-driven data management: what data products do, where they help, who they’re for, and how to get started.

Data Product Platform allows enterprises to organize their data based on data products. Its patented Micro-Database™ technology turns fragmented data into an accessible commodity.

This paper addresses the what, why, how, and who of data fabric, including data fabric architecture, challenges, benefits, core capabilities, vendors, and more.

This paper discusses the market need for data mesh architecture, as well as its core capabilities, objectives, challenges, use cases, and technology platforms

iPaaS coud data integration standardizes the integration of applications and data, allowing for the automation of business processes and the sharing of data across applications.

Learn how entity-based ETL (eETL) overcomes the limitations of traditional ETL and ELT, to deliver clean, ready-to-use data to your business analysts.

A data pipeline transforms and enriches data as it moves from source to target
stores, so that it can be analyzed, and used to generate business insights.

Data governance refers to the policies, processes, and strategies put in place
to assure that data is accessible, compliant, precise, reliable, and secure.

This article defines data virtualization, goes on to discuss its drivers, challenges, benefits, use cases, and limitations, and concludes with a novel data product approach that promises to make the technology more dynamic and robust than ever before.

Data is often buried in legacy systems, locked in vendor applications, and scattered in different formats and technologies. Liberate your data with K2View, and propel your business forward.

Join us as we delve into the journey of making enterprise data truly GenAI-ready with insights from Ronen Schwartz, CEO of K2view! In this discussion, we’ll explore the future of enterprise data and how it can power the next wave of Generative AI applications. Drawing on his extensive expertise, Ronen will offer strategies for maintaining data that is always accurate, up-to-date, and meets regulatory standards—ensuring it’s fully optimized for any GenAI initiative. Key topics we’ll cover:

1️⃣ Defining enterprise data and understanding the challenges of integrating it with LLM-driven solutions like chatbots

2️⃣ How companies are currently bridging enterprise data with Generative AI

3️⃣ Critical success factors, including safeguarding security & privacy, enabling real-time responsiveness, providing immediate data access, and ensuring scalability, reliability, and cost-effectiveness

With Ronen’s deep experience from K2view, NetApp, and Informatica, this episode is packed with practical guidance for anyone looking to harness the full potential of enterprise data for GenAI. Be sure to tune in!

In the age of Generative AI and retrieval augmented generation (RAG) the spotlight is on enterprise data. The data can reside in operational systems, data lakes / data warehouses, or on a data management platform, and it is an essential piece required for GenAI app to provide much more personalized and accurate information. 

Large Language Models (LLMs) can access structured enterprise data via RAG and SQL statements. For all the undeniable advantages of using an LLM to generate SQL queries, there are also a number of issues that accompany this approach.  

 Join 2 industry leaders as they discuss:  

* What is retrieval augmented generation (RAG) and how does it work? 
* The pros and cons of generating SQL with AI  
* Considerations for ensuring your enterprise data is GenAI-Ready  
* Closing the GenAI data gap

Will GenAI take your QA job? (Spoiler Alert: It won't, but it will change it!)

In just 30 minutes, join industry experts Noa Segol (PractiTest) and Amitai Richman (K2view) as they take you on a journey into the future of AI-powered testing. You'll discover:

* The current state of AI in testing: Uncover real-world applications of AI that are transforming test management and test data management today.
* Emerging trends and technologies: Explore the latest advancements in AI for test automation, optimization, analytics, PII discovery, synthetic data generation, and more.
* A glimpse into the future: Discover what's on the horizon for AI in testing and how you can prepare your team for the changes ahead.
* Practical strategies for leveraging AI: Gain actionable advice on how to implement AI-powered solutions to improve your testing processes.

Don't miss this opportunity to separate hype from reality and equip yourself with the knowledge you need to navigate the future of AI in software testing.

Chain-of-thought prompting is an innovative technique that trains GenAI models to use step-by-step reasoning to handle complex tasks with greater accuracy and agility. 

Join us to explore chain-of-thought prompting 101 and examine its potential for enhancing enterprise LLM applications across a range of use cases.  

Join 2 industry leaders as they discuss:  
* What is chain-of-thought prompting and how does it work? 
* The benefits of chain-of-thought prompting 
* Case study: How K2view uses chain-of-thought prompting to optimize customer support

Join us for an insightful fireside chat with Jean-Luc Chantain, former Global CTO of AI at Accenture.

We'll discuss techniques and best practices for leveraging the power of enterprise data for Generative AI use cases.

Jean Luc will unveil solutions to conquer critical enterprise data challenges in unleashing the power of GenAI

Join us for an insightful fireside chat with Jean-Luc Chantain, former Global CTO of AI at Accenture.

We'll discuss techniques and best practices for leveraging the power of enterprise data for Generative AI use cases.

Jean Luc will unveil solutions to conquer critical enterprise data challenges in unleashing the power of GenAI

Join Daniel Howard, Sr. Analyst @ Bloor Research, and Amitai Richman, Dir. Product Marketing @ K2view, as they dispel persistent TDM myths in an eye-opening webinar, "Test Data Management: Separating Fact from Fiction."
We'll cut through the noise and equip you with actionable knowledge to overcome test data challenges like lengthy and error-prone test data provisioning, low test coverage, and compliance risks.

Watch K2view CEO, Ronen Schwartz and Hod Rotem. Chief Product Evangelist explain how GenAI can help your business in real time at the Gartner D&A Summit 2024 in Orlando

Test data management (TDM) is a crucial aspect of software development and testing, but it also poses many challenges and complexities. Join us to learn how to overcome these challenges and optimize your TDM processes.

Join us as we delve into synthetic data generation methods, focusing on the 4 leading data generation techniques, including generative AI and rule-based data generation. 

Don't miss this opportunity to expand your knowledge of synthetic data generation, learn when to use each method and learn about the life cycle of synthetic data.

Join us for a deep dive into the world of synthetic data generation: uncovering its powerful techniques, versatile use cases, and the myriad of benefits it brings to businesses. Synthetic data generation has emerged as a game-changing solution for organizations seeking to maximize the potential of their data without compromising privacy or security.

Join us to learn about the transformative power of Test Data Management (TDM) in driving cost savings, enhancing productivity, and accelerating software delivery for your organization. 

We’ll share a proven methodology for quantifying the business benefits of TDM, and an ROI model to use in building your financial justification for a TDM tool.

We’ll delve into the concrete benefits of DevOps TDM and provide practical approaches to achieve these outcomes. Real-world case studies and best practices will be shared to illustrate the tangible impact of TDM on eliminating test data bottlenecks and accelerating the testing and release cycles.

Despite the adoption of Lean-Agile thinking, modern engineering, and DevOps practices - test data management has lagged, leading to bottlenecks in productivity and reduced test coverage.

Are you struggling to manage your test data efficiently and effectively? Are you looking for new ways to improve your software testing processes while ensuring data privacy compliance?
 
If so, you won't want to miss our webinar:
Test Data Management: Leading Approaches, Best Practices, and Strategies.

Learn how Vodafone Germany has successfully implemented a Customer Data Hub (CDH), which unifies customer interactions, transactions, and master data, across dozens of data sources, to serve both operational and analytical workloads.

Learn how Cellcom, a leading telco, took a data product approach to implement a real-time Customer Data Hub (CDH), with bi-directional integration to Salesforce CRM, in just 3 months.

Watch this interactive session to learn how to build, configure, and expose RESTful web services in minutes – using Graphit, our no-code/low-code, visual web-service designer.

Introduce a transformational approach to complete your current Customer 360 initiative in weeks, using a data product approach, and while leveraging your existing investments.

Focus on evaluating test data management strategy and tech stack for greater efficiency in 2022 and beyond. 

Learn how data mesh, data fabric, and data hub architectures are transforming data management. 

Learn about the critical components of a modern TDM strategy, and how they align with today's TDM trends.

Vodafone implemented K2View Fabric for Customer Data Hub and Test Data Management, to deliver strategic business outcomes across its operations.

Learn best practices for data masking of structured and unstructured data.

Learn why data fabric has emerged as the leading cloud data integration architecture for the enterprise, and the common challenges it resolves.

Learn about a revolutionary, data product approach to data tokenization. We’ll cover data tokenization concepts and methods, the business requirements of a data tokenization solution, and the limitations of traditional data tokenization tools. We’ll introduce a transformational approach to data tokenization that leverages data products to enable high-scale, real-time protection of sensitive data for operational and analytical systems.

We discussed the emerging approach to data management, founded on data products.

We discussed how the K2View Data Product Platform is used in real-life to unify globally dispersed data, while federating data ownership and fulfilling data residency and data privacy requirements. Live demo: Creating and delivering data products in a Data Mesh