Name: Mastering Synthetic Data Generation: Methods, Pitfalls and Best Practices
Start: 2023-11-16T15:00:00Z
End: 2023-11-16T15:00:59.000Z
Location: BrightTALK
Rating: 4.4

At K2View, we believe that every company should be able to liberate and elevate its data to deliver the most personalized and profitable customer experience in its industry, while being innovative and radically agile. 

With K2View, companies manage data in a whole new way, using a business lens: they create data products that continually sync, transform, and serve data from siloed source systems – delivering a real-time, holistic view of any business entity to any data consumer.

Our Data Product Platform fuels operational and analytical workloads, at enterprise scale. It deploys as a data fabric, data mesh, or data hub - in an on-premises, cloud, or a hybrid architecture - in a matter of weeks, and adapts to change on the fly.

The most data-intensive companies in the world, including AT&T, Verizon, American Express, Vodafone, and Hertz trust K2View Data Product Platform for their operational use cases - spanning Customer 360, cloud migration, test data management, data tokenization, and legacy application modernization.

Discover how enterprises can turn mainframe and other mission-critical system data into trusted, compliant datasets for testing, analytics, and AI. Modernization often slows down because teams struggle with slow data access, manual provisioning, and growing compliance demands. This webinar shows how the joint Rocket + K2view solution removes these bottlenecks with one unified approach.

You’ll learn how to access governed cross-system data on demand while preserving referential integrity across mainframe and distributed environments. A live demo will walk through accessing z/OS data with Rocket Data Virtualization, detecting and masking PII, provisioning production-like test data, and generating synthetic datasets for edge-case coverage. The session ends with a practical “how to start” guide, including recommended use cases, success metrics, deployment considerations, and a downloadable datasheet.

From mainframe to modern: compliant data for testing, analytics, and AI

As enterprises move from GenAI pilots to production, the challenge shifts from experimentation to scalability, governance, and trust. MCP (Model Context Protocol) is paving the way for standardized data access, but success depends on having real-time, governed data products behind it. 

In this session, K2view experts will share how leading organizations are preparing for 2026 by combining MCP and data products to build a future-ready data foundation that scales with their AI ambitions. 

Join us to learn: 

* Why data products are essential for MCP-driven AI ecosystems 

* How to create a governed, semantic data layer that feeds LLMs and AI agents 

* Steps enterprises can take now to be ready for 2026

2026 readiness: How MCP and data products future-proof enterprise AI

The Model Context Protocol (MCP) is fast becoming the interoperability layer that lets GenAI application connect to enterprise data. But wiring MCP clients directly to dozens or even hundreds of enterprise source systems doesn’t scale. Each connection adds complexity, risk, and maintenance overhead. Without a unified, governed, real-time semantic data layer, MCP-enabled workflows quickly become fragile and unsustainable in enterprise settings. 

In this webinar, we’ll demystify MCP for enterprise teams, show how it standardizes data access for LLMs, and demonstrate how K2view MCP server unifies access to multiple enterprise systems.  

 Join 2 industry leaders as they discuss: 

* What is MCP and why it matters now 
A clear overview of how MCP connects GenAI apps to data, and why is it becoming the de-facto standard for enterprises. 

* The enterprise challenge of MCP adoption 
Wiring MCP clients directly to hundreds of source systems creates complexity, security gaps, and governance risks – we’ll unpack these challenges and share how to overcome them. 

* Live demo: K2view MCP server in action 
See how K2view data products can be deployed as an MCP server, turning siloed enterprise data into a governed, real-time semantic layer that MCP clients can seamlessly invoke through resources, prompt templates, and tools.

Beyond the hype: Making MCP work in the enterprise

Most Test Data Management (TDM) tools promise to solve the test data bottleneck, but many fall short. The market is crowded with complex tools and confusing claims about AI, data virtualization, and synthetic data, making it harder than ever to know what really works.

This webinar cuts through the noise. Join Daniel Howard, Senior Analyst at Bloor Research, and Amitai Richman, Director of Product Marketing at K2view, for an honest, hype-free look at the TDM landscape. They'll help you separate fact from fiction and learn what it actually takes to deliver fast, compliant, and reliable test data. We'll cover:

• What works: The essential capabilities every effective TDM tool must have.
• What doesn’t: A candid comparison of common technological approaches and their hidden limitations.
• What’s next: How AI and synthetic data are starting to change the game.

Whether you’re choosing your first TDM tool, reevaluating an existing solution, or just keeping your pulse on the market, this session will bring order to the cacophony of TDM noise.

The truth about Test Data Management tools: What works, what doesn’t, and what’s next

Are you struggling to keep sensitive data out of your test environments? You're not alone. Our new 2025 State of Test Data Management Report reveals a startling reality: only 7% of companies are fully compliant with data privacy regulations in their test data processes.

Join K2view's Director of Product Marketing, Amitai Richman, and Simon McVeigh, VP Global Technologies for a live 45-minute webinar, "Test Data Management in 2025: From Legacy Tools to Synthetic Data," as they delve into the critical findings of this report and explore the future of test data management.

In this webinar, you'll discover:

* The compliance gap: Uncover the reasons behind the widespread struggle to keep PII out of test environments and learn actionable strategies to achieve compliance.
* The legacy tool trap: Discover how outdated TDM tools are hindering your progress and explore the benefits of modern approaches, including automation and advanced data masking.
* The C-Suite disconnect: Understand the differing perceptions of TDM effectiveness between executives and operational teams, and how to bridge the communication gap.
* The rise of synthetic data: Explore the growing adoption of synthetic data and its top use cases, including generating realistic data for edge case testing and development.
* Strategies for modernizing your TDM: Understand the different technological approaches to TDM and how to transition from legacy tools to a more agile, compliant, and efficient approach.

Test Data Management in 2025: From Legacy Tools to Synthetic Data

Ready to revolutionize test data management in your enterprise?
 
Join us for an exclusive webinar on TDM best practices and live demo, "AI-Driven Test Data Management: Live Demo".

Our experts, Simon McVeigh, Global VP, Technology, and Amitai Richman, Director of Product Marketing, will show you how K2tdm’s intuitive platform empowers your QA team to seamlessly find, reserve, and provision test data, accelerating your testing cycles without any roadblocks. No masked production data available? No problem! We’ll show you how testers can generate synthetic data for any test scenario.

See for yourself how to:
• Effortlessly subset test data: Navigate through our intuitive web portal designed for testers and developers.
• Generate realistic synthetic data using AI/ML or business rules: Create safe, compliant datasets for application testing, with complexity mirroring your own production databases.
• Master sensitive data masking: Implement robust masking techniques to automatically discover and mask PII with realistic lookalike data to protect your sensitive info and ensure compliance.

AI-Driven Test Data Management: Live Demo

Agentic AI frameworks are among the top technology trends for 2025 that holds a promise to transform both personal and professional landscapes in 2025 and beyond. 

In this webinar, we’ll delve into the foundations of agentic AI frameworks, unpack their potential to revolutionize operations, and highlight the pivotal role of enterprise data in ensuring their effectiveness. Don’t miss this opportunity to gain valuable insights and prepare your organization for the future of AI-driven innovation.

Join 2 industry leaders as they discuss: 
• What is Agentic AI and why should you care?
• Why enterprise data is key to success 
• The emerging use case - data integration into LLM agents

The Essential Truth About Agentic AI:  Why Enterprise Data Is Key to Success

Join us for a deep dive into the world of tabular synthetic data generation for software testing: 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. 

We'll cover: 
* Gartner’s research on synthetic data generation 
* Top drivers and use cases 
* Synthetic data generation technical approaches: Pros and cons of each 
* Masked production data vs. synthetic data - which is better?
* Best practices and tools

Don't miss this opportunity to explore the potential of  synthetic data for your organization.
Visit our LinkedIn page and follow us at https://il.linkedin.com/company/k2view

Unlocking the Power of Synthetic Data Generation: Methods, Use Cases & Insights

Are you ready to discover the biggest hurdles preventing organizations from harnessing the power of Generative AI (GenAI)? 

Our latest market survey reveals that the most significant roadblocks to realizing GenAI’s full potential lie in organizations’ existing data infrastructure, particularly in the areas of data accessibility and latency, data privacy, and security.

Join 2 industry leaders as they discuss:  

* The state of GenAI adoption 
* Why enterprise data is at the crux of the biggest challenges for AI deployment 
* How to address the GenAI data readiness gap

The State of GenAI Data Readiness

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

RAGs to Riches? The Realities of RAG and text-to-SQL

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.

Register now! Even if you can't attend live, register and we'll send you the recording so you can watch it at your convenience.

The Future of AI in Software Testing: What's Hype, What's Real, and What's Next?

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

Enhance Your Enterprise LLMs with Chain-of-Thought Prompting

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, including:
* Data governance and security 
* Metadata and data prep
* Prompt engineering and retrieval augmented generation (RAG)

Don't miss this opportunity to learn from one of the world's top AI experts, and ask your specific questions about data readiness for GenAI.

Ask The Expert: Is Your Data GenAI-Ready?

Ready to separate fact from fiction in Test Data Management? 

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.
• Uncover the hidden bottlenecks that sabotage your release cycles
• Unlock the ROI of modern TDM with our calculation guide
• Uncover the truth about 'self-service' TDM – and how to truly slash test data setup time by 70%
• Discover if synthetic data can replace traditional TDM
Plus, get a sneak peek at the future of TDM and the trends shaping the industry.

Don’t miss this opportunity - register now to secure your spot.
Even if you can’t attend live, we’ll send you the recording afterward.

Test Data Management: Separating Fact from Fiction

Test data management (TDM) is a crucial aspect of software development and testing, but it also poses many challenges and complexities. If you want to learn how to overcome these challenges and optimize your TDM processes, you should join our upcoming webinar to learn about:  

* The current state of TDM, including the latest trends and challenges  
* Advanced features and capabilities to look for in a TDM solution 
* Best practices and strategies, including TDM processes and org structure 

Take advantage of this opportunity to learn from the TDM experts and get your questions answered. Register now to secure your spot for this informative and interactive webinar. We'll send you the recording if you can’t attend live.

Master Class: Test Data Management for Complex Data Environments

About this session:

We're excited to invite you to attend the 2nd in our synthetic data webinar series.

We'll 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.


Agenda:
* The synthetic data lifecycle, and synthetic data management
* Synthetic data generation techniques, and pros and cons of each
* Product demo covering generative AI, rule-based, and data masking techniques
* Q&A

Mastering Synthetic Data Generation: Methods, Pitfalls and Best Practices

Test Data Management

Machine Learning

Data Analytics

Compliance

Data Privacy

Software Testing

ML Models

AI Models

synthetics

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Mastering Synthetic Data Generation: Methods, Pitfalls and Best Practices

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