Data minimization is the practice of collecting and retaining only the personal or sensitive data that is deemed absolutely essential to an organization.
Test data is a set of data used to verify that an app or system runs as expected – even under extreme conditions, such as edge cases and error scenarios.
Data anonymization removes classified, personal, or sensitive information from datasets, while data masking obscures confidential data with altered values.
Data de-identification is a data masking method that severs the connection between data and the person associated with it, to ensure privacy compliance.
Fake data – computer-generated synthetic data that emulates the characteristics of real-life datasets – ensures data privacy for testing/training purposes.
L Diversity reduces the risk of re-identification of sensitive data by ensuring that individual records in a dataset are not too similar to each another.
Data privacy laws have made pseudonymization or anonymization a requirement for data processing, access, and security. But which is best for your needs?
Pseudonymized data replaces PII with artificial identifiers that deter unauthorized access or disclosure. Examine the pros and cons of this technique here.
Re-identification of anonymized data occurs when an individual can be identified by linking masked data with public records or combined personal attributes.
Data anonymization techniques modify data so that it can’t be linked to a specific person, while preserving its analytical and operational functionality.
DevOps is associated with frequent changes to applications, tools, and technologies, making it difficult to manage test data across multiple environments. Table of...
K anonymity is a data anonymization technique used to protect individual privacy in a dataset, involving PII generalization, masking, or pseudonymization.
The entity-based test data management approach applies a business lens to test data, making it instantly understood and ready for use at enterprise scale.
Informatica released its end-of-life schedule for its on-prem TDM, leaving users with no choice but to migrate to its cloud-based solution, or find a new one. Migration...
Entity-based data masking technology allows data security teams to safeguard PII easily, while ensuring relational consistency and contextual integrity.
Anonymization of data is the process of preserving privacy by deleting or encoding identifiers that link people to their sensitive information in datasets.
Pseudonymization substitutes codes for personally identifiable information in a dataset, while maintaining data functionality for analytics and operations.
What is anonymized data, and why do enterprises require it? Learn about its key types, benefits, challenges, and a new approach based on business entities.
Huge amounts of data are often needed to train AI/ML models. A synthetic dataset is used not only to augment actual data, but also to protect data privacy.
With so many data masking solutions out there, it's hard to know which is best for you. Understanding the use cases, risks, and features helps you decide.
Conventional data masking tools can't process or analyze unstructured data because there's no predefined data model. Modern entity-based data masking can.
Synthetic data is lifelike fake data used to secure personal privacy, test apps before they’re released, train ML models, and validate high-scale systems.
Data migration testing enables the successful and secure movement of data. Discover how comprehensive testing overcomes complexity and prevents disruption.
Data masking is the best way to protect personal data, while ensuring its functionality. Learn about the most common data masking examples and use cases.
As new technologies emerge, test data management trends are evolving. Learn why an entity-based test data management approach is used by top enterprises.
Delphix Test Data Management is fine for some, but those with complex data environments should beware the 12 pitfalls of data virtualization. Support for a limited set...
Metadata provides all the characteristics, properties, and context of data within a data fabric. Read on to learn how to optimize both with data products.
If a data product is meant to democratize data, then it should be as relevant to a centralized data fabric, or data hub, as it is to a federated data mesh.
A good data migration plan is key to ensuring successful migration to the cloud. Build the right plan for your organization using this comprehensive guide.
What are the most essential features to look for in data masking software? Read about the use cases, capabilities, vendors, and approaches to consider.
What does cloud data migration mean, and what can enterprises do to enable a smooth transition from on-prem systems to the cloud? Keep reading to find out.
Data tokenization and masking protect personal information and enable compliance. Learn where and when to employ each – and how business entities can help. Table of...
Masking data, the process of anonymizing personal information with non-sensitive values, is key to ensuring data security and compliancy with privacy laws.
With so many data masking vendors in the industry, finding the right one for your company can be challenging. Here’s what to look for in your evaluations.
Learn the value of synthetic data creation, and what key features to look for when evaluating the different solutions currently available on the market.
An integrated, single customer view continues to evade most enterprises. What is it, and how can businesses achieve a 360 view of the customer in 5 steps?
Discover the key benefits of synthetic test data, and why it makes sense to include it among the test data management tools at your immediate disposal.
Post-M&A Customer 360 use cases illustrate the complex challenge of aggregating and organizing data. Read on to see how a data product approach can help.
For global enterprises, complying with privacy laws is a daunting task. Learn how to simplify compliance, by aiming for the highest data masking standard.
Customer data integration, the key ingredient of Customer 360, continues to evade most enterprises. Why is that, and what can be done about it? Read on to find out.
Dynamic data masking is a data masking technique that limits the exposure of personal or sensitive data by anonymizing it for all non-authorized users..
When evaluating a data masking tool, make sure it handles all types of data masking, and includes certain key features. Read on for the complete checklist, and learn how...
To support continuous development, most enterprises rely on test data masking. But more and more are realizing the value in synthetic test data. Learn why.
Enterprises use Customer Data Management to get the most out of their customer data. Be sure to check 3 things before choosing the right platform for you.
The top test data management tool automates test data delivery in complex environments, by combining a business entity approach with Micro-Database technology.
Intrusive or not, today’s consumers are more accepting of data-driven personalization. With Customer 360 based on data products, enterprises deliver a more personalized...
This guide covers the market drivers and benefits of Customer 360, the shortcomings of traditional Customer 360 solutions, and introduces a unique “data-as-a-product"...
Assure data protection with 5 data masking best practices: Identify PII, choose the right technique, test it, secure it, and ensure relational integrity. Table of...
Data masking and tokenization are used to protect sensitive data. Discover where and when to use each – and how a business entity approach optimizes both. Table of...
Learn why data tokenization is a top data compliance/protection method, and how a business entity approach optimizes its impact, in this webinar synopsis.
For a multinational accounting firm, with over 300,000 employees, complying with global and local data regulations – while federating data ownership, office by office,...
Data mesh is the go-to architecture for distributed data management platforms and systems. Read this guide before selecting a data mesh vendor for your enterprise.
The challenges associated with data mesh implementation need not outweigh its advantages. Data Product Platform is the direct route to decentralized data management and...
We had the pleasure of interviewing Teresa Tung, Accenture Cloud First Chief Technologist, about operational data products in a data mesh. Read on to learn how they...
Federated data management is an emerging approach employed by enterprises to improve business outcomes. Read on to learn how it works, and why it works best on a data...
Beyond the 4 principles of data mesh, we propose a 5th – Centralized Data Control – to enable enterprises to reap the full value of data mesh. Read on. Table of...
Data mesh and data lakes are 2 of today’s hottest topics in data management. How do they differ, and how can they work together? Keep reading to find out.
Although data warehouses and lakes are often associated with offline analytics, Reverse ETL can operationalize actionable insights to make a direct and ongoing impact on...
Today we launched the market’s first Data Product Platform, deployable as data mesh, data fabric, or data hub – in the cloud, on premises, or across hybrid environments....
The tokenization of data replaces personally identifiable information with a valueless equivalent that safely enables compliant testing and development..
Protecting sensitive data is a top priority for the enterprise. Data tokenization is one of the best data protection methods – and a data product approach makes it even...
Data virtualization offers new levels of access, speed, and efficiency when integrating data across disparate systems. Read on to learn how a data product approach...
To overcome the disconnect between business and IT, enterprises need to employ Data Product Managers – and a data management platform based on real-time data products.
With the rising need for self-service data integration, enterprises require a more effective cloud data pipeline solution. iPaaS, based on data products, is the answer.
This article explains "What is Cloud Data Integration?" and goes on to discuss its benefits, challenges, requirements, and a novel Enterprise iPaaS Platform based on...
Tokenization replaces sensitive data with unique identification symbols that retain the basic information about the data without compromising its security..
The top technology trend for 2022 is Data Fabric. A successful Data Fabric architecture is based on Data Products. Make the connection by reading this article.
As enterprises expand to the cloud, the need for a cloud integration platform is clear. But not all solutions deliver the speed and precision you require.
Cloud integration services are revolutionizing the way enterprises approach data, security, and business insights. Read about the drivers, benefits, and requirements...
Data tokenization software replaces sensitive data with a non-sensitive equivalent, or token, and may enable storage of the original data in a token vault.
Today’s enterprises strive to be “data-driven”, but can’t access a good part of their potentially valuable data. Data products get business-critical data out of the dark.
Legacy application modernization services replace outdated software systems with modern solutions to enhance functionality, maintainability, and usability.
Before an app can be released, it needs to be tested across its development cycle using realistic test data. Learn why a business entity approach is best.
"Data as a product", a core principle of the Data Mesh model, realizes its full potential in Data Product Platform. Learn more in this in-depth article.
Today’s enterprises are highly focused on data integration, with the global iPaaS market expected to reach $10 billion by 2025, with a 5-year CAGR of more than 40%.
This paper addresses the why, what, and how of data mesh, including the market need, a detailed description of data mesh architecture, and look at its core capabilities,...
Compare the architectures of data fabric vs data mesh, and learn how both approaches can be fused to create a versatile data management stack. Read on.
Building an effective data pipeline architecture is challenging, especially when enterprise requirements – like scale, speed, and security – are taken into account.
Find out how the ETL method of data integration enhances enterprise data pipelines, and how eETL overcomes the most difficult challenges. Learn more here. Table of...
Test data management best practices assure thorough testing, guard sensitive data, comply with regulations, improve test results, and reduce testing costs.
This paper addresses the what, why, how, and who of data fabric, including data fabric architecture, challenges, benefits, core capabilities, vendors, and more.
The growing dependence on big data is often met with statistics showing that many data lake projects fail because they did not adhere to data quality management best...
A core value of modern data fabric design is a common language between IT and business, providing for data literacy across the enterprise – and, ultimately, better...
What are the challenges and benefits of building data pipelines? This article examines all the requirements, and a data product approach that has them all.
Continuous testing is a process that tests applications under development continuously and automatically, throughout the software development lifecycle.
More than 80% of enterprises consider data integration a critical component in their ongoing business operations, so choosing the right way to integrate data is a...
Test data automation is the process of automatically delivering test data to lower environments, as requested by software and quality engineering teams.
Today’s “automated data preparation” tools aren’t really automated. This article discusses 4 key challenges on the road to automation, and 4 ways to overcome them.
Originally published on eWeek. In 2022, Amazon will become the largest company in the world based on revenue. When that happens, you could say that the world moved from...
Siloed systems impede enterprise data management. An operational data fabric is the best way to stitch current siloed systems together, and retire legacy ones.
Test data preparation tools generate and manage test data for validating the functionality, performance, and security of applications under development.
The data preparation process is critical, due to the importance of maintaining clean, high-quality data for operational and analytical workloads. Here are 7 essential...
Theoretically, data preparation is the process of exploring, combining, cleaning, and transforming raw data into curated datasets for data integration, data science, and...
An on-demand test data management strategy doesn’t just happen. Follow 6 key steps to ensure trusted test data is always accessible to your testing teams.
To enable quick and accurate delivery of applications, software teams need on-demand access to the right testing data. Test Data Management (TDM) is the process of...
We all agree that there is value in data. Just look at the 40% year-over-year growth in the amount of data we collect and utilize. To those of us focused on the field of...
When properly managed, data can bring many valuable insights and business opportunities. According to McKinsey, data governance enables companies to significantly...
Today we announced new core data fabric features associated with our Data Product Platform, to support high-scale operational uses cases in the enterprise.
Shift-left testing refers to testing earlier on in the software development lifecycle to find and fix bugs, improve app quality, and reduce DevOps costs.
After managing many complex digital transformations and migrations, for some of the world’s largest companies, I’d love to share some key data migration considerations...
Test data management is important to agile development in ensuring test accuracy and reliability, thus enhancing the effectiveness of the testing process.
This article will focus on which data store is best for real-time, massive-scale, hyper-speed operational use cases – Data Product Platform, as a data fabric vs data...
When we think about data privacy trends, many of us immediately focus on the regulations that are already in place. While existing laws continue to evolve at a growing...
We all know that data is the new oil. That is why the GDPR right to be forgotten, also known as the right to erasure, is essential. And, tough in multiple ways.
The short answer is: Automate. To find out about the 10 steps of processing a data subject access request, and, what you should automate, read the long answer.
After Europe’s GDPR and California’s CCPA, the latest data privacy law comes from none other than Virginia. In a unanimous voice, Virginia’s Senate passed the Virginia...
New K2View logo and website Companies operate “under the radar”, or “in stealth mode”, as they wait for product readiness, first orders, revenue milestones, or an...
What do the analysts say? Forrester defines data fabric as a platform for “orchestrating disparate data sources intelligently and securely in a self-service and...
Every so often, you get lucky, and the problem comes to you. And this is one of those times. Our world is full of problems looking for solutions and then, there are...
In our previous post, we discussed the data challenges that typically accompany the Right to Access Data. Now, it’s time to dive into the specific disadvantages of...
One of the primary rights highlighted by privacy regulations, like GDPR and CCPA, is the right to access data. This right, which is detailed in Article 15 of GDPR,...
Since GDPR became part of our world, it has influenced data-related practices within and outside of Europe. When the law passed in 2016, companies worldwide started...
When GDPR came into force in 2018, it introduced a set of data privacy requirements that transformed how companies track and analyze user activity, communicate their...
Depending on the type of data privacy management solutions your organization used to comply with California Consumer Privacy Act (CCPA), which went into effect on...
We often think of legislation as a slow and tedious process, believing that it takes time to implement even the slightest changes. Perhaps that’s why the pace of data...
Recently, we discussed why keeping up with the explosion of data privacy regulations and data privacy compliance necessitates a fundamentally new approach to data...
In 2018, the California State Legislature signed the toughest data privacy law in America to date. What started as a ballot initiative, became a disruptive new law that...
By Achi Rotem, CEO & Co-Founder Today we were happy to announce that for the first time in our history, K2View has raised a combined $28 million in capital with...
When we think of data privacy regulations and how they affect today’s organizations, most people first think of GDPR—the European Union’s General Data Protection...
April and May have been productive months here at K2View. Both Fabric 6.1 and TDM 6.1 were released. In addition to minor fixes, there are several changes to both...
Have you wanted to learn more about K2View's Test Data Management (TDM) product? If so, you are in luck; last week, Hod Rotem, VP of Global Solutions at K2View, provided...
This week we had a chance to speak with Dominic Garcia, K2View's Head of Marketing, on DataOps. In the video, we discussed the innovation of DataOps, as well as how...
Most organizations that take a modern approach to cybersecurity embrace the “assume you’ve been breached” mindset. This thought process is helpful as you think through...
Dominic Garcia, Head of Marketing at K2View, recently had an opportunity to sit down with Steve Kostyshen, K2View's CEO, and discuss the impacts of COVID-19 and the...
Last night, K2View had the distinct pleasure of being honored at a reception for our spot on the Dallas 100 – a list of the top 100 fastest private growing companies in...
This week, K2View Fabric was awarded the 451 Firestarter Award in Invisible Infrastructure for “tackling the challenge of real-time data integration and exposure at...
by Dominic Garcia, K2View Head of Marketing There’s been a little bit of buzz around K2View over the last few weeks. You may have seen our announcements regarding our...
by Achi Rotem, K2View CTO If we had it to do all over again, we’d never architect enterprise systems the way we did 20, 10, or even five years ago, especially when it...
Israel – July 25, 2017 – Cellcom Israel Ltd., (TASE: CEL, NYSE: CEL) the largest cellular operator in Israel, today announced that K2View,inc., a leading distributed...
K2View today announced plans for participation at Dreamforce, the annual Salesforce customer event on November 6 – 9 in San Francisco. Dreamforce is the largest software...
K2View today announced plans for participation at The CIO Insight Summit, a GDS Summits event on November 15 – 17 at the Resort at Pelican Hill in California. The CIO...