Or Lenchner, CEO of Bright Data, has been ahead of the market-leading web data collection platform since 2018, driving its expansion, innovation and growth to over $100 million in annual revenue. BRIGHT Data enables Fortune 500 companies, leading enterprises, renowned universities and public sector entities to access public network data in real time and at scale. Lenchner is a strong advocate of keeping public network data open and accessible, emphasizing its critical role in driving innovation.
What inspired your journey into the world of data and AI, and how have you shaped Bright Data’s mission and vision since becoming CEO in 2018?
I’ve always been fascinated by the power of data, especially how it drives decision making and drives innovation. When used correctly, data can also drive business transparency. Becoming CEO of Bright Data in 2018 gave me the opportunity to help shape how AI researchers and businesses procure and leverage public network data.
What are the main challenges facing AI teams in purchasing large-scale public network data? How to solve the bright data?
Scalability remains one of the biggest challenges for AI teams. Since AI models require a large amount of data, efficient collection is not a small task. And, since AI models are only as good as the data they train, ensuring teams have access to fresh, high-quality data is an ongoing challenge. This is especially true with the development of the network in real time.
Another major issue is compliance. Data privacy laws and requirements are evolving, so AI teams need to be aware of these changes at all times. They also have to understand how to deal with websites that enforce countermechanisms, which can complicate the data collection process.
The platform we build on Bright Data can solve these challenges. We provide scalable automatic data collection that provides structured real-time data. Our AI-powered tools clean and verify data for accuracy. We have taken strict measures to ensure legal and ethical data collection is in compliance. Our idea is to empower AI teams to focus on building great models while we deal with the complexity of data procurement.
How high-quality web data can help AI model performance, and what are the best practices to ensure data accuracy?
High-quality data means complete data, no bias, and most importantly, accuracy. If you lack or get stuck in errors and errors, the resulting AI model will not be executed as expected.
To achieve accuracy, it is best to obtain data from various public resources with established reliability. Using only a few or worse is that a single data source can cause problems such as incompleteness. Having multiple sources provides the ability to cross-reference data and build more balanced and well-represented datasets. Additionally, organizations should consider automatic data verification and cleaning to effectively get rid of wrong and inconsistent data.
In the bright data, we consider all of these factors. We provide AI teams with structured and real-time data that has been verified to ensure accuracy. This way, they can train the model confidently.
What is the biggest ethical issue in today’s public network data collection?
Privacy remains one of the biggest problems in public network data collection. People are worried about their data being abused and abused. To ensure data remains private, it is crucial to emphasize transparency. The organization that accumulates data must make expectations in terms of the data it collects. It is important to ensure that the public uses their data in accordance with strict ethical guidelines.
Another major concern is monopoly. Some large companies can control a large amount of data, which creates an uneven playing field where only a few people have access to the information needed to train AI models and drive innovation. This is not the case. Businesses, researchers and developers should have access to public network data. In this way, AI development will not be concentrated in the hands of only a few major players.
Morality is not the afterthought of bright data. They are embedded in every decision we make. We not only follow industry standards – we set them. We lead the data collection industry and define the right ethical standards. We want to ensure that public network data is accessed in responsible, transparent, and fully compliant with global regulations.
How can bright data ensure compliance with global data privacy regulations while still enabling large-scale data collection?
Our organizations are committed to collecting and utilizing compliance with global legal and regulatory requirements. We believe that we comply with the requirements of GDPR, CPRA, CCPA and other relevant regulations. It is important that we strictly follow the Know Your Customer (KYC) protocol to ensure that only legitimate users can access our platform. Our data solutions are accessible only by legitimate businesses and researchers.
Our acceptable usage strategies are also evident when defining which data can and cannot be collected. This includes responsible use. We have a dedicated compliance team responsible for continuously monitoring regulations to determine that our latest legal and regulatory requirements are up to date.
In any case, we still believe that public network data should remain accessible. Our goal is to provide the data we need to provide AI teams with the data they need while ensuring compliance with privacy and legal standards.
How do you balance business growth with maintaining ethical data collection practices?
We always believe that morality and growth are not mutually exclusive. The trust of customers and the relationships they have established are the most important issues. We understand that we can achieve long-term success only by collecting data under transparent terms and in accordance with applicable laws.
Therefore, we have developed a strict review agreement for users. This is designed to ensure that the data we collect is used ethically. We allocate time, energy and resources to compliance and security to protect our customers and the general public in general. By observing ethical data collection, we have achieved success in our business while contributing to building a transparent and responsible AI ecosystem.
How does bright data maintain data privacy regulatory changes?
We understand that our data usage processes and policies inevitably have to change to reflect changes in relevant laws and regulations. Therefore, we regularly consult legal experts and communicate with regulators. We also discuss with lawmakers and others involved in policy making and provide opinions on developing meaningful data regulations. Our goal is to strike a balance between innovation and data privacy.
With the release of new laws and amending regulations, our data collection and use framework will continue to evolve. We have a compliance team that can proactively update our data usage policies to ensure our platform is always fully compliant. In addition, we operate client education programs to facilitate ethical data use.
What are the emerging trends in AI data collection that companies should know?
Real-time data collection has become a must-have for today’s AI models. For them, accessing the latest or freshest data to provide high accuracy and provide a better user experience is crucial.
Another notable trend is the reliance on synthetic data for data augmentation, where AI-generated data can complement data sets collected from real-world scenarios.
I also saw a strong interest in pursuing explainable AI. Currently, most AI models suffer from black box effects or their decision-making process lacks transparency. Companies are seeking to change this paradigm by creating AI models that detail the way they formulate their outputs or decisions.
Finally, the company is aware of the growing data privacy issues. This is why AI technology is designed to protect data privacy, such as federated learning. Organizations want to maximize AI model training without any compromise on user data privacy.
We make sure we have these trends in hand, so we can build solutions that keep the AI team competitive.
How do you see AI-driven agents and automation changing the data collection landscape?
Currently, AI models utilize most manually collected structured datasets. These datasets are also subject to preprocessing, cleaning and other procedures that typically involve human intervention. With the rise of AI agents, it can change in the near future to automatically collect and process data for AI training. They make it possible to automatically learn from real-time web data at an unprecedented scale.
We have created infrastructure that supports the deployment and evolution of AI agents, allowing smooth access to high-quality real-time data on the network. This technology enables complex AI systems to continuously interface with dynamic network data, learn from it and become bigger and better.
Instead of relying on static and manual processing of data, artificial intelligence agents can change the industry while allowing AI systems to access and learn to constantly change data sets on the network. For example, this could lead to banking or cybersecurity AI chatbots that are able to come up with decisions that reflect the latest reality. This leads to huge improvements in efficiency and more areas of automation.
In Bright data, we implement this transformation not only in the field of data collection. We believe we are at the forefront, introducing a technology that introduces the next generation of artificial intelligence. We are excited to help businesses and AI teams as they leverage the full potential of AI agents.
Thanks for your excellent interview and hopefully learn more about the readers who should access the bright data.
