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IT & Data Center

How AI development and web data collection go hand in hand

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Article ImageIn the case of developing AI using web data, knowledge is power, says Bright Data’s Or Lenchner

Artificial intelligence and machine learning are often at the heart of the systems involved in collecting web-based data to guide a specific research process or a business looking to make an informed decision based on available data sets.

AI is becoming increasingly crucial in commercial operations.

According to Deloitte's research, 73 percent of IT and line-of-business executives perceive AI as an essential aspect of their present operations.

But the reverse is true, too. AI systems can only ever be as powerful as the data they're based or trained on.

Data collected online can be fed back into AI systems to help them develop better and more efficient processes. In fact, we can go as far as to say that web data and the development of AI go hand in hand.

What are the crucial web data collection elements that enable AI development? How is this data sourced and collected? And how can systems be trained in the right way?

Public web data is the main fuel for AI development

Automated web data collection has continued to advance across every industry mainly due to the increasingly fierce market competition and the need to be informed with up-to-the-minute data.

Of course, public web data can come from a variety of places, but the biggest place of all is the internet.

It provides a level of information about consumer habits, likes, and dislikes activities, and preferences that were impossible to obtain a decade ago.

For example, social media data is being used by organizations as a source of information about consumer sentiment and behavior. Here’s where AI development shines.

Its greatest asset is its ability to continually learn and adapt based on the data fed into its system.

Its capacity to recognize data trends is only useful if it can adapt to changes and fluctuations in those trends.

The AI knows what data stands out, what is significant or not, and can adjust as necessary.

All this data is being used to develop AI systems by businesses in industries as varied as insurance, market research, consumer finance, and real estate to gain an edge over their competition.

There is no such thing as data overload when it comes to AI. In fact, the more information you have, the better.

Many data hurdles ahead

However, problems arise when the data being used is not good or reliable data.

Accessing public web data at this mammoth scale is not without its challenges.

Organizations are often blocked by competitors in the process of retrieving data, or they encounter difficulties accessing data in every region they are looking to target globally.

As a result, it's critical for organizations to invest in a web data platform that can provide them with the data they require on a constant basis.

It will have to be a worldwide network capable of handling massive data volumes.

Being able to access the correct data is essential, as teaching AI systems properly is impossible without following the proper data retrieving protocols.

Only 'clean' accurate data can create the right level of ROI for businesses. Often, requests seen as coming from data centers are blocked by websites or fed incorrect information, as businesses want to prevent accessing data by their competition to gain a competitive advantage.

Using a flexible web platform solves this problem, as it provides a transparent view of the internet – just like how it was initially intended to be used when it was created.

How can AI continue to develop using web data?

Building an AI system is akin to building a house. You can have the best architect or the best team of builders available, but if there are flaws with the raw materials like if they are the wrong type or there are simply not enough of them, there are going to be serious issues with the final product.

But that’s just one side of putting together the AI jigsaw puzzle. Yes, you need the tools, but you also need the people behind the AI to feed data into the engine; you need the real 'architects' behind the technology to truly make AI 'smarter'.

Then comes the data, lots of reliable data, sourced from the web, which enables AI to truly realize its potential.

For AI to continue maturing and evolving, it requires massive amounts of data. In the case of developing AI using web data, knowledge is power.

To best unlock this power, you must define the type of data you need, where to look for it, and how to reliably get it.

You also need to make sure you can trust the data to train your most valuable systems, which are responsible for your business and your customers' decision-making processes for prosperity.


Or Lenchner is CEO of Bright Data. For the past three years, under his leadership, the company has advanced its product offerings to include first-of-its-kind automated solutions, enabling its customers to collect and receive data in a matter of minutes. Among Bright Data's thousands of customers are Fortune 500 companies, major e-commerce firms, and sites, prominent finance firms, leading security operators, and academic and public sector organizations.

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