Web data is driving AI development
"When looking at building an AI system, you could look at it as if you were building a house. If there are any flaws with the raw materials there are going to be serious issues with the result," writes Or Lenchner, CEO of Bright Data
AI systems can only ever be as powerful as the information that they are built on. With huge quantities of very specific data needed to effectively train systems in the right way, here is a brief overview of the key points behind the data required and how it is being sourced.
Web data - The AI goldmine
First, we will look at where the data comes from. It often comes from the largest source of information that has ever existed – publicly available web data. Why? Web data is considered the only source of data that is flexible enough to reflect our actual reality and sharp enough to provide us with trustworthy data. When it comes to relying on data – no data at all is better than unreliable data. Web data sources also include public social media data. This form of data is widely being utilized by organizations as a source of information about consumer sentiment and behavior. When it comes to AI systems, this data helps develop systems by industries as varied as insurance, market research, consumer finance, and real estate to gain an edge over their competition.
In these instances, information such as public Twitter posts and online reviews data is leveraged to develop the AI insights needed to stay afloat in a volatile business environment. For example, hiring announcements on Twitter or other job websites for positions in the automotive industry could indicate an economic rebound in that sector, or that the industry itself anticipates an uptick in demand.
Overcoming data hurdles
Although public web data is widely available, accessing it at the large scale needed comes with several challenges. Organizations are often blocked by competitors or for other reasons in the process of retrieving data, or they encounter difficulties accessing data in every region they are looking to target globally. Web data platforms support this quest and help overcome all challenges presented including the sheer scales of operation.
However, being able to access reliable 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, accessing public web data is met by competitive considerations, leading you to misleading data which can be detrimental to your mission. Using flexible web platforms solves this problem, as it provides you with a transparent view of the internet – allowing you to view the internet just like any other person across the globe.
The power of the right type of data
Data is growing at an exponential rate – that has now become an understatement. Yes, businesses can benefit from this to a great extent. When looking at building an AI system, you could look at it as if you were building a house. You can have the best architect or the best team of builders on the planet, but if there are any flaws with the raw materials, they are the wrong type, or there are simply not enough of them, there are going to be serious issues with the result. The same goes for your data-driven operation. If you build on a foundation consisting of clean and accurate web data sources, you will have a robust base from which you can build powerful AI systems. These systems will be able to provide effective, dependable, and relevant business insights despite the unprecedented volatility in market trends.
Or Lenchner is the CEO of Bright Data