[ad_1]
Or Lenchner is CEO of Bright Data, a position he has held since July 2018. 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, travel sites, academic and public sector organisations.
Artificial Intelligence (AI) is fast shaping the world around us and is becoming increasingly important within business operations. In fact, research by Deloitte shows that 73% of IT and line-of-business executives see AI as an indispensable part of their current business. It’s clear to see there is great potential for AI in virtually all areas of our lives, but 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, we’ll explore 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, and it is more easily available than you might have assumed. That’s because it often comes from the largest source of information that has ever existed – publicly available web data. Public social media data, to give just one example, is being utilised by organisations as a source of information about consumer sentiment and behaviour. 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.
In these instances, information such as 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 the data is widely available, accessing public web data at this mammoth scale is not without its challenges. Organisations 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. Therefore it is important businesses adopt a web data platform that can consistently feed them the data they need. It will need to be a global network, with the capacity to handle gargantuan 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 because only “clean” accurate data can create the right level of ROI for businesses. Often, requests seen as coming from data centres 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 you with a transparent view of the internet – just like it initially intended to do.
The power of correct data
Data is growing at an exponential rate and although businesses can benefit from this, they must take steps to ensure the right technology and processes are in place to generate real value. When looking at building an AI system, you could see it like 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 final product. If you build on a foundation consisting of clean and accurate web data sources, you will have a robust base that you can build powerful AI systems on top of. These systems will be able to provide effective, dependable, and relevant business insights despite the unprecedented volatility in market trends.
Editor’s note: This article is in association with Bright Data
(Photo by calvin chou on Unsplash)
[ad_2]
Source link