Data-driven decision making is not a new practice for successful organizations. However, recent increases in the use of public web data by enterprises is reducing organizations’ reliance on traditional data sources that are sourced from systems like ERP and CRM. Instead, they’re accessing up-to-the-minute alternative data from the world’s largest database—the internet.
The internet and IoT benefit from AI, and AI is quickly shaping the world around us and becoming increasingly important within business operations. In fact, research by Deloitte shows that 73% of IT and line-of-business executives view AI as an indispensable part of their current business. It’s clear to see that there is great potential for AI in virtually all areas of our lives. However, AI systems can only ever be as powerful as the information they are built on. Huge quantities of very specific data is needed to effectively train systems in the right way. Here, we’ll explore the key points behind the data required and how it is being sourced.
Web Data—the AI Gold Mine
To use data to drive AI, you need to know where your AI gold mine exists, and it is more easily available than you might have assumed. That’s because this "mine" often comes from the largest source of information that has ever existed—publicly available web data. To give just one example, organizations are using public social media data to source consumer sentiment and behavior information. 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 some of these instances, information such as Twitter posts and online reviews is leveraged to develop the AI insights needed to stay afloat in a volatile business environment. For example, on Twitter or other job websites, hiring announcements for positions in the service industry could indicate an economic rebound in that sector, or that the industry itself anticipates an uptick in demand.
Overcoming Data Hurdles
Despite the overwhelming availability of public web data, accessing this kind of data at such massive scales has its challenge. When organizations look to retrieve web data, they are often blocked from accessing that information. There are also other factors that can prevent companies from processing web data, and these can even be regionally specific to global enterprises. One conclusion from this? Businesses need to adopt a web data platform that can consistently feed them the data they need to guide informed decision-making. This platform will need to be a global network, with the capacity to handle mammoth data volumes.
When it comes to powering AI, the ability to access and retrieve correct data is essential to properly teaching AI systems the outputs your organization wants them to achieve. The power of correct and “clean” data, coupled with AI, shows the potential ROI businesses can earn. Too often businesses' websites will either block requests coming from data centers to access information or even feed incorrect information to the data center. This is a result of businesses attempting to prevent who they perceive as competitors from gaining a competitive advantage. Unfortunately, this deceptive practice ultimately hurts the end-user—your customer. One solution to this practice is for data-seeking organizations to utilize a flexible web platform. These platforms provide your organization with a transparent view of the internet—just like it’s original intent.
The Power of Getting it Right
Data is growing at an exponential rate, and although businesses can benefit from this growth, they must take steps to ensure that the right technology and processes are in place to generate real value. Building an AI system could be compared to building a house. You can have the best architect or the best team of builders, but if there are any flaws with the raw materials, (e.g., if they are the wrong type of material, or if there is simply not enough of the correct material) there are going to be serious issues with the final product. Similarly with your AI systems, if organizations build them on a base consisting of clean and accurate web data, they will have a healthy foundation on which to begin generating powerful AI systems. These systems will be able to provide effective, dependable, and relevant business insights in the face of unprecedented market trends and market volatility.
With Great Power Comes Responsibility
The data industry faces unique challenges, and one of its biggest challenges is around the ethical use of bots to drive AI and data collection. Organizations are maturing and adapting in a time where data growth is exponential from day to day, and the data yield is a good thing for organizations to have. Technological innovations have never occurred so rapidly, and everyone should be excited about this.
The catch? Industry leaders and customers alike are challenging the status quo and are calling for the creation of responsible and compliance-driven guidelines around larger data practices and automated data collection. Compliance and data run hand in hand, and conversations around the two need to be a priority at the helm of all companies. Organizations can start this process by having transparency around their guidelines, like openly communicating their data sourcing operations.
Another industry-wide challenge as it relates to AI and data is the responsible use of bots. Bots help organizations keep up with the ever-increasing, fast-moving automated actions. They simply make us faster and more efficient. However, like with any other technology, there are those that use bots to cause harm. IT teams have been primarily responsible for providing oversight on bot use. Despite their de facto stewardship, questions around responsible bot use should align with an organization’s mission and come from the C-suite. A recent survey conducted by research firm Vanson Bourne and Bright Data indicates a large appetite for clearer and possibly stricter bot regulations and guidelines.
By applying AI-powered solutions with a compliance-driven foundation for automated data gathering, for example, organizations can use automation to augment tedious, manual work around data, thus ensuring higher-quality data collection with which to build and power AI. Public web data collection challenges are easily overcome with the right technologies and guidelines in place. Such guidelines will surely enhance clarity and trust and allow us all to enjoy the major competitive edge that data provides while advancing towards a transparent future.