Data-driven decision making is not a new practice for successful organizations. However, recent increases in the use of public web data by organizations are reducing organizations’ reliance on traditional data sources that are sourced from systems such as ERP and CRM. Instead, they access the latest alternative data from the world’s largest database – the Internet.
The Internet and the Internet of Things are benefiting from artificial intelligence, and artificial intelligence is rapidly shaping the world around us and is becoming increasingly important in business operations. In fact, Deloitte research shows that 73% of CIOs and business executives view AI as an indispensable part of their current business. It is clear that there is great potential for artificial intelligence in almost all areas of our lives. However, AI systems can be just as powerful as the information they are built on. Huge amounts of highly specific data are needed to effectively train systems in the right way. Here, we will explore the main points behind the required data and how it is obtained.
Web Data – AI Gold Mine
To use data to drive AI, you need to know where your gold mine is, and it’s more readily available than you thought. This is because this “proprietary” often comes from the largest source of information that ever exists – publicly available web data. To give just one example, organizations use public social media data to obtain information on consumer sentiment and behaviour. This data is used to develop AI systems by companies in industries as diverse as insurance, market research, consumer finance, and real estate to gain an edge over their competitors.
In some of these cases, 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 sites, hiring announcements for positions in the service industry could indicate an economic recovery in that sector, or that the industry itself expects a rise in demand.
Overcoming data hurdles
Despite the huge availability of public web data, accessing this type of data at such massive scales is quite a challenge. When organizations seek 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 factors can be regionally specific to global organizations. One conclusion from this? Companies need to adopt a web data platform that can continuously feed them with the data they need to guide informed decision making. This platform should be a global network, having the ability to handle huge data volumes.
When it comes to running AI, the ability to access and retrieve the right data is essential to properly teach AI systems the output your organization wants to achieve. The power of correct and “clean” data, along with artificial intelligence, shows the potential for ROI that businesses can earn. Oftentimes corporate websites either block requests from data centers for accessing information or even provide incorrect information to the data center. This is a result of companies trying to prevent what they see 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 organizations looking for data to use a flexible web platform. These platforms provide your organization with a transparent view of the Internet – just like its original purpose.
Data is growing at an exponential rate, and while companies 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 can be compared to building a house. You can have the best architect or the best team of builders, but if there are any defects in the raw materials, (for example, if they are the wrong kind of materials, or if there aren’t enough right materials) there are going to be problems. serious with the final product. Likewise with your AI systems, if organizations build them on a base consisting of clean and accurate web data, they will have a sound foundation to start building powerful AI systems. These systems will be able to provide effective, reliable 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 is the ethical use of robotics to drive artificial intelligence and data collection. Organizations are maturing and adapting at a time when data growth is exponential from day to day, and data return is a good thing for organizations. Technological innovations have never happened so quickly before, and everyone should be excited about it.
catch? Industry leaders and customers alike are challenging the status quo and calling for responsible, compliance-based guidelines around Big Data and automated data collection practices. Compliance and data work hand in hand, and conversations about the two should be a top priority for all businesses. Organizations can initiate this process by being transparent about their guidelines, such as explicit communication of their data source operations.
Another industry-wide challenge as it relates to AI and data is the responsible use of robotics. Robots help organizations keep pace with the ever-increasing and fast-moving automated procedures. They simply make us faster and more efficient. However, as with any other technology, there are those who use robots to cause harm. The IT teams were primarily responsible for providing oversight over the use of the bots. Despite their actual oversight, questions about responsible bot use should align with the organization’s mission and come from the C-suite. A recent survey by research firm Vanson Bourne and Bright Data suggests there is a strong appetite for clearer and possibly stricter regulations and guidelines.
By implementing AI-powered solutions with a compliance-based foundation for automated data collection, for example, organizations can use automation to augment the hard manual work around data, thus ensuring high-quality data collection to build and run AI. The challenges of collecting public web data can be easily overcome with the right techniques and guidelines in place. These guidelines will certainly enhance clarity and confidence and allow us all to enjoy the key competitive advantage that data provides while moving towards a transparent future.