Omri Orgad
AI initiatives have quickly become integral to the present and future of many modern organizations. According to a Deloitte survey, 73% of IT and line-of-business executives see AI as an indispensable part of their current operations.
But businesses are facing two key challenges. Firstly, the power and efficacy of AI systems is dependent on the information they are built on, and secondly, effectively training these systems requires huge quantities of very specific data.
Sourcing the right information
Often, the data required comes from the world’s largest source of information – publicly available online data. For example, organizations commonly use social media data to uncover information about consumer sentiment and behavior. This data is being used to help businesses develop AI systems and gain a competitive edge in a wide range of industries, including insurance, market research, consumer finance, and real estate.
By leveraging data from the likes of Twitter posts and online reviews, businesses can develop the AI insights needed to track competitor movements, stay on top of industry trends and remain afloat during periods of uncertainty. For example, hiring announcements and job postings on social media or other job websites in a certain industry could indicate an economic rebound in that sector.
But collecting these types of data on a large scale is easier said than done. Challenges such as being blocked by competitors when gathering data or encountering difficulties collecting data in certain target regions are commonplace. These challenges are further complicated by the need to counterbalance business goals with the responsibility of treating consumer data with the respect it deserves. Not only that, businesses must remain in accordance with data protection legislation such as GDPR.
If businesses want to be able to develop the AI systems they need to stay competitive in a responsible manner, taking the right approach to online data collection is an all-important factor.
Pay attention to the method of data collection
When it comes to properly and effectively training AI systems, it’s essential that businesses follow the appropriate data collection protocols. Only “clean” and accurate data can create the level of return on investment (ROI) required.
However, data collection processes are often hindered by competitors, such as by preventing data scraping in an attempt to maintain a competitive advantage. For example, requests seen as coming from data centers may be blocked by websites, or fed incorrect information. This can have a significant impact on ROI, with research from Cognilytica indicating that 80% of project time is spent cleaning data in preparation for AI usage. Businesses can solve this problem by leveraging a robust network of residential IPs. With this in place, data collection requests will appear as indistinguishable from real consumer activity, so will yield the same data points.
Instead of data being blocked or displayed incorrectly, IP proxy networks enable businesses to view online information exactly as it appears to everyday consumers, thereby providing a realistic and transparent view of the internet in every region. Having this level of market visibility is vital for businesses, providing insights such as how dynamic pricing impacts the cost of an online product for consumers in different countries. Having these subtle distinctions reflected in organizations’ data sets will give them the tools to produce the value-creating AI models they need to prosper.
Make better decisions with cleaner, more reliable data
If businesses want to generate meaningful ROI from their AI and machine learning systems, clean data-sourcing must be made a long-term priority. Practically implementing this requires careful planning, including defining the ultimate goal of their AI endeavors – whether that be predicting future real estate prices in a certain area or monitoring the activities of a competitor. The next step is to define the data that is most important to fulfilling this goal. For example, ata indicating the number of new businesses opening in a specific region could act as a rough guide of economic growth.
Lastly, businesses need to adopt a data collection platform that can consistently feed them the data they need at the large scale they need it. Key criteria include having a global network, the capacity to handle massive data volumes, and the ability to incorporate consumer devices in every location. In a recent survey published by the leading analyst firm Frost & Sullivan, 54% of IR decision makers expressed a need for larger-scale data collection as appetite for data continues to grow have.
Having an online data collection platform in place will provide a foundation for AI development. Think of it like building a house. Any flaws with the raw materials will ultimately lead to serious issues with the final product – irrespective of the skills of the architect and builders. The same is true when building AI systems. Starting with a foundation consisting of clean and accurate online data sources will provide a robust platform for high-quality AI systems to be built on top of. These systems will be able to provide powerful, dependable, and accurate business insights despite the unprecedented volatility in market trends.
In today’s ever-changing world, businesses are faced with more potential for error than ever before. They are under huge amounts of pressure, which is why a higher proportion of business decisions are being made firmly on the basis of AI derived insights. Collecting the best online data possible empowers businesses to make the most effective decisions and gives them the best chance of long-term success.
