Are AI And ML The Answers To The Data Tsunami?

In our digital age, advanced analytics including artificial intelligence (AI) and machine learning (ML) have become valuable tools in business decision making. Businesses are gathering and processing more data than ever before and investing considerable resources to drive analytics-based decisions. With an exponential volume of information flowing on a daily basis, businesses have struggled to keep pace with advanced technology and make the best use of their data. AI and ML are the solutions everyone seems to want to employ, although few understand the difference in those technologies or how to effectively employ them. Should businesses rely on AI or ML to keep up with the waves of the data tsunami?

Benefits Of Artificial Intelligence And Machine Learning

While the terms AI and ML are both part of the advanced analytics lexicon and are often used interchangeably, there is a difference. Machine learning defines a computer system that has the ability to learn how to do specific tasks, which includes using past data to make decisions or predictions without human interaction. Artificial intelligence refers to "computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages." Many AI systems use machine learning techniques to function.

One of the key similarities between both AI and ML is that both technologies become even smarter with data. They help us create actionable information from the incredible amount of data we have access to, therefore allowing us to have more meaningful interactions with our customers. They can help us answer the age-old questions of "Who are our customers?" and "What do they want to buy?"


For example, a music subscription service uses AI and ML to evaluate what a user has listened to in the past in order to make recommendations for future listening sessions. Voice-activated assistants and chatbots use AI and ML to perform tasks, like responding to a user when they a question about a product or service. When consumers shops online, businesses will use an algorithm to feed them advertisements for similar products the customers are likely to purchase based on their shopping or viewing history.

As consumers, we get a more personalized experience with the apps and programs we’re using on a daily basis. As businesses, we gain vital information on those who are interacting with our brand.

Challenges Of Artificial Intelligence And Machine Learning

One of the challenges associated with AI and ML is having a sufficient amount of data and the type of data required for a system to learn over time. For example, as people have used search engines over time for answers to all kinds of life questions, those search engines have relied on that data, along with AI and ML systems, to generate more accurate and relevant responses each time someone searches. Cross-device identification has added another layer to this data tsunami — a user might search a term on their laptop, and then conduct a separate search on a cell phone later that day. A business must be equipped with the right tools to identify that consumer and keep up with their interests and habits.

There is also a transparency factor when it comes to AI and ML. If a business uses an ML system to predict a user’s next playlist or song choice, the results might be skewed if the user’s friend takes over the music during a road trip. The machine’s next few suggested songs or playlists might not make sense to the user until the algorithm starts to learn again with the original user.

In addition, businesses need to be mindful of legal implications and customer privacy before utilizing AI and ML. In a regulated industry such as banking, advanced analytics can be a convenient tool to help businesses make lending decisions based on their consumers’ spending habits and credit histories, but expandability and compliance are both concerns. While the impact of product sales is important, in that AI and ML help to increase sales and contribute to the business’s bottom line, the decision doesn’t have the same implications as a regulated decision. When a customer applies for a loan, a financial institution can face a large-cost implication if it wrongfully rejects or accepts an application. When a machine helps make the decision, it becomes hard to provide the reasoning. In addition, it becomes harder to ensure non-static decisioning models are compliant. You might be able to ensure they are compliant to begin with, but as they learn and adjust, you can no longer prove that model is still compliant.

A Future With Artificial Intelligence And Machine Learning

As more data is created, more sources become available and more data sets are created, the ability to start thinking about alternate ways to manage the data becomes more relevant. While AI and ML have their challenges and benefits, both have become increasingly important. When used correctly, they both can help businesses sell more, make better predictions and create satisfied customers. There is still plenty of room for growth and improvement, but as long as a business determines what its objectives are, the outside factors that might impact a data-driven business decision and any potential implications for an undesired outcome, the answer is yes: AI and ML can help us better ride today’s data tsunami.

Source: Forbes

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