Is Applied Intelligence the Future of Data?

Feb 2, 2020

 Cloud-based supercomputing, self-driving cars, hyper-personalization of customer experiences, and autonomous drones are all evidence of the dramatic changes taking place all around us. We are in the midst of what many have dubbed the “Fourth Industrial Revolution,” by the end of which we may be challenging our notions of what it means to be human.

 A fundamental tenet of this so-called “Industry 4.0” will be applied intelligence. And no, I didn’t mean to write artificial intelligence (AI). Applied intelligence, while intrinsically linked to AI, is what many businesses will depend on to make critical decisions by gathering intelligent insights into both operations and customers.

 So what exactly is it? And how will it shape our future data?

 What is Applied Intelligence?

 Simply put, applied intelligence combines the power of analytics, AI, and automation to enable high-functioning systems to surpass human intelligence for a specific purpose. Over the coming decade, businesses will use applied intelligence to create insights that identify clear opportunities for action. This new form of actionable intelligence helps companies find novel ways to satisfy customers by using intelligent technology to augment the scope of human ingenuity.

 The most significant application of this type of intelligence is within data. Today, companies generate billions of data points daily. But without intelligent analysis, data is just data. With actionable intelligence, however, businesses can glean actionable insights from that data to make better-informed decisions. Instead of relying on the analysis of past data, organizations can make decisions based on what is going to happen in the future based on real-time data.

 Better still, this can be a process that takes place without the need for human involvement. Organizations can use machine learning algorithms to perform analysis of real-time data to accurately predict future outcomes and carry out tasks automatically based on those predictions.

 This real-time data analysis is already taking place within companies hoping to lead the charge into the future of intelligent decision making. It’s worth looking at a few real-world applications to better understand how applied intelligence is going to influence future data collection and analysis.

 Uber Using Applied Intelligence to Remove Surge Pricing

 There are multiple issues that ride-sharing apps such as Uber and Lyft are utilizing applied intelligence to solve. Determining what price to charge, reducing wait times for passengers, and matching up itineraries of fellow passengers to minimize detours are all front-line problems that better insights from data can help to solve.

 Currently, Uber relies on an unpopular policy of surge pricing to balance demand, but by the end of the year, this will no longer be the case. By setting up machine learning algorithms to analyze billions of data points generated by drivers, the app will automatically divert drivers to exactly where they need to be, precisely when they need to be there — eradicating the need for surge pricing.

This important change is a classic example of how applied intelligence will be used by a broad range of companies to improve the consumer experience, gaining a competitive advantage in the process. But applied intelligence’s utility isn’t limited to consumer-facing problems, as the credit scoring agency FICO has been demonstrating.Dara Khosrowshah discusses future data and applied intelligence

 FICO Leading the Fight Against Online Fraud with Applied Intelligence

 During 2021, 2.14 billion people will make an online purchase. Great news if you’re an online retailer, or perhaps not. Before the end of 2023, those retailers will have collectively lost approximately $130 billion as a direct result of digital CNP (Card-not-Present) fraud.

 The daily transaction volumes of online retailers are far too high for human analysis. Even if humans were to analyze these data sets, the fraudsters would be long gone before the suspicious transactions are uncovered. The rising fraud epidemic has sprung the analytics firm FICO into action to better protect businesses against the risk of trading online.

 FICO uses applied intelligence to analyze millions of data points pertaining to the person making an online transaction to determine whether the purchase is likely to be fraudulent or not. Data points such as transaction frequency, transaction size, and type of retailer all factor into the neural networks FICO has created to predict fraudulent transactions.

 Notice I said predict? FICO’s use of applied intelligence facilitates the recognition of fraudulent transactions before they even take place. That is the real power of applied intelligence, providing companies with actionable insights before the event, instead of trying to implement after-the-fact. Banks such as UBS save close to $200 million a year in fraud losses thanks to FICOs fraud prevention technology. 

 Applied Intelligence Represents the Future of Data

 There’s no doubt in my mind that applied intelligence will shape and mold the use of our future data. Businesses are already using it to automatically make decisions to help to deliver more positive experiences for customers and increase efficiency within operations. I recently wrote about how Lowe’s Home Improvement is overhauling their fundamental approach to customer experiences.

An excellent example of this occurred during my time leading the Big Data organization at Lowe’s Home Improvement. We were steadily closing the gap on the Home Depot by competing with them using future data. Through the creation of single views of the customer, product, and locational insights, we were able to predict customer shopping patterns and trends long before they became a reality both online and in the store.

 The businesses that are leading the applied intelligence charge are often those that were born in the digital age. However, it’s those who have a more traditional method of operating, like Lowe’s, that need applied intelligence the most. What’s ironic is that many of those companies sit on potential treasure troves of data, but consistently fail to make optimal use of it.  

 Manufactures, retailers with physical stores, health care providers, and financial institutions stand to gain the most from automated decision making based on artificially-enhanced analytics, with those that make a move to incorporate applied intelligence first benefitting the most. Unfortunately, the companies that fail to adopt this burgeoning solution will get left behind, trapped in a vicious cycle of trying to react to their ever-evolving environment, rather than accurately predicting what’s next.  

 

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