The Success of Artificial Intelligence and Machine Learning Requires an Architectural Approach to Infrastructure

August 2018 //

Single graphArtificial intelligence (AI) and machine learning (ML) are emerging technologies that will transform organizations faster than ever before. In the digital transformation era, success will be based on using analytics to discover the insights locked in the massive volume of data being generated today. Historically, these insights were discovered through manually intensive data analytics—but the amount of data continues to grow, as does the complexity of data. AI and ML are the latest tools for data scientists, enabling them to refine the data into value faster.

In the past, businesses worked with a finite set of data generated from large systems of record. Today, there are so many more endpoints connected to a business, each generating its own set of data that needs be analyzed. For example, a decade ago, the concept of the Internet of Things (IoT) did not exist. Now, businesses are connecting new devices at a furious rate. ZK Research forecasts that by 2025, there will be 80 billion connected endpoints (Exhibit 1), each generating significant volumes of data. IoT isn’t needed for AI and ML, as many other data sources exist, but the addition of IoT accelerates the need for AI and ML. Given the difficulty companies have analyzing today’s volumes, it’s impossible to see how organizations will adapt to the upcoming explosion of ingested data. The only way to compete effectively is by using AI and ML.

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