Digital transformation is the key imperative in the corporate suite of most forward-thinking enterprises. IDC coined the term Digital Darwinism to reflect the impact of digital transformation on businesses of all sizes and across industries.  According to IDC, organizations are moving away from business as usual and embracing digital transformation to become more competitive.

 

Key components of enacting digital transformation are the applied sciences of artificial intelligence, machine learning, deep learning, and prescriptive analytics, the creation of computational systems that allow autonomous decision making. Through prescriptive analytics, organizations will redefine how business decisions are made. Prescriptive analytics can take many forms, including autonomous data center functions, automated stock transactions, critical health support systems, intelligent traffic flow pattern optimization, power generation automation, autonomous cars, and even, eventually, autonomous planes. NetApp believes that the fundamental element of all these capabilities is the access, organization, management, and understanding of highly distributed data.

 

At least since the first century BCE, humans have been consumed with the problem of creating machines that mimic the human brain. In modern times, the term “artificial intelligence” was coined in 1955 by John McCarthy. In 1956, McCarthy and others organized a conference titled the Dartmouth Summer Research Project on Artificial Intelligence. This simple but profound beginning led to the creation of machine learning, deep learning, and predictive analytics, and now to prescriptive analytics. It has also given rise to a whole new field of study, data science.

 

Let’s examine these terms.

 

Artificial intelligence is the basis for mimicking human intelligence processes through the creation and application of algorithms built into a dynamic computing environment. Applications of AI can be seen in everyday scenarios like financial services fraud detection, retailer purchase predictions, and online customer support interactions.

 

A subset of AI, machine learning is the area of computation science that focuses on analyzing and interpreting patterns and structures in data to enable learning, reasoning, and decision making outside the construct of human interaction. Typical examples of machine learning include traffic pattern recognition and routing, data security and threat detection monitoring, and natural language processing.

Deep learning is a subfield of machine learning that mimics the construct of human neural networks and applies it to computational analytics. In practice, computational nodes are created and networked, with each node specializing in specific aspects of the learning process, mirroring how humans ingest, analyze, and decipher information and use it to make decisions. One application for deep learning is advanced credit monitoring. Although many businesses use credit scores as a determination of credit worthiness (for example, to purchase a car or a house), deep learning can be applied to understand and synthesize additional inputs into the decision process.

 

For example, deep learning might pull in your social media habits (is the person exhibiting behavior outside their norm?); location-based housing trends (is the street location value decreasing, are people improving their homes or pulling money out of them?); work industry trends (is the person’s job stable or undergoing downsizing?); are the person’s personal relationships healthy? and so on. The only limits to deep learning are access to the data and computational power. Another application of deep learning is the ability to sort through digital medical images such as MRIs and X-rays to provide an automated second opinion for doctors to use in diagnosing patients.

 

Deep learning is the next frontier in autonomous decision making. The next evolution though is prescriptive analytics. Making decision without human interaction.

 

Prescriptive analytics takes the output from machine learning and deep learning to predict future events (predictive analytics), and also to initiate proactive decisions outside the bounds of human interaction. An autonomous car transports you safely to a destination that you determine. It selects a route based on current data (traffic congestion, route optimization). In contrast, an autonomous car equipped with prescriptive analytics transports you to a location based on deep learning analytics. This could be as simple as to the grocery store based on dinner plans pulled from your social media channels or to your doctor based on external inputs (personal device based heart rate monitor, social media input, proximity tracking) that recognizes that you have been exposed to a flu virus and will require medical care. A simpler example of prescriptive analytics can be seen today when an enterprise application is autonomously moved from one server to another (or even to another availability zone) based on predicted imminent failure of the server (or data center). Prescriptive analytics is the final frontier of computational intelligence. It will be incorporated into a host of applications, including stock market transactions, health and prescriptive medical diagnosis and treatment, design manufacturing, and power generation.

 

As the data authority, NetApp understands the value of the access, organization, management, and control of data. We have adapted the principles of computational intelligence into our solutions like NetApp® OnCommand® Insight, an open platform for on-premises and hybrid cloud data center management, and Active IQ®, a predictive analytics and proactive support application for the hybrid cloud. OnCommand Insight implements machine learning to constantly analyze and provide consistent insight across your data center so you can monitor and manage your hybrid IT multivendor storage, compute, and networking infrastructure. Active IQ builds on the NetApp AutoSupport® set of predictive technologies with artificial intelligence, powerful machine-learning capabilities, and additional deployment options. Core to all these solutions is NetApp’s Data Fabric vision. Data Fabric gives organizations of all sizes the ability to accelerate critical applications, gain data visibility, streamline data protection, and increase operational agility.

 

Want to know more about Active IQ? Then you’ll want to check out this interview with Sudip Hore.

 

Michael Elliott

Michael Elliott is NetApp’s Cloud Evangelist and thought leader focusing on data center evolution with particular emphasis on private and hybrid clouds. Michael started his career as a mainframe programmer for General Electric and held the role of adjunct professor of marketing at the University of Akron. Michael has a mathematics degree from the University of Cincinnati and an MBA from Pennsylvania State University.