By Bill Schmarzo, CTO, Big Data Practice of EMC Global Services
What is the Intelligence Revolution equivalent to the 1/4” bolt?
I asked this question in the blog “How History Can Prepare Us for Upcoming AI Revolution?” when trying to understand what history can teach us about technology-induced revolutions. One of the key capabilities of the Industrial and Information revolutions was the transition from labor-intensive, hand-crafted to mass manufactured solutions. In the Information Revolution, it was the creation of standardized database management systems, middleware and operating systems. For the Industrial Revolution, it was the creation of standardized parts – like the ¼” bolt – that could be used to assemble versus hand-craft solutions. So, what is the ¼” bolt equivalent for the AI Revolution? I think the answer is Analytic engines or modules!
Analytic Modules are pre-built engines – think Lego blocks – that can be assembled to create specific business and operational applications. These Analytics Modules would have the following characteristics:
The BCG Insights report titled “Winning in IoT: It’s All About the Business Processes” highlighted the top 10 IoT use cases that will drive IoT spending including predictive maintenance, self-optimized production, automated inventory management, fleet management and distributed generation and storage (see Figure 1).
But these IoT applications will be more than just reports and dashboards that monitor what is happening. They’ll be “intelligent” – learning with every interaction to predict what’s likely to happen and prescribe corrective action to prevent costly, undesirable and/or dangerous situations – and the foundation for an organization’s self-monitoring, self-diagnosing, self-correcting and self-learningtwoIoT environment.
While this is a very attractive list of IoT applications to target, treating any of these use cases as a single application is a huge mistake. It’s like the return of the big bang IT projects of ERP, MRP and CRM days, where tens of millions of dollars are spent in hopes that two to three years later, something of value materializes.
Instead, these IoT “intelligent” applications will be comprised of analytic modules integrated to address the key business and operational decisions that these IoT intelligent applications need to address. For example, think of Predictive maintenance as comprised of an assembly of analytic modules addressing the following predictive maintenance decisions including:
As I covered in the blog “The Future Is Intelligent Apps,” the only way to create intelligent applications is to have a methodical approach that starts the predictive maintenance hypothesis development process with the identification, validation, valuing and prioritizing of the decisions (or use cases) that comprise these intelligent applications.
Read the source article in Data Science Central.