Ford drove the first automobile down the streets of Detroit in 1890. It would take another 30 years before the company streamlined production and made cars available to the mass market. The obvious lesson: Sometimes technology has a long gestation period before we can scale it for everyday use. But, digging a bit deeper, there is a more profound lesson.
Over the first hundred years of the self-propelled vehicle, manufacturers established essential building blocks — standard components like the combustion engine, steering wheel, and axle. These building blocks enabled scale, which led to wider adoption. And, as is often the case, the building blocks catalyzed complementary innovations, which then helped improve the building blocks.
Consider that in the first generation of vehicles (1750-1850), if a person wanted a means of transport, they had to design and fabricate every component. This “design” phase produced unique artifacts such as the Cugnot Steam Trolley, the first self-propelled land-based vehicle. Having gone through several additional phases, including build and repair, we’re now at a point in which we can pick out a car and drive it off the lot.
The evolution of the auto industry is similar in form to the currently nascent world of artificial intelligence. And like the auto industry, in order for AI to flourish, organizations must adopt and embrace a prerequisite set of conditions, or building blocks. For example, AI requires machine learning, machine learning requires analytics, and analytics requires the right data and information architecture (IA). In other words, there is no AI without IA. These capabilities form the solid rungs of what we call the “AI Ladder” — the increasing levels of analytic sophistication that lead to, and buttress, a thriving AI environment.
AI currently mimics and improves the human function; said another way, it brings human features to technology. In the consumer world, that is mimicking speech, vision, and daily interactions. In the enterprise, it mimics and improves enterprise functions, such as logistics, marketing, finance, operations, and HR. While it is similar in concept, the difference is as stark as the Cugnot Steam Trolley and a Tesla.
Enterprise AI is about solving sophisticated business problems in highly dynamic environments. This requires an understanding of well-defined use cases and starting points, as well as an acknowledgment that, per MIT professor Erik Brynjolfsson, “the bottleneck now is in management, implementation, and business imagination.”
Of course, the entry points for AI vary from organization to organization. In some cases, companies jump directly to the top of the ladder and adopt established AI technologies for specific use cases. But in many others, organizations begin to build out their enterprise AI environment by getting their IA in order.
To provide fluidity and avoid Brynjolfsson’s bottlenecks, organizations have three distinct foundational areas of technical advancement to embrace and exploit: hybrid data management, unified governance and integration, and data science and business analytics.
- Hybrid data management provides a platform to manage all data types across all sources and destinations. It incorporates all forms of data management (SQL, NoSQL) and all flavors of techniques (row store, column store, document store, Hadoop), collectively optimized with rich SQL access. An effective data strategy has to be singular in approach, and that starts with hybrid data management.
- Unified governance and integration provide the components that make data easily and securely available across cloud platforms. The process enables robust data preparation, policy creation, authorization, auditing, and more.
- Data science and business analytics combine to provide holistic and collaborative analysis of all data types, empowering people to economically extract meaning from and interpret complex data sets. Key components include analytics tools, advanced statistical models, and machine learning.
The data science and AI renaissance is flourishing because of digitization, the data explosion, and the transformative impact that machine learning has on data — namely, to enable the completion of new tasks. But while existing AI techniques give us greater insight, to get back to the automobile analogy, we still do not have self-driving cars. This is because building AI systems involves more than learning how to perform a specific task from data — it requires an infrastructure. Through the adoption of hybrid data management, unified governance and integration, and data science and business analytics, organizations of all sizes and all levels of understanding can begin to unleash the power of AI in the enterprise.
Rob Thomas is general manager at IBM Analytics.