As companies begin to understand the vast potential of their data, the question they face is: How does our business make the most of it? The answer lies in getting real-time insights that enable better business decisions and accelerated product development. But what if the insights were used not just by humans, but by the systems themselves, leading to ongoing optimization at previously inconceivable speed and accuracy? That’s the promise of adaptive intelligence (AI) and machine learning, which are already impacting consumer experience with personalized shopping, self-driving vehicles, online wealth management, and virtual assistants. Here, we’ll look at how data is driving the coming AI revolution.
In its most basic sense, human intelligence is the ability to make decisions based on observation and experience. Artificial intelligence, then, is the same capacity in machines, and potentially as vast and complex. Here at Oracle, we prefer to focus on a subset of AI called adaptive intelligence. Adaptively intelligent apps consume streams of raw data from multiple sources—such as customer experience, enterprise resource planning, supply chain management, and human resources—to develop predictive analytics. This is an ongoing and self-correcting process, so that these analytics are continually refined and adjusted to reflect changing input.
The key, of course, is high-quality data—and lots of it. “Data is the fuel that drives organizations towards automation,” Rich Clayton, vice president of Oracle’s Business Analytics Product, recently wrote. “But most organizations lack a comprehensive data strategy, one that seeks to acquire, curate, combine, and commercialize it.”
Chuck Hollis, senior vice president for Oracle’s Converged Infrastructure group, uses the example of hiring to explain how machine learning and adaptive intelligence work. In this case, the enterprise data being leveraged includes a complete history of all candidates selected and hired, their key attributes, how they were on-boarded once hired, and their eventual performance in the organization. An analysis engine extracts key features that contributed to candidates’ success and creates a recommendation engine that can rate new applicants along their likelihood to thrive at the organization.
Simple data analytics, right? Yes, except that the algorithms, rather than people, decide which factors matter and which do not. Furthermore, the system continually processes ongoing results of those candidates, updating its recommendation engine rules over time. The system learns from actual experience, just like humans do. But it does so far more rapidly and objectively.
“Now, extend this capability to other high-value, high-frequency business processes,” Hollis writes. “Timing and pricing of supply chain purchasing. Negotiating discounts on large orders. Measuring the temperature of your customers to determine when a small issue might become a big one. Today’s AI-informed recommendations become tomorrow’s advanced automation.”
All this is well and good, but how do you bring AI capabilities to bear as they become available—and how do you set the right foundation for future implementation? Oracle director of content strategy and implementation Margaret Harrist, who specializes in digital disruption, Big Data, and IoT, offers a three-step process.
1. Start with business outcomes. Data brings value to the organization in two ways: by driving top-line revenue and bottom-line profitability. You can achieve any range of business outcomes using data—for example, increase revenue by helping to target customers more accurately with the right product at the right time, create entirely new markets with disruptive technology (think Amazon or Uber), or grow profitability by making operations increasingly efficient. Once you know what business outcome your organization must pursue, you can determine what data you need to collect and what questions you need it to answer.
2. Unlock data silos. One law of the data capital economy is that the more freely data flows, the more value it has. But in many organizations, data is typically stored and utilized in functional or operational silos. There’s marketing data, sales data, supply chain data, HR data, and so on—and these functions are highly interdependent. Data from one influences outcomes in another. Organizations need a single, shared repository of data to fully unleash value. A cross-organizational ERP system is a great start, but fully integrating ERP with CX, HCM, and SCM is even better.
3. Modernize your IT infrastructure. Look for solutions that have AI embedded right into the infrastructure services. According to Mark Hurd, in an interview to CNBC, "Historically in our industry, we used to extract data from an application, send it somewhere, let people do magic with it...and then send it back." However, the best approach for AI "is really getting it integrated and embedded into the services themselves." For instance, this is the approach Oracle takes with the Oracle Management Cloud, which leverages machine learning and big data techniques to provide next-gen monitoring, management and analytics cloud services.
Falkonry is on the leading-edge of industrial transformation. The company’s software uses machine learning and pattern-recognition AI to improve its customers’ operational efficiency by analyzing volumes of time-series data.
Falkonry relies on Oracle infrastructure as a service (IaaS) to give its customers rapid and cost-effective access to artificial intelligence. Oracle IaaS provides Falkonry’s customers across a variety of industries with high-performance, high-availability, and cost-effective infrastructure services. By allowing companies to solve previously unapproachable problems in dramatically shorter time frames—days or weeks instead of years—Falkonry draws tremendous value from its data.
Make no mistake: The data capital economy is here to stay, and enterprises across multiple industries are using AI to tap their vast data wealth and create competitive advantage. To learn more about how your company can flourish in the data capital economy, read the MIT and Oracle collaboration on the “The Rise of Data Capital.”