What's next in Business Analytics?
By Rich Clayton, VP of Business Analytics & Big Data Product Group, Oracle
It is no wonder why analyst firm IDC states that by 2020 organizations that analyze all relevant data and deliver actionable information will achieve an extra $430 billion in productivity gains over their less analytically-oriented peers.
This particular moment is shaped by the remarkable confluence of several factors happening simultaneously. Each one not only impacting the others, but also extending to far-reaching corners of the enterprise in ways we had not yet considered. These five factors are:
• Rapid advancements in robotics and sensors, which are perhaps the most data gathering and data generating innovations we’ve experienced so far.
• A superabundance of data, both within and especially outside the enterprise, which will continue to grow at exponential rates.
• Impressive advances in computational capacity and cloud computing, which make it easier and more affordable for us to manage, visualize, share insights, and predict outcomes on all this data.
• A fast-growing base of digitally-engaged customers, who expect more meaningful connections from our businesses in every interaction and transaction; and,
• An increasingly connected workforce, with growing needs for immediate access and the power to be productive wherever they are, regardless of the type of device they use.
As a result, data is now managed as an asset and analytic platforms are an even more strategic investment in all types of organizations. So it’s only natural for us to ask what’s next for business analytics.
The answer is adaptive intelligence. Not to be confused with artificial intelligence, adaptive intelligence is the analytics layer for artificial intelligence and machine learning applications, and is the intersection of people judgment and machine automation.
While machines can ingest more data in 1 second than what people can in 10 years without forgetting it and without fatigue; and automation greatly simplifies repetitive computational deductive or inductive processes, we cannot replace human reasoning.
An adaptive intelligence platform powers the applications with a high performance infrastructure for running analytics at the scale required
The ability to understand and adjust analytic model inputs and training data, improve data imperfections, and apply ethics to our use and interpretation of data are a few examples of what machines can not completely replace.
This symbiotic relationship creates many new opportunities, as will be able to have a much improved real-time understanding of our business; greater ability to test ideas and hypotheses with unprecedented ease and speed; and much refined forecasting accuracy even as volatility and fast-changing conditions increase.
Artificial intelligence and machine learning solutions are based on algorithms that can learn from data without relying on rules-based programming. They can redefine how we interact with information and transform how we both work and live. For example, in the workplace they significantly impact how we formulate so-called “next best offers”; recruit top talent; prevent and detect fraud; automate supply chains; optimize supply chain finance; and boost data center efficiency, to name a few.
Outside of the workplace, they impact our personalized shopping experiences; our transportation with self-driving eco vehicles; how we manage wealth; and even our use of virtual assistants.
From a technology perspective, adaptive intelligence has three main areas: intelligent applications; an intelligent platform, and data itself.
Adaptive intelligence applications are a new category of continuously adapting, self-learning applications powered by enterprise data from transactional business applications, such as CX, ERP, SCM, HR, etc.. Their purpose is to derive insight without human bias and deliver smarter solutions to unique business challenges—with a very high degree of confidence and on a very large scale.
An adaptive intelligence platform powers the applications with a high performance infrastructure for running analytics at the scale required. It provides interactive data visualization capabilities to discover, narrate, and predict outcomes. It does not compromise between the need for analytic speed by business users, and the need to govern data access and preparation by administrators. It embeds a decision recommendation engine; and, most importantly, it ensures a seamless experience across devices (mobile, desktop) and deployment modes (on-premises, cloud, or a hybrid).
The third area of adaptive intelligence, data, consolidates many sources, whether internal or external to the organization to generate observations and training data used by the self-learning algorithms. It could include sensor data, with social data, and profile information from data exchanges. Data is the fuel that drive organizations towards automation, but most organizations lack a comprehensive data strategy; one that seeks to acquire, curate, combine and commercialize it.
Adaptive intelligent systems are not in the distant future. In fact, they’re here now. Their impact depends on how quickly and accurately we can take advantage of all this intelligence. Imagine the possibilities:
• If insights came to you when you need them most?
• If the content for your next operations review were 50 percent machine generated
• You didn’t have to search for data to support your business case but only ask the system some complex questions?
• You had an unbiased analytical advisor with you 24 x 7?
It is evident that these new types of machine learning technologies will raise the collective intelligence of people in all professions and socio-economic strata and locations. What is required from us, starting today, is to formulate an expansive data strategy, as well as assess and prepare our analytics platforms to capitalize on an adaptive intelligence world.