The World of Augmented Analytics: Q&A With Oracle Analytics’ Bruno Aziza

Oracle has identified a need for "augmented" analytics, leveraging machine learning and AI throughout the analytics process to help drive up the impact and value of data, and enable knowledge workers to uncover more insights. Recently, Bruno Aziza, group VP, Oracle Analytics, described this new phase in analytics, the role that cloud plays in making it possible, and what the capabilities will enable for customers.

DBTA: What are the challenges in analytics today?

Bruno Aziza, Oracle Analytics

Bruno Aziza: Despite a significant investment in business intelligence and analytics by organizations of every type, people are still not recognizing the value they expect for a number of reasons. For one, there is too much manual data preparation—in fact, many teams spend as much as 80% of their time on data prep, and only 20% on analysis. Similarly, there is too much ramp-up time required for understanding the data and its implications.  In other words, as more and more people engage in analytics, they find they need to hunt and peck for what’s in the data and what it means, instead of having an intelligent system to guide them to what’s important and where to start.

DBTA: What are the other issues?

BA: Another problem is analytics stopping before the last mile.  Many analytics implementations are envisioned as essentially newer versions of data warehouse and reporting solutions from yesteryear.  That vastly shortchanges what analytics can deliver.  Organizations that start with the end—their expected outcome, their desired change—and work toward that objective are much more successful than their counterparts. Taken together, these challenges are prohibiting organizations from realizing the true value of analytics.

DBTA: Oracle has identified a need for augmented analytics. What does that term mean?

BA: For Oracle, “augmented” means applying machine learning and artificial intelligence throughout the analytics process to assist knowledge workers in uncovering more insights more quickly and more easily than ever before. 

DBTA: How do AI, machine learning, and cloud enhance analytics?

BA: Cloud dramatically extends the reach of analytics by making world-class analytic power available to anyone, anywhere, in seconds.  Oracle Analytics’ flagship customers, Skanska and OUTFRONT Media, talk about paying for a subscription and having the service up and loaded with data in no time to deliver meaningful dashboards to stakeholders at the speed of business.  The same service then scales to thousands of users, offers high performance, security, and massive analytical horsepower to every team, regardless of size.  This level of speed and accessibility is new—as is the fact that it doesn’t take huge IT teams to maintain it. 

DBTA: And where do machine learning and AI technologies fit in?

BA: Machine learning and AI change the game by making it easier for everyone to get insights from data, which drives up the impact and value. Natural language processing opens up interaction with data to a much broader audience than ever before.   Even in an age of self-service, analysts continue to build content for the masses of business people to consume—a relatively passive approach to information distribution.   With natural language processing, those business people can ask questions of the data in a way that’s natural for them—and without having to learn how the BI tool works—and they can refine what they’re looking for.  Thanks to conversational interfaces, data can be accessed by voice or text input quickly and easily, wherever the business person is.   We also see chatbots as a vehicle to structure that conversation between business people and data, using natural language processing as the interface to retrieve information in context and deliver back results in a conversational mode.

In addition, natural language generation enables BI tools to create narratives from data so that trends, variances, and exceptions can be described as well as visualized.  The adage, “a picture is worth a thousand words” is often true, but for many people, there are a variety of ways to interpret that picture—in this case, visualization. Narration describes visualizations so there is no ambiguity about what they mean. 

DBTA: How is Oracle bringing augmented analytics to market?

BA: Oracle’s strategy is to apply machine learning and AI at every level of our solutions so that the entire experience is augmented. This includes automatically enhancing data as it’s being loaded and prepped; making automatic, intelligent recommendations for data prep; offering dynamic iterative data flow creation and management; and recommending datasets to enhance analysis. The strategy also includes using natural language processing and generation to explain attributes and how they relate to each other, guiding people on the best ways to investigate their information. Likewise, having natural language generation explain what people are seeing in their analysis helps to radically accelerate understanding and remove ambiguity.

DBTA: The entire experience is augmented.

BA: Yes, from identifying meaningful data to work with, prepping it, and creating insights from it that can be easily shared.  These capabilities are available across the Oracle Analytics solutions.  They are equally available when Oracle Analytics solutions are embedded in daily workstreams so users can make informed decisions and take meaningful action in context. Additionally, this strategy is in play across Oracle’s entire solution set.  Machine learning and AI are embedded in the database and data warehouse solutions, as well as applications, helping Oracle’s product lines run autonomously and offering development platforms to create, use, and operationalize algorithms. 

DBTA: Why is it needed now?

BA: Augmented analytics is the only way to make sense of two hyper-growth trends:  data and the off-the-charts increase in volume, velocity, and variety; and analysis by all with everyone now a knowledge-worker, whether they even know or like it, or not.  These factors continue to accelerate and put pressure on people and information systems to handle them.

DBTA: Where is there a need for augmented analytics?

BA: Everywhere—in the information consumption experience, in the application experience, and in the administration experience; and in every industry, at every level—from individuals and small teams to global companies.  Whether you’re maintaining systems or processing transactions or consuming insights, augmented analytics lets you interact in a way that adapts to how you work, not how the system is architected to operate.  We believe augmented analytics in this regard will become like other systems that we rely on, such as power, water, and communications. It will become so fundamental to our experience that in the future we will notice it when it’s missing, rather than observing when it’s there.

How does it help enhance business processes?

BA: It gets to answers quickly and pinpoints what factors are influencing what’s really happening. For example, if you understand that the drivers of profit are not product cost, but delivery and transportation charges, you’ll be able to focus your efforts there, without tinkering without taking action where none is needed.  It also helps explain what people are seeing and the implications as well as recommending actions, which is critical, as many people still say they aren’t sure what action to take based on their analysis.

DBTA: In your view, what is Oracle seeking to provide that is missing in the market?

BA: While we think that people will always explore and investigate data, our view is that people don’t come to work to do analytics but rather to get insights that drive decision making and innovation.   To that end, we intend to provide a holistic augmented analytics experience, so that every aspect of analysis is automated where it can be, intelligently enhanced, and accessible without limits.  This experience is not available in the market today, and other vendors still tend to focus on the act of analysis—queries, visualizations, the feature race—rather than focusing on insights that drive innovation.  We are focused on the outcome, at scale, so we can empower everyone to be an excellent analyst, even if they don’t call themselves that.

DBTA: In 5 years, what do you expect the impact of AI and machine learning will be on analytics?

BA: Well, 5 years is a relatively long time horizon. Gartner identified augmented analytics to be the number-one data and analytics technology trend for 2019. According to Gartner, by 2020, augmented analytics will be a dominant driver spurring new purchases of analytics and BI, as well as of data science and ML platforms, and of embedded analytics. The number-two trend highlighted by Gartner is augmented data management. Gartner states, that through the end of 2022, data management manual tasks will be reduced by 45% due to the use of ML and automated service-level management.

Interview has been edited and condensed.