Data analytics might start with numbers, but it’s a craft dedicated to uncovering and telling stories. And while that sounds simple, it belies the exacting technical journey that data must travel before it can tell its tale.

Now, a raft of added capabilities and enhancements in Oracle Analytics Cloud (OAC) simplify that journey and offer new ways to learn from data using the power AI, machine learning, and natural language processing.

“The Oracle Analytics team has been hard at work on capabilities that power the entire analytics workflow,” for everyone from business analysts, to data scientists, and data engineers, says T.K. Anand, senior vice president of Oracle Analytics. The new features cover tasks including connecting to a data source; transforming and preparing the data; modeling the data with relationships, hierarchies, and metrics; and exploring the data to find insights.

The enhancements go beyond exploring data to helping people use information to take action. These include new ways of sharing data in infographics or data stories, so people can create custom visual experiences with little or no code. “One of the important principles behind instilling a data-driven culture is the ability to engage your audience with visually compelling data experiences,” Anand says.

Oracle Analytics Cloud brings all these capabilities together in one secure platform that can operate on all your data wherever it may reside. “That includes data from applications and databases that run in Oracle Cloud, in third-party clouds, or even in on-premises systems,” Anand says. In addition, Oracle Analytics Server (OAS) lets companies have the capabilities of Oracle Analytics Cloud while operating inside their own data center. “If you’re ready to move to the cloud, you can migrate your Oracle Business Intelligence (OBI) instance to OAC. But if you need to stay on-premises, there’s a fast upgrade from OBI to OAS that means you can benefit from all the innovation in OAC over the past many years,” Anand says.

The competitive edge

Thousands of enterprise customers have already benefited from Oracle Analytics Cloud. For example, the UK’s National Health Service (NHS) has saved more than £1 billion by using data analytics to identify inefficiencies and fraud and optimize patient treatments. Likewise, the UK’s Home Office cut their recruitment time by one-third through the insights they gained into their hiring process. And FedEx was able to create a data-driven culture by deploying modern analytics to more than 20,000 business users.

These are examples of “folks who are innovating with OAC in their organizations and creating a data-driven culture,” says Anand. Around them Oracle has built a Global Leaders program, through which “they provide valuable feedback to the product team that helps us with our roadmap. And most importantly, they share their learnings with each other and help build a vibrant Oracle Analytics community across the globe.”

Here are some examples of the recently added features that will help businesses learn more from their data:

• Explainable machine learning: Any user can now see simple explanations of the factors that influenced why a machine learning model predicted a certain outcome. For example, of all the factors that influenced the denial of a bank loan application, employees using OAC can quickly see which were the most determinant and why.
• Automated data preparation: A data profiling engine samples and scans data to identify and proactively prompt users about potential data quality issues, such as suggesting the obfuscation of sensitive credit card information. It can add value other ways, such as enriching zip codes with city, population, income, ethnicity, and payment data to provide more in-depth location analysis.
• Text analytics: Text analytics lets analysts extract words from unstructured data, count them, visualize the results, and then join that analysis with the original data. For example, sentiment analysis uses text analytics to determine whether comments are negative, positive, or neutral. This can, for example, help better understand how a product launch is performing based on text in surveys or social media.
Video: T.K. Anand, senior vice president, Oracle Analytics, discusses how Oracle Cloud is leading the third wave of data analytics.

• Affinity analysis: This feature discovers relationships in data by identifying sets of items that often appear together. For example, using market basket analysis, retailers can look at popular combinations to better manage store layout, coupon offers, and cross-selling. It’s also valuable for direct marketing and sales promotions.
• Graph analytics: Graph analytics show data relationships visually, such as how people and transactions are connected, or what the shortest distance is between two hubs in a network. This has powerful applications in a variety of domains, from social media to security and compliance. Marketers can use it for ranking and measuring the importance of website pages.
• Custom map analytics: Map analytics gives users the ability to apply custom images as map backgrounds and create map layers to enhance data visualizations. For example, doctors can visualize data on an image of the human body to identify areas that require attention and visually track the impact of medication or other treatments. Customers can bring in map information, such as weather and building schematics, and easily present it with their business data.
• Oracle Analytics mobile app: The new Oracle Analytics mobile app lets users find data quickly and easily, all with a consistent user experience across Oracle Analytics Cloud and the app. It lets users interact with data visualizations, explore dashboards, and share information across teams for further collaboration. Users can also listen to natural-language–generated audio narratives of the most salient points from reports, dashboards, and visualizations.
• Natural language processing: Oracle Analytics Cloud allows users to query their data in natural language using a simple, search-like experience—using text or voice—and obtain spoken narratives of the results. It supports 28 different languages and various language constructs, such as synonyms, abbreviations, dynamic filters, and calculations. Users can type, text, or speak aloud to ask business questions, such as, “what’s our employee churn this month?” and get an employee attrition dashboard in return.

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Illustration: Wes Rowell