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TDWI Upside - Where Data Means Business

Autonomous BI and Analytics: Are We There Yet?

Are BI and self-service analytics headed inevitably toward more automation?

This is the dawning of the Age of Autonomy -- not Aquarius, as some baby boomers might recall nostalgically. Artificial intelligence (AI) is or will soon be making its debut in manufacturing, energy plants, industrial kitchens, hotels, hospitals, homes, and on the roadways. Self-driving cars are developing rapidly, with the pace likely to pick up as technology providers and automotive companies begin to work more closely together rather than face off as rivals.

Automation and Learning Algorithms

The disruptions brought on by autonomous vehicles and intelligent robots will put pressure on makers of business intelligence, analytics, and data management solutions and platforms as well as the professionals who work with them. To be faster, smarter, and more responsive, these technologies must also become more autonomous.

For Further Reading:

To Be Successful with Machine Learning, Expect to Learn As You Go

5 Ways to Add Cognition to Your BI Program

Preparing Your Company for Machine Learning

Never one to miss a beat, Oracle's executive chairman and chief technology officer Larry Ellison played on the buzz about self-driving vehicles in his keynote at Oracle OpenWorld 2017 in early October. He announced Oracle Autonomous Database Cloud, a "totally automated, self-driving database."

Ellison said that the database and companion automated security system, to be ready this year, will use machine learning (ML) to "eliminate human error" that slows down performance and will reduce the need for manual tuning, thereby lowering labor costs. Oracle said that its database management system (18c) will automatically tune itself based on ML and can resize its computer and storage use on its own.

Almost nothing is hotter today than machine learning. It is an application of AI that enables practitioners (primarily data scientists) to apply programs and algorithms that teach computers to learn from the data and, using rule-based and AI methods, take action automatically.

Oracle, Amazon, IBM, and many other technology providers are racing to build ML algorithms into their solutions with the goal of becoming smarter, faster, more scalable, and automated. This is particularly important in the competition for cloud-services customers who want flexibility and performance and do not want to hear reasons why they can't have it. If they are not satisfied, they will just switch to a competitor.

Autonomous BI and Self-Service Analytics

Are BI and self-service analytics headed down the same path? Certainly, to support ease of use, these solutions have become more automated internally than their enterprise BI predecessors. Many solutions today compete on how well they enable users to work at a higher level of abstraction, including with drag-and-drop visualization, so that users do not have to write code to pose queries.

Some solutions are incorporating natural language processing (NLP) to enable users to compose queries in everyday language. In vogue at many of the past year's user conferences have been demos where users speak queries into a device, similar to Amazon Alexa, Apple Siri, or Microsoft Cortana intelligent personal assistant (or "digital agent") applications.

It's uncertain whether BI and analytics truly lend themselves to that level of abstraction. At Looker's JOIN user conference in September, Colin Zima, Looker's chief analytics officer and VP of products, cast doubt on the applicability of voice command queries. He noted that a manager asking, for example, "When did that customer churn?" would be frustrated by the experience.

Analyzing data about customer churn involves questions leading to more questions, understanding the context of the queries, and so on. That is beyond what an Alexa or Siri would understand. Looker and other solution providers do facilitate prebuilt queries and access so that the path to query satisfaction is quicker. Looker, however, assumes that most users will want to explore the data further, including by writing queries using the company's LookML language for accessing SQL databases.

Although some BI and self-service visual analytics vendors are beginning to embed ML and other AI capabilities for querying, these technologies are more rapidly becoming part of data preparation, cataloging, and metadata management. Companies such as Paxata, Alation, and Trifacta are using ML to enable their customers to explore, profile, and derive knowledge from big data so that they are not limited to working with samples and aggregates. These solutions help organizations understand data relationships across many sources for security, marketing, risk management, and other business purposes.

AI, Advanced Analytics, and Emerging Technologies

TDWI's Conference> and Leadership Summit, coming up in Orlando December 3-8, 2017 will offer numerous sessions focused on the next wave of BI, analytics, data warehousing, and data management, including the influx of ML and AI technologies.

Shala Arshi, senior director of technology enabling at Intel Software and Services Group, will present a case study at the Leadership Summit about how Intel is using AI to enhance actionable insight. Conference sessions will address how to apply new technologies to modernize data warehouses and develop a coherent strategy for incorporating new technologies into your data strategy.

Exciting developments lie ahead as AI becomes a bigger part of BI, analytics, and data management. However, organizations need to make prudent decisions and not be tempted by silver bullets that offer a lot of hype but not enough benefit.


About the Author

David Stodder is director of TDWI Research for business intelligence. He focuses on providing research-based insight and best practices for organizations implementing BI, analytics, performance management, data discovery, data visualization, and related technologies and methods. He is the author of TDWI Best Practices Reports on mobile BI and customer analytics in the age of social media, as well as TDWI Checklist Reports on data discovery and information management. He has chaired TDWI conferences on BI agility and big data analytics. Stodder has provided thought leadership on BI, information management, and IT management for over two decades. He has served as vice president and research director with Ventana Research, and he was the founding chief editor of Intelligent Enterprise, where he served as editorial director for nine years.

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