TDWI Upside - Where Data Means Business

AI Techniques You Need to Know

Big data capture and management fuels AI possibilities.

For most of the last decade, you had to have a big data storyline in your pitch if you wanted to secure venture capital. Now artificial intelligence must be part of your pitch. Rather than viewing AI as a passing trend, you must see it as part of the accumulating trends in the Information Age.

For Further Reading:

Working AI into Your Enterprise Initiatives

When You're Ready for AI But Your Company Isn't

Artificial Intelligence Starts with Data

Artificial intelligence needs data and lots of it to work its wonders inside the organization.

The current abundance of data is what propelled AI to where it is today -- on a vaunted seat at the table of the future. The Internet of Things and augmented reality are leading disciplines in the data-generation process. With data volumes increasing at an accelerating pace, the more AI can learn about nuances and subtle factors in the human experience the better. With the proper set up and algorithms, AI has mastered games and activities at a remarkable pace. Now it's time to apply AI to enterprise data.

With more data, AI can detect and react to more nuanced and anomalous situations. Here are three common AI techniques you'll be taking advantage of in the future -- a near future if you have the data!

Regression

With regression, the past is used to establish trends and predict a nonlinear future. AI accuracy rises with more variables, data sets, and data describing the "full picture" of the circumstances surrounding the past it is evaluating. For example, detailed customer activity combined with characteristics such as a geography, product, and timing patterns can be used to find nonlinear phone and website patterns that, in turn, lead to the soft churn that leads to full churn.

Outlier Detection

Outliers that really matter can be incredibly difficult to detect, yet they are vital to understand. Only massive amounts of detailed data at AI's disposal can ensure a company excels at outlier workloads such as health monitoring, environmental change, and sensor anomalies. For example, measuring the four main vital signs of the body (body temperature, pulse rate, respiration rate, and blood pressure) regularly yields an enormous body of potential outliers against the innumerable maladaptive conditions of the body.

Relationship Data

By looking across an expanse of organizational data, AI can see the implication of activity across the entire organization. The more data it has, the more AI can discover previously unknown patterns and develop solid relationships among the data. A transportation company can discover relationships between departure times, fuel efficiencies, and customer satisfaction for example.

A Final Word

Big data capture and management fuels AI possibilities. The two are more than just correlated; AI is completely dependent on big data.

Wherever you are in your AI journey, think data first.

About the Author

McKnight Consulting Group is led by William McKnight. He serves as strategist, lead enterprise information architect, and program manager for sites worldwide utilizing the disciplines of data warehousing, master data management, business intelligence, and big data. Many of his clients have gone public with their success stories. McKnight has published hundreds of articles and white papers and given hundreds of international keynotes and public seminars. His teams’ implementations from both IT and consultant positions have won awards for best practices. William is a former IT VP of a Fortune 50 company and a former engineer of DB2 at IBM, and holds an MBA. He is author of the book Information Management: Strategies for Gaining a Competitive Advantage with Data.


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