02. Digging Deeper on Machine Learning

Machine learning can be as varied as the data itself, from text to image analytics to a widening array of sensor based data. The good news is that tools are advancing rapidly, and many are open sourced.
 

03. Talent and Tools

Tools to work with big data are becoming more accessible, but users need to understand what they're doing. That's where learning and experience take analysis to the levels where discovery occurs.
 

04. Don't Jump Into the Deep End

Start with a small project and solid data. Learn and build from there.
 

05. The Strategic Value of Pre-Filling Data

The speed of AI and its ability to retrieve information quickly means its a great tool in easing the path for prospective insureds.

 

06. Valuable Tools But No Magic Ponies Here

Estimates of the transformative power of artificial intelligence and machine learning are running high, likely higher than most organizations' ability to harness the technologies.

 

07. AI and ML Work with Moving Pictures, Not Snapshots

Time is often a missing element in data sets. It's helpful to know a certain metric, but it's even more valuable to know how that's changed over time, and the direction of change.
 

08. Data Rarely Comes Machine-Ready

In the ramp-up to utilizing artificial intelligence and machine learning, some make the mistake of assuming that all data is equal, and that all data is equally usable.

 

09. Working Within Regulatory Constraints

Regulators have become increasingly interested in what data organizations collect and retain about individuals, how that data is protected and ultimately whether that data is used appropriately.

 

10. Getting Started With Data Analytics Is an Investment, Not an Expense

Venturing into artificial intelligence and machine learning means bringing in new skills and personnel, but ultimately it may be less costly than many large-scale technology projects.

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