Trends and Outliers
TIBCO Spotfire's Business Intelligence Blog
Category Archives: Information Management
2013
5 Ways Predictive Analytics Help Insurance Agents, Carriers Work Together
What if you, Mr. or Ms. Insurance Agent, could predict the future so you could keep your best customers? And what if you, Big Insurance Carrier, could work with your agents to more accurately price risks?
Well, you can.
All you both have to do is focus on sharing data and using up-to-date predictive analytics to target risks in order to retain customers, according to this article in PropertyCasualty360.
Sharing data can help you and your carriers operate more efficiently around “pricing and underwriting; customer-retention strategies; finding good prospects; customer segmentation; and cross-selling among various lines of business,” according to the article.
Here are five ways agents and carriers can use predictive analytics to improve business:
1. Out With the Old and in With the New. In the past, predictive analytics solutions were so costly and so slow that carriers just didn’t want to “take on the challenge of working with agents individually to target business,” according to Wade Bontrager, the author of the article.
But the days of expensive, old-school analytics tools are long gone. Now, using modern predictive analytics, carriers can analyze more data and get the answers they need more quickly. And cloud-based predictive analytics solutions make it even easier and less expensive for carriers to share that information with brokers and agents.
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Filed under: CRM analytics, Information Management, Public data
2012
The 7 Most Important Sections of a BI Business Case
Almost one-third of all business intelligence projects fail to “meet the objectives of the business,” according to Gartner Research Inc.
Andy Hayler echoes that sentiment by noting that high failure rates are also associated with data management projects in general.
He contends that nearly every one of these failures can be attributed to the lack of a business case that should include, at minimum, information regarding the total cost of ownership, the potential risk of failure, and the benefits to the organization as a result of success.
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2012
How to Define Big Data
“Big data” is a popular term these days – it seems to pop up everywhere. But do people mean the same thing when they say those words?
In The Big Data Management Challenge, a recent report from Information Week, Michael Biddick provides a very useful description of what constitutes big data. He suggests there are four elements needed for data to qualify as “big.”
- The most obvious is size. A good point of demarcation is around 30 terabytes.
- Next is type. Structured data can be easy to work with even in very large amounts, whereas multiple data types (for example, structured, unstructured, plus semi-structured) can be challenging even when data sets are smaller.
- One of the most challenging elements is latency. “Really big” data typically changes fast.
- Finally, there’s complexity. Complex data may involve sparseness, inconsistency, and other atypical qualities.
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2012
Big Data Requires an Extreme Information Management Makeover
Many companies have been struggling for years to stay afloat amidst the deluge of data created by internal systems.
But now, big data or extreme data is pouring into organizations in higher volumes, faster, from a wider variety of data sources, and in more formats than ever before.
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