Enterprising Developments

More Analytics Means More ROI

Joe McKendrick
Insurance Experts' Forum, June 6, 2012

It's the long-sought holy grail of the decade for insurance companies: to be able to “compete on analytics,” by having access to boatloads of data that can be quickly converted into actionable information that helps executives—and machines—make the right decision at the right time.

In a recent report, Nucleus Research outlines the four stages of evolution that bring most organizations to the brink of an analytics-driven culture. The good news is that as the organization advances to the next stage of analytics, ROI dramatically increases for many reasons. For example, the deeper a company moves into analytics, the more employees’ work practices improve “as they increasingly embrace analytics as a way to make better decisions and incorporate more data into their analyses." Of course, decision making also improves, “as analytics is embedded into more processes and enables employees to base their conclusions on data rather than intuition.”

The four stages of analytics identified by Nucleus include the following:

Automated: At this stage, enterprises “use analytics to automate report building and achieved benefits that included increased productivity for data analyzers and reduced workloads for IT departments. Data management capabilities at this stage typically included the construction of data warehouses and data cubes.” Average ROI: 188 percent.

Tactical: Here, organizations start using analytical solutions “to improve decision making, rather than just increase productivity. Tactical users of analytics typically have expanded their data management capabilities to include data migration, data integration, and better data quality control.” Average ROI: 389 percent.

Strategic: At this stage, enterprises have analytics strategically deployed across most of the organization, “and used analytics to align daily operations with the goals of senior management. Strategic analytics organizations typically have advanced data governance tools and practices.” Average ROI: 968 percent.

Predictive: Finally, the deployment of predictive analytics “achieve higher returns by tapping into what is commonly referred to as 'big data.' Such deployments “also reach beyond the traditional limits of internal enterprise data to the Web, customers, vendors, and partners,” says Nucleus. Average ROI: 1,209 percent.

(Thanks for Herman Mehling for surfacing this study.)

Joe McKendrick is an author, consultant, blogger and frequent INN contributor specializing in information technology.

Readers are encouraged to respond to Joe using the “Add Your Comments” box below. He can also be reached at joe@mckendrickresearch.com.

This blog was exclusively written for Insurance Networking News. It may not be reposted or reused without permission from Insurance Networking News.

The opinions of bloggers on www.insurancenetworking.com do not necessarily reflect those of Insurance Networking News.

Comments (0)

Be the first to comment on this post using the section below.

Add Your Comments...

Already Registered?

If you have already registered to Insurance Networking News, please use the form below to login. When completed you will immeditely be directed to post a comment.

Forgot your password?

Not Registered?

You must be registered to post a comment. Click here to register.

Blog Archive

The Other Auto Insurance Telematics Shoe Drops

Progressive's decision to charge Snapshot drivers more if their driving data indicates higher risk has started the industry down a road of data-driven adverse selection.

Core Transformation – Configuring in the Rain

The whole point of core transformation is that changes at the micro level can be used as a stimulus for changes at the macro level.

6 Ways to Develop a Productive IT-Business Dialog

Relationship management 101 for keeping IT and business on the same page.

Unified Digital Strategy: Succeeding in the Digital Revolution

A unified digital strategy recognizes that all business strategies and technologies touch the customer in some way and that a one-size-fits-all channel model is obsolete.

Agile and Continuous Delivery in a Regulated Environment

Just because a development team is doing continuous delivery or packaging releases into two-week sprints doesn’t mean that code is being moved to production.

Dealing with the COBOL Brain Drain

Documentation on aging systems often is akin to tribal knowledge, and the potential for things to go bump in the night increases as these environments face generational transition.