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Data Management

Insurance Certification Options for Data Analytics

Amy C. Waninger · 2019-04-16 · 2 Comments

Are you an insurance professional looking to boost your résumé with new credentials? Consider a new insurance certification in data analytics.

Why Data Analytics?

You’ve probably heard a lot lately about big data, predictive modeling, machine learning, and artificial intelligence. These concepts are all possible because of the enormous amounts of data being produced every day. The data come from consumers’ browser history, smart devices, and data in the public domain.

At the same time, computing power has continued to scale at an astonishing rate. New hardware, software, and cloud computing technologies are evolving rapidly.

As a result, a new discipline has emerged: data science.  Jobs that didn’t exist five years ago are now changing the business of insurance. You won’t want to be behind the curve on these new concepts. Now is the time to start learning about insurance data analytics!

The Institutes’ new AIDA insurance certification

The Institutes offers an introductory-level Associate in Insurance Data Analytics (AIDA) designation.  This program provides an overview of statistical concepts, loss triangles, and other fundamentals.

The courses are:

  1. Big Data Analytics for Risk and Insurance
  2. Risk and Insurance Analysis Techniques

Recently, I earned the AIDA designation from The Institutes. I found it valuable in understanding the work of the data science, actuarial, and modeling teams I support. One of those data scientists is now pursuing this designation as well. She believes it will help her tie her analytics experience to the work she is expected to do within the insurance industry.

Insurance Data Management Association (IDMA)’s insurance certifications

The Insurance Data Management Association (IDMA) offers an introductory course and two levels of certification for insurance professionals.

IDMA offers a standalone introductory course, Data Management for Insurance Professionals. This course offers an overview of the data issues insurance companies face. The course is an inexpensive, low-commitment option to help you get started. It does not, however, apply as credit toward the certification program.

If you want to learn more — and add an insurance certification to your résumé — consider their four-course designation path. The courses are:

  1. Insurance Data Collection and Reporting
  2. Insurance Data Quality
  3. Systems Development and Project Management (waived if you have the Associate in Technology (AIT) designation from The Institutes)
  4. Data Management, Administration, and Warehousing

Students can earn the Associate Insurance Data Manager (AIDM) designation with no prior coursework. The Certified Insurance Data Manager (CIDM) designation is available for students who have completed additional coursework from other institutions.

I recently completed the CIDM certification. Thanks to the AIT waiver, I only needed to complete two more courses to add CIDM to my credential list. Full disclosure: I did fail one of the exams on the first try. Nobody’s perfect!

New insurance certification options from Casualty Actuarial Society (CAS)

Finally, Casualty Actuarial Society (CAS) now offers a Certified Specialist in Predictive Analytics (CSPA) designation and will soon launch a Catastrophe Modeling certification.

While I listed them here, I’ve not yet explored these insurance certification options personally. My colleagues in the actuarial discipline, however, were very interested in learning more.

Additional Reading on Data Analytics

If you don’t want to commit to a certification, consider picking up one of these books to learn more. In particular, I recommend Predictive Analytics by Eric Siegel.

#whatimreading Big Data by Bernard Marr

Amy C. Waninger · 2017-06-21 · Leave a Comment

Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance seems to be less about data and analytics and more about providing structure to strategic planning and operations management.  Marr’s “SMART” process is the crux of his text, and mirrors the measure-manage-optimize process I’ve seen elsewhere, with a slightly different focus:

  1. Start with strategy
  2. Measure metrics and data
  3. Apply analytics
  4. Report results
  5. Transform business

There is some great guidance on cataloging data sources, which will come in handy in my day job.

Marr also provides tips on data visualization and infographics:

  • Identify your target audience
  • Customize the data visualization
  • Give the data visualization a clear label or title
  • Link the data visualization to your strategy
  • Choose your graphics wisely
  • Use heading to make the important points stand out
  • Add a short narrative where appropriate

He follows this with an effective distillation of Edward Tufte’s guidelines for visualizations. See also:

    • The Visual Display of Quantitative Information
    • Envisioning Information
    • Beautiful Evidence

How are you using data to drive strategic and operational decisions?

More Reading on This Topic

Communicate with Pictures: Slide:ology by Nancy Duarte

Amy C. Waninger · 2017-06-21 · Leave a Comment

Communicate Clearly and Stand Out

Communication is an important skill in almost every type of work. Technical reports and business proposals frequently include data to support findings or recommendations. Most people struggle to communicate visually compelling stories from data. You can stand out from the crowd if you learn to convey complex information in a meaningful way.  Nancy Duarte‘s book Slide:ology offers clear, actionable steps to transform your illustrations and improve the way you communicate.

Putting Slide:ology into Practice

Reading Slide:ology, by Nancy Duarte helped me improve my design and communicate more clearly.

Background Information

These graphics are based on the work of a user support team I managed. We started with a number of disparate processes that were impossible to measure. Our solution was to centralize our intake through a call center with issue-tracking software.

Communication Goals

The goal of this presentation was to highlight our significant improvements to customer service. The data was on our side, but we still had some vocal critics of the new process.

Specifically, we needed to:

      1. Show that we were resolving 80% of issues within two days.
      2. Give customers a clear reason to contact us by phone rather than by email
        Need to communicate call-to-action
        Before: This version shows our service level, but fails to reinforce our call-to-action.

        clearly shows that calls get resolved faster
        After: This version calls out the disparity in effective service levels between calls and online tickets.
      3. Show that we were using the data we collected to improve the process over time
        visually cluttered
        Before: Lots of “Chart Junk,” as Edward Tufte would say.

        Concise communication
        After: Does this tell the same story more simply, or did I go too far?
      4. Demonstrate that we understood confusion around our communication processes
        poorly organized data
        Before: Poorly organized data loses its meaning.

        clearly communicate the problem
        After: By combining categories with similar results, the problem we need to address becomes clearer.

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