Do Actuaries have the skills to be Chief Analytics Officers?

Actuaries have the mathematical, statistical and programming skills to take a company’s data to the next level. But how do their skills align to current Chief Analytics Officer (CAO) roles? Bill Konstantinidis investigates the gaps and what courses on the market can help bridge them. 

A Chief Analytics Officer (CAO) looks at all available data, structured and unstructured, internal and external, across the business and works out how it can be used to achieve a competitive advantage.

Some would say it’s the ultimate ‘big data’ job.

The role sometimes includes what is known as a Chief Data Office function; the process of consolidation of all data into an easily digestible format for the organisation.

A sensible technological infrastructure is essential to allowing an organisation to truly live the “data driven company” dream using analytics.Bill k

Targeting the elusive “single customer view” is one strategy. The collection and analysis of data to assist all aspects of the value stream is another.

It is the CAO’s role to provide new insights strategically to push the business forward, rather than just being an order taker delivering to internal stakeholders.

This is a transformational change for most organisations.

Quotes from a job ad for ‘Vice President and CAO’ at Horizon Blue Cross Blue Shield evidence this:

“serving as the key strategist to shape and drive the transformation of …  core data and analytics strategies.”

“this is a transformational position”

“seeks a leader who can revolutionize the way data and analytics are used to drive the organization.”

“develop and lead a consolidated enterprise strategy for capturing, analyzing, and leveraging data across the organization to drive financial performance, operational and network efficiencies, healthcare quality, member engagement and member satisfaction.”

What the CAO Job Ad asks for

CAO is a potential actuarial career path but the current requirements of the role must be examined carefully. There are three key elements I see as key to any CAO role:

DRIVE STRATEGY

Driving data and analytics strategy

TRANSFORM

A need to transform and change an organisation and

ENHANCE COMPANY-WIDE

PERFORMANCE

Utilising data across the organisation to add value to improve performance across all aspects of the organisation

 What do Actuaries have that is aligned to the needs for a CAO?

Qualifications

Actuarial qualifications are valued in the list of candidates included amongst the usual mathematics based and information system degrees

Analytics

Strong analysts in an insurance context covering multivariate modelling of large data sets, knowledge of and application of statistical techniques

Tools

Foundational programming skills

Use of statistical packages and modelling tools

Problem Solving Toolkit

An ability to apply analytical techniques to new environments

Good problem solvers

Exposure to some transferable models eg. customer churn

Analytics in financial and insurance data

Judgement and Decision-making

Legal

Knowledge and appreciation of some of the laws surrounding data

Controls

Understanding of the needs, importance and use of control frameworks

 

What do Actuaries lack?

Knowledge

Lack of context outside insurance processes such as HR, operational, quality control, internet traffic. This may slow to identify appropriate solutions to business problems.

Tools

Many don’t understand the techniques and tools of the new data science world and the cost and benefits of those solutions

Lack of advanced programming and knowledge of available tools and their uses

Business Intelligence systems, structures and delivery options and their costs

Web technologies

Data

Knowledge of Big Data structuring, hosting and processing and costing

Best practice for customer-centric data design

Unstructured data structure and techniques

Visualisation

Knowledge of the various visualisation techniques and tools and their cost

Change Management

Lack of Change management skills

Known as contributors to strategy rarely drivers

Lobbying and toolkit to sell the strategies up the line and to the rest of the organisation

Philosophy

Predictive analytics – appreciation and understanding of prescriptive models eg self-adapting models – Diagnostic, Descriptive, Predictive and Prescriptive

Knowledge Management

Security

Data security and privacy and laws around the use of data

Data management and access control

Delivery

Planning and Project management across multiple functions

 

Areas of suggested learning for Actuaries:

  • Change management frameworks
  • Knowledge of Data storage methods
  • Knowledge of technology, processing costs and risks
  • Knowledge of data protection and security technologies particularly when scaling data across the organisation
  • General Knowledge management
  • Big Data technologies and techniques

Examples of problems that may need Actuaries to broaden their Knowledge:

  • How do I optimise marketing spend?
  • What makes a good branch manager?
  • Call centre optimisation – how do I minimise waste and absenteeism?
  • How do I improve the quality of data?
  • How can I use my digital internet data to optimise my customer purchases?

Masters Programs on the market

Data analytics is a space where a number of Masters programs have sprouted up to upskill professionals. Here are three Masters degrees currently on the market:

University

Program

Estimated Cost

RMIT

MBS

UTS

Master of Analytics

Master of Business Analytics

Master of Data Science and Innovation

$40,000

$54,000

$45,000

Click for detailed Masters Course outlines

SOLUTION: 

The list of courses offered in each of the above programs is listed below including core and electives. There are many subjects which align with actuarial training though there are a number which address areas of security, big data and databases.

I think one thing missing from the below is the change management process – how to present the benefits, bring the organisation on the journey, and shape strategy and knowledge management.

RMIT Master of Analytics Estimated at $40,000

Advanced Programming Algorithms and Analysis Analysis of Categorical Data Analysis of Large Data Sets Applied Bayesian Statistics Big Data Infrastructures

Big Data Processing Data Mining

Data Visualisation Database Concepts Database Systems Forecasting

Minor Thesis

Game Theory and its Applications GIS Fundamentals

Industrial Research Methods Industrial Research Project Information Systems Security Information Theory for Secure Communications

Introduction to Statistical Computing Introduction to Statistics Mathematical Modelling and Decision Analysis

Methods and Models of Operations Research

Multivariate Analysis Techniques Programming Fundamentals Programming Techniques Questionnaire and Research Design Regression Analysis

Scripting Language Programming Sports Analytics

Statistical Inference

Statistics of Quality Control and Performance

Analysis

Systems Simulation Time Series Analysis

 

MBS Master of Business Analytics Estimated at $54,000

Introduction to Business Problems Foundations of Business Analytics Computing and Programming for Business Problems

Data Warehousing

Decision Making and Optimisation Statistical Learning 1

Advanced Business Analytics

Statistical Learning 2 Data Visualisation Predictive Analytics Text and Web Analytics

Personal Effectiveness Program (PEP) Industry Practicum

Customer churn/loyalty

 

Logistics and supply chain Forecasting demand

Optimal product or category portfolio

Marketing-mix optimisation Credit risk

Employee selection, retention and training

Analysis of social media or other unstructured data sources Business Analytics Applications Finance Analytics

Marketing Analytics Supply Chain Analytics Business Case Study

 

UTS Master of Data Science and Innovation Estimated at $45000

Data Science for Innovation Statistical Thinking for Data Science Data, Algorithms and Meaning

iLab 1

Project Managing Data Driven Solutions

Data Visualisation and Narratives Data Driven Decision Making iLab 2

Advanced Data Analytics Algorithms Advanced Database

Advanced Interaction Design Advanced Software Modelling Building Intelligent Agents Business Intelligence

Business Intelligence 2: Advanced Planning

Business Intelligence Modelling and Analysis

Business Intelligence and Analytics Business Intelligence for Decision Support

Business Systems Design

Cloud Computing and Software as a Service

Data Visualisation and Visual Analytics Digital Experience Design

Digital Forensics

Enterprise Application Development Using Cloud Platforms

Epidemiology and Population Health Fundamentals of Data Analytics Internet Programming

Introduction to Biostatistics Investigative Research in the Digital Environment

Judgment and Decision Making Linear Algebra

Managing Projects Marketing Research Mathematical Statistics

Modern Analysis with Applications Multivariate Data Analysis Network Security

Neural Networks and Fuzzy Logic Numerical Analysis for Quantitative Finance

Numerical Methods of Finance

Planning and Evaluating Health Services Privacy and Surveillance: Law and Policy

Project Communication, HR and Stakeholders

Project Management Principles Project Time and Cost Management Quality Control

Quality Planning and Analysis Quality and Operations Management Systems

Research for Communication Professionals

Seminar (Statistics)

Statistics for Quantitative Finance Stochastic Calculus in Finance Systems Thinking for Managers Technology Research Methods Using Health Care Data for Decision Making

Web Technologies

 

An Institute Course

Data science is emerging as a stream in actuarial study programs around the world. Masters degrees can be quite expensive and, as an alternative, the Actuaries Institute could potentially provide valuable courses at a cheaper cost. The Institute’s Data Analytics Working Group (DAWG) is investigating this and can provide a range of useful resources and a network for interested actuaries.

In the meantime, should actuaries keen to upskill in data analytics start doing these Masters programs to help them own this space? I believe absolutely yes. 

While the Institute refines its strategy on data analytics and designs programs in line with industry demands, actuaries should look to capitalise on this emerging field. 

As Ron Arnold told our GI Glimpse Seminar this week: actuaries sit in a privileged position, we are well trained in experimental design and we understand data, we often get an executive seat within a business and are respected for our advice.

With data-driven insights already transforming insurance and the industries actuaries work in, now is the time to rethink our roles, skill set and value proposition.

Please don’t hesitate to get in touch with the DAWG to find out more, and add your comments on this topic below. 

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