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.
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?
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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 |
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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 |
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Legal |
Knowledge and appreciation of some of the laws surrounding data |
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Controls |
Understanding of the needs, importance and use of control frameworks |
What do Actuaries lack?
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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. |
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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 |
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Data |
Knowledge of Big Data structuring, hosting and processing and costing Best practice for customer-centric data design Unstructured data structure and techniques |
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Visualisation |
Knowledge of the various visualisation techniques and tools and their cost |
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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 |
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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 |
$40,000 $54,000 $45,000 |
[reveal heading=”
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” id=”id1″]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 |
[reveal]
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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