As an actuary, you will be well-versed in the world of data. You know how to collect it, analyse it, and use it to make sound decisions. But what happens when the data gets unwieldy and too complex? That’s where data architecture comes in.
Data architecture is the foundation upon which all data-related decisions and processes are built. It’s what ensures that data is properly collected, organised and stored so that it can be effectively used.
Defining a data architecture for the actuarial profession
Your data architecture should be designed in such a way that it meets the specific needs of the actuarial team. At MBE we have clearly defined how data architecture relates directly to the needs of the actuarial profession.
One of six enablers that makes up our APM Framework¹, Data Architecture defines the conceptual data model for risk and actuarial results data and describes how this data flows between applications as well as the storage, management and maintenance of this data.
The goal of data architecture is to translate business needs into data and system requirements, and to manage data and its flow through the organisation.
And to achieve actuarial excellence, we have identified that insurers must have a data architecture that enables efficient and granular analysis across all teams within the business to meet the organisation’s objectives and support the strategic vision.
Planning and executing your data architecture
Designing a successful data architecture is no easy task. But with careful planning and execution, it can be achieved. That’s why it’s important to:
Define your goals. Before you can start designing your data architecture, you need to know what you want to achieve with it. What are your specific goals? What data do you need to collect and store? How will you use that data? Answering these questions will give you the platform to set your goals.
Keep it simple. The more complex your data architecture is, the more difficult it will be to manage and maintain. Try to keep things as straightforward as possible.
Consider scalability. As your data requirements grow, your data architecture will need to be able to scale and be applied across other actuarial processes accordingly.
Be flexible. As data needs and requirements change over time, your data architecture should be able to adapt as well.
Ensure data quality. Make sure that your data architecture includes processes and controls for maintaining data quality.
We take these considerations a stage further when designing data architecture for actuarial teams.
Uppermost in our planning and structuring of a data architecture are these four key success factors:
1. Data flows are correctly optimised and applied across all risk management and actuarial processes.
2. Data consumers (actuaries) can access the data through self-service interfaces and tools that enable them to accelerate their work.
3. Data is well-organised, up to date, cleansed and validated, with a common understanding of data definitions across the organisation.
4. Data models and data flows used in your risk management and actuarial processes are properly documented.
To meet these four success factors here are some tips to help you devise a data architecture that is fit for purpose and will support the actuarial team in its work.
Data is made a shared asset. A modern data architecture must eliminate departmental data silos and give all stakeholders a complete view of the company.
Users/Actuaries are given easy access to data. Beyond breaking down silos, modern data architectures need to provide interfaces that make it easy for users to consume data using tools fit for their jobs.
Security is considered paramount. A modern data architecture must be designed with security uppermost, and built to support data policies and access controls directly from the raw data.
Common vocabularies ensure common understanding. Shared data assets, such as product catalogues, fiscal calendar dimensions, and KPI definitions, require a common vocabulary to help avoid disputes during analysis. Use terminology that everyone understands and produce a definition guide.
Data should be curated. By this we mean that the data collected from different sources needs to be correctly organised and integrated to ensure the value of the data is maintained and that it remains available for reuse. Therefore, it’s worth investing in core functions that perform data curation (such as modelling important relationships, cleansing raw data and curating key dimensions and measures).
Data flows should be optimised for agility. Reducing the number of times data needs to be moved will reduce cost, increase data freshness and optimize enterprise agility.
Communicate with the IT team. Developing better, more effective communications with IT will help enrich your strategy.
If you would like support with assessing your current data architecture or help with developing one that is specific to your strategic actuarial goals, contact the team.
¹ The Actuarial Performance Management (APM™) Framework is MBE’s unique and proprietary methodology for transforming actuarial performance. The Framework is a comprehensive, structured approach that underpins our actuarial transformation projects.
Using the APM Framework, we can assess your current actuarial performance relative to six Enablers to identify what and how you need to improve to achieve Actuarial Excellence.
