Why the role of Data Engineer could be the catalyst your business needs
10 July 2018
In our last blog we discussed how the combination of increased data availability and customer expectations was driving the opportunity for increasingly personalised policies. The insurance industry has been built on data foundations with underwriting, claims and reserving all creating data and pertaining processes which are enhanced through the use of data.
Over the last 3 to 5 years the volume of data being created both internally and externally has grown exponentially. We’ve worked with a number of clients to develop capabilities to use this data and have observed the growth in this capability using different delivery models. However, one consistent challenge is the significant effort required in Data Mastering – the process of gathering data from multiple sources, including database tables, external sources, unstructured and manually created data. This is the role of a Data Engineer.
Data Engineers often fill the void between IT departments, the business and analytical teams. We’ve observed data mastering in practice in various guises at every firm we’ve worked with. From IT teams who aren’t adequately engaged with the business to be effective and proactive, to analytical resources who are clear on business requirements but develop slowly due to this not being their core skill set. Both can lead to inefficiency and often at a cost base which is disproportionate to the activity being completed.
This challenge is often presented within organisations through teams telling of the time-investment required on data preparation and resolving data quality issues ahead of the delivered ‘product’. We have observed analytics teams spending up to 80% of their time on this activity with management’s awareness limited as the expected output from the team is the 20% of time-spend.
Recognising Data Engineers as a distinct role within your organisation will act as a significant operational lever to increase the speed of analytics capability development and the effectiveness of these capabilities in the future. In addition, the insurance industry is facing a challenge in respect of where future analytical (including Data Scientist) capabilities will come from. Having a firm grounding in technical capabilities combined with a business understanding, the business purpose of analytics is surely a serious pathway for talent in the analytics sphere.
The target operating model for insurance analytics is an issue which has not only been highlighted through the growth in data volumes and associated analytics capabilities, but also the wider implications of the General Data Protection Regulation (GDPR) which places a greater responsibility on firms processing personal data to have data management in place. This will bring significant portions of firms’ data into the scope of a defined and operational data governance framework, which has historically often been limited to financial data and that material to Solvency II models.
A truly analytics driven insurance business requires an effective operating model with clearly defined roles all working towards the strategic priorities of the business.
Sam Jones | Insurance Data & Analytics
Timothy Harrison | Senior Manager, Advanced Risk and Compliance Analytics