Digital transformation: the move from ‘data rich’ to ‘data led’ in Insurance
Insurance companies are undergoing the change from being ‘data rich’ to ‘data led’, embarking on digital transformation programs to increase the utilisation of analytics and automation. This article explains how to set up your enterprise for success with the latest approach to digital transformation.
Covid-19 has been the catalyst for insurers to accelerate their digital agendas. The pandemic created new complexities around data sharing which requires secure cloud systems to be implemented. In addition, it has encouraged the industry to utilise new technologies. Insurers are adopting tools like intelligent automation, AI and ML to support the workforce as these technologies mature. Post-pandemic is the opportune time to develop these capabilities and drive efficiency in the workforce to enable new revenue streams.
Becoming a data led organisation
Insurance companies are traditionally ‘data rich’, transferring enormous amounts of information to power day to day processes and risk assessment. This data is typically utilised to run day to day processes, however, Insurance companies are often behind the curve when it comes to running advanced analytics and using technology to optimise existing processes. This next wave of innovations in will enable the insurance industry to become ‘data led’ by measuring current and historical trends to improve operations. benefit from automation now more than ever before. To do this, it is essential to prepare existing data so it is ready to power new technologies.
From fragmented to consolidated
Large insurance companies often rely on legacy and outdated systems which creates challenges in the adoption of new technologies. Multiple systems deliver a fragmented access to data, where information is often siloed in different systems. This results in significant effort expenditure for business teams to access and consolidate data for day to day use, which has a direct impact on efficiency and productivity levels.
To add to complexity, existing insurance processes often result in multiple formats for data: structured, unstructured and semi-structured. Companies share data in a variety of ways, such as email, PDF, online portals and even phone calls. These methods require data to be appropriately quality-assured, standardised and contextualised before it can be utilised in technology, which is a significant drain on resources.
This fragmented data ecosystem also causes challenges in the adoption of new technologies, which require consolidated, trustworthy data to deliver value. New InsurTech solutions such as automation, ML and AI are powered by clean, standardised data to inform automated and intelligent decisionmaking. Creating this standardised data layer is often a major roadblock in the journey to digital transformation.
Data preparation is essential to digital transformation
Data must be quality assured, complete (no missing values), in a consistent format, and a consistent location to be ready for use. We call this a quality data layer. A quality data layer is a central, clean repository of all the data assets in your business. This repository should act as the ‘source of truth’, storing data in a consistent, consolidated format ready for utilisation by systems, processes and reports.
Preparing a quality data layer is a big step in the journey to utilising new technology. Therefore, it’s critical when planning digital transformation to start with the data, and then follow up with the process. Organisations that focus too heavily on process constrain the view of data within the enterprise, by thinking in terms of the data they already have, not the data they could get. Considering data first removes this constraint.
The next step is planning how data from various systems and formats can be brought into a single view. To do this, it’s important to think of the master format that each data point should be in, and the system or storage that the data is currently housed in. This allows teams to create a centralised data framework of the assets within their organisation, which details the target state for your data model.
Creating a data quality layer
Once this target state is established, the next challenge is to create this centralised view. To do this, it’s essential to first think about your data supply chain. When correctly managed, this data supply chain should connect data from multiple systems/processes and automatically quality assure and transform the data into the target centralised state.
This will create a central data quality layer; a consolidated, centralised view that standardises data governance, data quality and data format, ready for utilisation by downstream systems or processes. To be most effective, data in the quality layer should include a level of transparency, collecting metadata about the data source, time of creation and change history to access for future reference.
Seize digital transformational opportunities
Once you have a data quality layer in place, it’s much easier for data operations managers to seize the opportunities that intelligent automation, AI and ML can offer to solve complex issues within the insurance industry. This central repository of business-critical data is essential not only to power systems today, but also to ensure that data does not pose a roadblock to adopting future technologies.
That’s why at Think Evolve Solve, we’ve developed a solution to automate the creation of a data quality layer, through our data supply management platform gather360. gather360 offers a range of ways to connect data from multiple systems, and automate quality assurance, transformation and consolidation to create a central data quality layer that powers systems and processes. The system gives Operations Managers the ability to monitor the data supply workflow and identify bottlenecks with the dataops dashboard, to deliver quality data up to 94% faster.
As Insurance providers continue to identify new ways of driving efficiencies in this post-pandemic world, creating a data quality layer is essential to creating a scalable, flexible, future-proof and data-led digital transformation strategy.