Firstly, what is a data driven company? In such a company data is at the very core of the business. It drives business decisions, it creates a competitive edge, and is actually the business language of the company. It means that significant resources and attention is being dedicated to data strategy and data science ultimately plays a huge role in the company culture and the resulting impact on the customer.
Here are key themes from the discussion:
Different types of companies have different challenges but similar opportunities regarding data
For smaller, younger data-driven companies, often the solutions to data-related challenges can lie in obtaining more data (frequently that being external data), and continuously evolving and testing smarter algorithms because your starting point is often a greenfield. By contrast, from the perspective of larger organisations, the opportunity is being able to unlock insights from the large existing data sets. You can generate a lot of business insights by analysing customer data with historical data, for example. The challenge comes from how to combine various data sets, organize that within a large organization, and bring it into a data pipeline that is directly impacting the users and customers.
Regardless of the size of the organisation, having the highest quality data and the highest volume of data is a huge opportunity in order to create points of differentiation. One hugely important challenge arising from a high volume of quality data is determining what your edge is going to be? So there must always be a purpose and an experiment that must emerge at the end of the day, whether it is a new product, process optimization, or unique risk assessment.
Insurance industry can leverage data for a unique competitive advantage
Firstly using unique approaches to data gives one an edge from a risk and underwriting perspective. To make this work, a tight relationship is needed between the people who gather data in an organisation and the people who actually use the data (e.g. the data scientists), as it can be easy to gather data but the more important question is how that data can is then used from the business perspective.
Ultimately data should be improving the customer journey. How to continuously optimize and reduce friction from the customer journey is essential. Automating retrieval of hard to get data and providing many inputs automatically would create a journey that should be ultimately better than your competition.
Predictions for how the space will evolve in the coming years
For companies like Baloise, it will become essential to connect our ecosystems. Thomas Kandl from Baloise mentioned: "Being able to integrate our internal data with the external world and extend it into our growing ecosystems will new generate new business insights of customers and partners that will help us to shape new products and new exciting customer journeys".
An additional trend that could emerge is: ‘Algorithms as a Service’ - much like Software as a Service. Considering that there is a double challenge of a shortage as well as a huge competitive demand for data scientists, how can Algorithms be used as reusable, on demand products. Tools like Plotly are a first steps to something like that which aid data scientists currently with pre-built tools and apps.
Blockchain technology and underlying DeFi infrastructure will connect to traditional finance with data flowing back and forth between these ecosystems, creating a much more powerful convergence. Companies like Kaiko are aiming to deliver this type of data to various specialist and even traditional financial institutions.
Lastly, it is important to improve how data and insights actually get communicated. Not being able to properly communicate the insights and meaning of data science can lead to that becoming a silo in a company. Therefore it is important to keep innovating how data gets visualized and communicated.
This is a first in a series of panel discussions and we will continue to bring discussion insights from various sessions.