Why it’s time for new data architecture in insurance
In order to take full advantage of the immense amount of data insurers are capable of collecting now, internal restructuring can help set an insurer up for success. As industry giants work to find the best practices for managing and analyzing their data, one thing to consider is adopting a new data architecture to organize the existing data as well as the data constantly coming in.
While ensuring there is the right team, management tools, and data analytics to gain valuable insights from data is important, that data first has to be easy to access. Thanks to new cloud solutions and sophisticated methods of storing data, insurers have a number of options.
In order to properly manage their data, insurers must restructure their data architecture
Insurers who don’t restructure their data architecture now run the risk of being overwhelmed in the years to come as incoming data continues to expand. There are numerous benefits to strengthening and organizing an insurer’s data foundation, the first of which is it is significantly easier to manage.
In the insurance industry, the underutilization of existing and new data typically stems from a poor data management system. This can lead to any number of other issues and frustrations, including slow and untimely reporting, blocked analytical capabilities, slow response times, delayed product development, and more. Perhaps most significantly, it severely delays the time between collecting data and turning it into an insight or action point. The speed at which the insurance industry moves today, and the expectations from modern customers, make poor data management a dangerous weakness.
A proactive data management system can elevate performance, improve analytical capabilities, and increase overall timeliness, efficiency, and execution. It allows you to uncover and unlock much of the value that your data holds. Developing strong data management, however, can be difficult, if not impossible, to do without the right data architecture. But to understand why this is true, it’s important to note what’s wrong with the way things are.
The issues with old school data architecture
The key issue with old school data architecture is that it isn’t optimized for the needs of modern insurers or modern data. What ‘optimization’ means in this regard will vary a lot from company to company depending on data demands and changing requirements.
Regardless of what approach an insurer takes to optimization, it should address the key disadvantages of the old infrastructure, including:
- Redundant data sets, tools, processes, or technologies that can slow performance and provide no real value.
- A complex and inflexible structure that makes data difficult to access or share.
- Lack of centralization that prevents data teams from making clear and concise insights and actions from data in a timely manner.
- Lack of integration that makes it complicated to connect the appropriate data to the systems, processes, technologies, and teams that need it. This can be further complicated if these other components also lack integration in their own right.
Data management: data lakes vs. data warehouses
When insurance professionals discuss data management today, they typically use the terms ‘data lake’ and ‘data warehouse’ – but what do those terms mean, and how are they different?
A data warehouse is a very structured, and more traditional, approach to data management and organization. They are organized around a centralized collection(s) of data that is broken down into specific, related sets of data. This is very useful for businesses looking to understand how different data pieces relate to each other as well as to their business context and strategies. While more organized than a data lake, this approach to data management requires more time to access and organize.
A data lake, as the name implies, refers to large pools of original, unorganized data. These are useful when an insurer wants to look at all possible data sources relating to one particular kind of incident and wants nothing excluded, regardless of format or structure. Data lakes are easier to put together than data warehouses, and are also quicker to access. They’re a more recent approach to data management, a response to the massive increase in available data many insurers now have access to.
These two different approaches can certainly work together, as both have different pros and cons depending on the use case or scenario. It’s certainly helpful to be comfortable and proficient with both in order to make the most of the data you have and to gain the best possible insights from it.
The word is Compliance
Regardless of what approach you take to data management, and however you may decide to redesign your data architecture, no issue right now is hotter in the insurance world than compliance. Regulatory bodies all over the world are implementing stronger rules and regulations concerning data privacy and protection, especially for customer data.
With fines numbering in the millions for companies suffering breaches in customer data, insurers must ensure the massive amount of customer data they collect on a daily basis is safe. Developing and maintaining a high-performing data governance structure alongside a strong and secure data architecture can go a long way towards making this happen (and remaining compliant). The consequences for not having this in place can be costly in terms of money and reputation.
Greasing the wheels for agile development and flexible adjustments
Building a sound, efficient data structure can also be a valuable exercise for the different teams needing to access and gain insights from data on a regular basis. By collaborating to build this architecture together, these teams can work through their various needs and priorities together and build something that performs optimally for everyone.
This agreement is important at the building level because so many teams within an insurance company require access to the data that’s collected. When all teams can quickly sift through data and deliver key datasets to analytics teams, valuable and actionable insights can be derived in a timely and organized way.
Having data in a platform that’s efficient and easily understandable for everyone means more agile development and the ability to make adjustments quickly whenever they’re needed. Not only is this important for development purposes, but it’s vital to ensure that insurers can adjust these data structures in the event of customer, market, or regulatory changes. This flexibility allows for quick reactions and enables teams to experiment more with everything from product development to pricing.
Transforming and digitizing for success begins with accessible and organized data
There’s a lot that goes into adjusting a large insurer for today’s market. Partnerships, optimization, and leveraging new technologies are all vital components of this general transformation. But without the right data architecture in place, all these other pieces will struggle to perform optimally.
When data architecture is right, however, data, and the insights that can be derived from it, can be made available to all relevant teams and individuals in real time. Moving away from outdated data structures is a crucial component of an insurers’ move towards a more dynamic, flexible, and agile business model. As Chubb chairman and CEO Evan Greenberg said: “We’re in a world that’s going from analog to digital – everything is. If you remain analog, you’re history.”
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