Insurance and big data analytics: Data management strategies could give insurers competitive advantage
Data governance gives insurers big data advantage
Insurers could gain from data management strategies to improve insurance data analytics.
While few insurers today are ready to take full advantage of the opportunities afforded by insurance data analytics, they could start improving their data management strategies immediately. Insurers need to take a bold approach to shake-up their business models, enhance their data governance, and focus on enterprise data management.
Our experience suggests that few insurers today are ready to take full advantage of the opportunities that could be captured with greater data insights.
Opportunities in insurance company data
Many insurers continue to struggle to optimize their data and analytics capabilities, since big data is continuously growing, not only in size and scope, but also in complexity. As a result, many insurers now need to explore new forms of data stewardship, including data acquisition management and integration in order to wring real insights from their data.
Basic opportunities include gaining insights simply by integrating social media data with claims data to identify potentially fraudulent activity, assess liabilities more accurately, and better monitor risks.
Thinking more broadly, we recently worked with one insurer to create a ‘risk dashboard’ that integrates data from more than 40 different social media sources to provide real-time monitoring of a range of risks in key geographies. This lets customers more actively manage and monitor their risks, reduces overall risk for the insurer, and creates value-added service to clients.
However, moving beyond a traditional ‘process’ focus (i.e. how things get done) and towards a more ‘informational’ focus (i.e. why things get done and in what situations) will require insurers to reconsider their approach to people, process and technology.
Challenges to harness insurance big data
Many insurance executives still see big data as a ‘big challenge’ rather than a ‘big opportunity’ and – with little competitive pressure to adapt and a long list of other high-priority challenges on the horizon – most have opted to dabble with their data rather than attack it.
The greatest challenge revolves around current data management strategies, since data is often trapped in silos, inconsistently labeled or locked behind access controls, preventing a ‘single view of the truth.’ Likewise, data governance and ownership is often spread across the organization.
Strategies for data stewardship
Our experience suggests that insurance organizations could take data management actions today to evolve into data-driven, insight-led organizations:
- Clean up what you already have: Before purchasing reams of new data sets and sources, insurance organizations should clean up and integrate the data they already have.
- Develop a data governance model: Most insurance organizations will need to focus on creating an enterprise-wide approach to data governance, to create consistency in the standards and controls guiding data usage.
- Create an enterprise data management function: While many insurance organizations have some data stewardship function, most are single-mindedly focused on creating policies rather than improving information flow and usage. They require an enterprise data management function.
- Build a culture of experimentation: Given the regulation governing the insurance sector, and the risk averse corporate cultures, insurers will want to consider how to implement a culture of experimentation to safely test out new approaches.
- Look for the blue sky: Rather than trying to force your data to tell you things you already know about your business, focus on creating new areas for improvement or value creation. Insurance organizations should look for ways to use their data to shake-up the business model and transform the status quo.
- Move towards probability-based analytics: The key to driving insights from big data is being able to eliminate the information that isn’t relevant. Finding value in big data will require insurers to understand, and embed, probability-based approaches and techniques into their core data and analytics capabilities.
- Make sure you are asking the right questions: If insurers invest in identifying the right questions to ask of big data – and then couple the right questions with smart probability-based analytics – insights can start to flow very quickly.
We believe insurers that are able to create the right environment and data governance framework to support data-driven experimentation and exploration will win in the ‘customer-centric model’ of the future. Those who merely ‘dabble’ with their data may soon find it difficult to catch up.
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