Four big obstacles are blocking insurers from reaping the value of their data and becoming data-powered companies.
Insurers are fast realizing that their data is a critical asset that can give them a big edge on their competitors. But many carriers struggle to unlock the value trapped in their data. Our research shows that big companies only analyse 12 percent of their data. A staggering 88 percent of corporate data is sitting idle.
What’s the solution?
First, insurers need to get a clear understanding of the problem. Then, they need to use digital technologies to unlock the value stored in their data. Critical technologies are cloud computing, data analytics and artificial intelligence (AI).
Through our work with companies across many industries, we’ve identified four big obstacles that block businesses from unlocking the value of their data. By overcoming these obstacles insurers can transform themselves into data-powered companies.
A data-powered company, as I mentioned in my previous blog post, combines extensive data resources with analytics and AI. This improves the performance of the company’s systems and processes. It also gives it valuable insights into the needs of consumers, business partners and employees. A data-powered company goes beyond data monetization. It puts data at the core of its business. It uses data to inform all key decisions.
Data analysts only spend 20 percent of their time working on data.
Four biggest obstacles stopping insurers from becoming data-powered companies
- Data is not managed as a strategic asset. Companies often talk about the importance of data and its vital role in their digital businesses. But few of them are treating data as a strategic asset. What’s stopping them? Most companies are still keeping their data in silos where it is difficult to access. And even more difficult to analyse. What’s more, as much as 80 percent of data gathered by large organizations is unstructured. Poor integration of data blinds them to the many opportunities to extract value from this critical asset.
- Poor data quality and governance. This is a big obstacle. We found that data analysts in large companies spend as much as 80 percent of their time searching for, cleaning and preparing data for analysis. Only 20 percent of their time is used to analyse data. Why’s data analysis and governance so poor? One of the main reasons is the enormous growth in the sources of data. Research firm Statista, for example, says there are more than 20 billion sensors currently linked to Internet of Things (IoT) networks. This figure could double in the next five years. The introduction of new recognition and tracking technologies will hasten this proliferation. These technologies can detect facial features, body movements and even heartbeats. Increased machine-to-machine communications and the roll-out of 5G networks will also add many new data sources. Furthermore, lots of companies have yet to adopt a standardised approach to data management and governance. They’re often inconsistent in their use of data and lack standards for storing and accessing this resource.
- Ineffective distribution of data capabilities and responsibilities. Key data skills and resources are often scarce and not aligned with the organisation’s needs. Many companies haven’t defined clearly the responsibilities of workers who are required to manage their data. Tasks are often duplicated. Project procedures are frequently inconsistent. This encourages shadow IT to flourish.
- Inadequate technology foundations. Many firms have yet to deploy data analytics because their technology platforms can’t cope with the huge volumes of data streaming into their organizations. Traditional data warehouse facilities can’t provide the agility and flexibility they need to unlock the value of their data. These legacy systems are unable to support advanced company-wide business intelligence solutions that require quick access to data from many sources. Such shortcomings often result in the proliferation of discrete data applications that serve small groups of users. Progressive insurers have begun to tackle this obstacle. They’re creating data lakes to better manage their structured and unstructured data. Some of these insurers have also implemented a Universal Metadata Repository. This enables them to automatically triage, analyse and store the vast volumes of data that are being generated by increasingly important real-time applications. Internet of Things applications, for example, as well as image-processing solutions, often used for claims adjustment, generate huge amounts of data.
In my next blog post, I’ll discuss how insurers can start to transform themselves into data-powered companies. More information about data monetization and data-powered companies can be found at these links. Otherwise, send me an email. I’m keen to hear from you.
Accenture and M6: Monetizing big data
Great post Jean-Francois! I will touch on your point about flourishing Shadow IT that we can also call a “Blindspotted IT” or IT failure of managing it. Since there are so many people involved in managing data, the data itself is spread on multiple different SaaS apps which pose a great risk when the same company is using multiple apps to do the same tasks.
The biggest issue I see here is the communication between employees and departments, more specifically between IT departments and the rest of the workforce. This can be solved by constantly communicating procedures and apps the company is using to keep the data in one place.