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Four data traps as snare insurance companies



Four major obstacles prevent insurance companies from reaping the value of their data and becoming data-driven companies.

Insurance companies quickly realize that their data is an important asset that can give them a huge advantage over their competitors. But many carriers struggle to unlock the value captured in their data. Our research shows that large companies only analyze 12 percent of their data. An astonishing 88 percent of company data is vacant.

What is the solution?

First, insurers must have a clear understanding of the problem. Then they have to use digital technology to unlock the value stored in their data. Critical technology is cloud computing, data analysis and artificial intelligence (AI).

Through our work with companies in many industries, we have identified four major barriers that prevent companies from unlocking the value of their data. By overcoming these obstacles, insurance companies can transform into data-driven companies.

A data-driven company, which 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 provides valuable insight into the needs of consumers, business partners and employees. A data-driven company goes beyond revenue generation. It puts data at the core of its business. It uses data to inform all important decisions.

Data analysts spend only 20 percent of their time working on data.

Four major barriers to insurers from becoming data-driven companies

  • Data is not handled as a strategic asset. Companies often talk about the importance of data and its vital role in their digital businesses. But few of them treat data as a strategic asset. What prevents them? Most companies still keep their data in silos where it is difficult to access. And even harder to analyze. In addition, as much as 80 percent of the data collected by large organizations is unstructured. Poor data integration combines them with the many opportunities to extract value from this critical asset.
  • Poor data quality and control. This is a major 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 analyze data. Why is data analysis and control so bad? One of the main reasons is the enormous growth in data sources. The research company Statista, for example, says that there are currently more than 20 billion sensors connected to Internet of Things (IoT) networks. This figure could double over the next five years. The introduction of new recognition and tracking technologies will accelerate this spread. This technique can detect facial features, body movements and even heartbeats. Increased machine-to-machine communication and the roll-out of 5G networks will also add many new data sources. In addition, many companies have not yet used a standardized strategy for data management and control. They are often inconsistent in their use of data and lack standards for storage and access to this resource.
  • Inefficient distribution of data functions and responsibilities. Important skills and resources are often scarce and are not adapted to the needs of the organization. Many companies have not clearly defined the responsibility of workers who are required to handle their tasks. Data is often duplicated. Project procedures are often inconsistent. This encourages shadow IT to flourish.
  • Insufficient technology foundations. Many companies have not yet distributed data analytics because their technology platforms cannot handle the huge volumes of data flow to their organizations. Traditional data warehousing facilities cannot provide the flexibility and flexibility they need to unlock the value of their data. These older systems cannot support advanced enterprise-class solutions for business information that require rapid access to data from many sources. Such shortcomings often result in the proliferation of discrete computer programs that serve small groups of users. Progressive insurance companies have begun to address this obstacle. They create data lakes to better manage their structured and unstructured data. Some of these insurance companies have also implemented a Universal Metadata Repository. This allows them to automatically triage, analyze and store the large amounts of data generated by increasingly important real-time applications. Internet of Things applications, for example, as well as image processing solutions, which are often used for application adjustment, generate huge amounts of data.

In my next blog post, I will discuss how insurance companies can start turning themselves into computer-driven companies. M ore information on revenue generation and data-driven companies can be found at these links. Otherwise send me an email. I'm anxious to hear from you.

Accenture and M6: Make Money on Big Data

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