Data Governance in insurance industry to enhance the outcomes
Any business with an online presence collects and stores data. While data and its analysis using various technologies, like AI, has become a key component of running a business, it has also increased the risk of a breach which can cause significant damage. But it is a necessary risk, as collecting, storing, and analysing data is vital in most industries, enabling a better understanding of customer needs, streamliningday-to-day operations, and makinginformed business decisions.
One of the key aspects of data is its accuracy,and data governance helps ensure data is accurate when business insights are derived from it. For these reasons, it is imperative for businesses to implement data governance to safeguard the way data is gathered, cleansed, stored, and used.
Role of Data Governance in insurance industry
Insurance organizations collect vast quantities of data, and some of it is considered ‘sensitive data,’ requiring special considerations. Companies across the financial services sector, including insurance, are regularly targeted by cyber attackers due to the large volume of sensitive information they have, including consumer fiscal data. As a result, data governance is becoming largely regulated, and many countries are requiring insurance companies to apply and maintain an information security program to protect consumer data.
Data Governance in the insurance industry frequently requires insurance associations to re-think their approach to data to break internal silos &protect data while making it accessible and usable to both internal and external stakeholders, especially when it comes to protecting personally identifiable information (PII).It also requires investment in technology, people, and processes. . Data Governance is an ongoing process that needs to constantly keep itself aligned with changing regulatory requirements.
Goals and benefits of data governance
The role of data is critical in the insurance business. Much of thedata relied on by insurance companies is proprietary, meaning those entities alone are responsible for safeguarding it. Doing just that, while avoiding non-compliance with relevant data governance regulations, is critical. Getting it wrong can lead to significant fines, loss of consumer trust, and an overall negative impact on the company.
One of the goals of data governance is also to enhance the overall quality and reliability of data. Accuracy and completeness of data have never been more critical. Implementing a proper data governance framework is imperative in order to enforce data quality rules, as ignoring it can lead to inaccurate insights leading to the risk of losing revenue.
Implementation of Data governance
The need for Data Governance is exploding with data and analytics disrupting businesses and established ways of doing business. But governance must be approached holistically to ensure all areas of an organization are aligned and trained on appropriate governance approaches and techniques.
This enterprise-wide approach is especially important in insurance, where multiple departments within an organization need access to a centralized database so the entire organization can make business decisions based on the same information. Understanding and complying with laws and regulations pertaining to data in different jurisdictions is also a challenge, as running afoul of standards can be damaging to an insurance organization’s reputation and bottom line.
To implement a data governance program, an organization must first understand the types of data it uses, both owned and externally sourced. There are several challenges any insurance organization faces while collecting, storing, and effectively using that data for various purposes:
• Data coming in different formats via different channels leading to data format mismatch while storing data in a common repository
• Data quality issues such as unclean, non-standardized, and duplicate data
• Data Traceability issues because of poor metadata and lineage
• Data Ownership issues because of the absence of data stewards and data owners
Once the data landscape is defined, steps must be taken to identify the types of data an organization utilizes that require special considerations and safeguards – like personally identifiable information or payment card data.
When the foundational work is complete, however, the data governance implementation process is not finished. Continuous process analysis and improvement, sometimes with significant investment required, is necessaryto monitor andadjust to regulatory changes. Additionally, ensuring the right resources and technologies are in placeto addressthese changes is vital. In short, data governance programs are three-pronged initiatives: People, Process, and Technology.
All these challenges can be met by defining a proper data governance framework that ensures data formats are uniform and correct, the quality of data is improved, and metadata and lineage are adequately defined before data gets stored in the common repository. Lastly, it is also important that data stewards and data custodians are identified and assigned to effectively manage and govern the data on a continual basis.
Digital transformation of data governance
Data use is surging across insurance organizations as they strive to digitally transform operations, and this surge requires careful management and control with increased attention to the security and privacy of the customer information that comes with it. This can be a challenge for data governance protocols, but the good news is the same digital transformations can apply to the governance of data.
Digital tools can help insurance organizations not only realize more value from the data they have but also help adapt to changing regulations and internal uses. A digital approach to data governance must include the ability to monitor data usage across the enterprise and identify potential exposure points, as well as automate the monitoring of regulatory updates so organizations can keep current as the legal landscape changes.
Companies across industries, not just in insurance, must rise to meet these requirements or they will fall behind the competition or suffer significant reputational harm.
One of the key aspects of data is its accuracy,and data governance helps ensure data is accurate when business insights are derived from it. For these reasons, it is imperative for businesses to implement data governance to safeguard the way data is gathered, cleansed, stored, and used.
Role of Data Governance in insurance industry
Insurance organizations collect vast quantities of data, and some of it is considered ‘sensitive data,’ requiring special considerations. Companies across the financial services sector, including insurance, are regularly targeted by cyber attackers due to the large volume of sensitive information they have, including consumer fiscal data. As a result, data governance is becoming largely regulated, and many countries are requiring insurance companies to apply and maintain an information security program to protect consumer data.
Data Governance in the insurance industry frequently requires insurance associations to re-think their approach to data to break internal silos &protect data while making it accessible and usable to both internal and external stakeholders, especially when it comes to protecting personally identifiable information (PII).It also requires investment in technology, people, and processes. . Data Governance is an ongoing process that needs to constantly keep itself aligned with changing regulatory requirements.
Goals and benefits of data governance
The role of data is critical in the insurance business. Much of thedata relied on by insurance companies is proprietary, meaning those entities alone are responsible for safeguarding it. Doing just that, while avoiding non-compliance with relevant data governance regulations, is critical. Getting it wrong can lead to significant fines, loss of consumer trust, and an overall negative impact on the company.
One of the goals of data governance is also to enhance the overall quality and reliability of data. Accuracy and completeness of data have never been more critical. Implementing a proper data governance framework is imperative in order to enforce data quality rules, as ignoring it can lead to inaccurate insights leading to the risk of losing revenue.
Implementation of Data governance
The need for Data Governance is exploding with data and analytics disrupting businesses and established ways of doing business. But governance must be approached holistically to ensure all areas of an organization are aligned and trained on appropriate governance approaches and techniques.
This enterprise-wide approach is especially important in insurance, where multiple departments within an organization need access to a centralized database so the entire organization can make business decisions based on the same information. Understanding and complying with laws and regulations pertaining to data in different jurisdictions is also a challenge, as running afoul of standards can be damaging to an insurance organization’s reputation and bottom line.
To implement a data governance program, an organization must first understand the types of data it uses, both owned and externally sourced. There are several challenges any insurance organization faces while collecting, storing, and effectively using that data for various purposes:
• Data coming in different formats via different channels leading to data format mismatch while storing data in a common repository
• Data quality issues such as unclean, non-standardized, and duplicate data
• Data Traceability issues because of poor metadata and lineage
• Data Ownership issues because of the absence of data stewards and data owners
Once the data landscape is defined, steps must be taken to identify the types of data an organization utilizes that require special considerations and safeguards – like personally identifiable information or payment card data.
When the foundational work is complete, however, the data governance implementation process is not finished. Continuous process analysis and improvement, sometimes with significant investment required, is necessaryto monitor andadjust to regulatory changes. Additionally, ensuring the right resources and technologies are in placeto addressthese changes is vital. In short, data governance programs are three-pronged initiatives: People, Process, and Technology.
All these challenges can be met by defining a proper data governance framework that ensures data formats are uniform and correct, the quality of data is improved, and metadata and lineage are adequately defined before data gets stored in the common repository. Lastly, it is also important that data stewards and data custodians are identified and assigned to effectively manage and govern the data on a continual basis.
Digital transformation of data governance
Data use is surging across insurance organizations as they strive to digitally transform operations, and this surge requires careful management and control with increased attention to the security and privacy of the customer information that comes with it. This can be a challenge for data governance protocols, but the good news is the same digital transformations can apply to the governance of data.
Digital tools can help insurance organizations not only realize more value from the data they have but also help adapt to changing regulations and internal uses. A digital approach to data governance must include the ability to monitor data usage across the enterprise and identify potential exposure points, as well as automate the monitoring of regulatory updates so organizations can keep current as the legal landscape changes.
Companies across industries, not just in insurance, must rise to meet these requirements or they will fall behind the competition or suffer significant reputational harm.
