Every data governance framework values data as a strategic business asset. Insurers have huge data resources and the traditional data management exercises – tending to rely on internal data with rigid infrastructure – are becoming insufficient in this fast-paced digital era.
Insurers today need a comprehensive strategy for systems, processes, and rules for use of data across the whole organisation. There is a trend towards deeper analytics and better preparation of the data to be better able to answer questions such as: can you access data where and when you need it in the insurance business flow? Do you know if your data is clean, current and complete?
This means that, as a first step to effective data management, you need to question the data first, pulling it together in many different formats, cleaning and normalising, before going on to ‘do’ things with the data.
Data governance should incorporate all IT systems and create decision-making processes that rise above departmental silos and should provide accountability for data quality.
A productive data governance strategy will improve decision-making, ensuring information is understood, increasing the credibility of data, whilst protecting data sources in the regulated environment. Data governance should support the company’s business objectives by:
- Managing data as a shared asset across the organisation
- Having transparent data governance policies and decisions.
Insurers are generally aware that data is critical for their business, but the importance of data governance has still to hit home, in terms of business culture and in terms of systems. A holistic data governance approach can help insurers:
- Generate flawless insights that are aligned to the business goals
- Promote greater efficiency and cost savings
- Not be overwhelmed by swelling data volumes (simplifying access to traditional and ‘new’ data sources).
A robust data governance framework requires:
- Enforcement of procedures to ensure data gathered is as per the business needs, if necessary re-shaped, merged or transformed from multiple data tables
- Integration of data from different business units and sources for a unified view of customers, often requiring sharing of meta-data across business domains
- Exploration of the available data, as well as new data, to uncover and understand the quality or continuity issues that may exist (by matching or cross-checking across data tables)
- Monitoring of data on an ongoing basis to check for enforcement of data rules, identifying any quality issues and building data quality into existing processes.
Data governance is all about a data management structure to implement the business policies framed under it. Data management functions include data quality, data administration, data warehousing and data analytics. A data governance framework helps stakeholders within an organisation – from business, IT, data management, compliance, and other departments – to gain clarity of concepts and objectives.
Data governance charts three main aspects: rules of engagement, decision rights, and data accountabilities.
This can be done with the right policies and procedures, structures, roles, and responsibilities within the organisation. Data governance is required to manage data effectively and inject data quality across the organisation. Continued compliance of policies and procedures will improve data quality over the long term.
Here are the steps broadly required to be taken to create an effective data governance framework:
- Decide the strategy for data use and ensure this is aligned to the business processes (often this involves talking between departments and organisational restructuring that is not traditionally regarded as ‘data management’)
- Create a hierarchy for administering the use of data
- Put in place an organisational structure and reporting that aligns with data governance
- Create, document, communicate, and enforce data governance policies.
Each organisation will be in its own unique situation and may be confronted with distinctive challenges, but these steps would provide a foundation to build an effective data governance framework.
Effectively, a data governance framework rests on these four pillars: availability, applicability, integrity, and security.
It ensures simplified coordination among people and processes enabled by technology to reap benefits from the value of data. The goal of data governance is to homogenise the work of people and processes to enhance data integrity and quality.
Data governance has attained some new dimensions with digital technologies, providing data insights and micro-steps with data enrichment that were not possible in the pre-advanced analytics era. It is becoming significant, not just as a point of competitive advantage, but also to ensure compliance in the regulated environment.
The answer to why data governance is critical today is in what Bill Gates suggested, way back in 1999. He said then: “Virtually everything in business today is an undifferentiated commodity, except how a company manages its information. How you manage information determines whether you win or lose.”
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