It may sound like an oversimplification to say that Data Mastery (DM) is the ability of an insurer to gain mastery of their data, so perhaps it would be better to say that DM is the ability of an insurer to gain business insight from raw data.
Since the financial crisis began in 2007, insurers have significantly increased their focus on Business Intelligence (BI) and analytics efforts to help improve customer retention, targeted marketing, claims fraud, operations, finance, actuarial and underwriting. Even as the economy has slowly improved, competition has driven insurers to work even harder at turning their data into a competitive advantage.
Many of the challenges facing insurers with respect to DM efforts today are cultural and not technology based. Most insurers have pockets of DM expertise within departments or lines of business, but have not been able to leverage data effectively at an enterprise level. This has resulted in a myriad of data models, definitions, standards, tools and data fiefdoms that have prevented most insurers from obtaining the full business value of their data.
That said, and as the old saying goes, insurers tend to be data rich and information poor. Most insurers only use five to ten percent of their current data due to the lack of an enterprise data strategy, data governance, and data quality. The key to unlocking the full business value from their data, or gaining mastery of enterprise data, is developing an enterprise data strategy which starts with the business objectives and goals of the enterprise at its heart. It is mission critical that such a strategy be business driven and not IT driven.
By starting with the business objectives and goals, the correct metrics and key performance indicators (KPIs) can be developed to drive appropriate business decisions – whether it is market growth, improved customer satisfaction, reduced risk or reduced operational costs. The next step after focusing on the business priorities is developing consistent business entity definitions. Many insurers struggle with multiple definitions of policy, earned premium, closed claims, etc. While business units may use different definitions to best drive their units, the enterprise data definition of the most important business entities must be consistent and standardized. This is supported by having a mature insurance data model that covers the full insurance life cycle, so as data from each of the insurer’s data sources, internal and external, all map to the same definitions.
Successful enterprise data strategies start with not only executive support, but are driven by corporate structure that fully supports the data strategy through a formal data governance program. The organizational structure must reflect the importance of the business value of the data. Keep in mind, at its core, insurance is about data and processes; insurers do not make a tangible product. Data abounds about customers, property, operations, trends, finance, processes and risk within carriers, but that data is locked away securely in the business silos. Strong executive leadership is critical to breaking down the data silos that have over the years built strong bunkers. The data, after all, belongs first to the enterprise, then to the business units.
Data governance helps drive data quality solutions back to the origin of the source of the data, at the point of data entry. The cost and effort to correct and standardize data at the data warehouse (end of the data process) is exorbitantly high. While it may be required for compliance and historical reporting, it will be the anchor that brings many insurers to their knees if they do not address their data quality concerns at the origin.
Data currency (how current is the data being used for business decisioning purposes) is also enabled by a strong data governance process. The more current the data used in decisioning, the better insurers can respond to catastrophes and market changes, thereby gaining a strong competitive advantage.
Finally, decisions about Big Data, small data, internal data and external data can only be successful if driven from the enterprise level. It is also important to realize that if an insurer cannot handle small data, then handling and utilizing Big Data will be impossible. Big Data particular implies a required enterprise focus, which should have been the focus of small data all along.
Strategy, direction, and quality must come from the top down, so the data can successfully flow from the bottom up and provide the most value to the insurer. Thus, the key to unlocking business insights and enterprise business value and becoming a data-driven decision company is an enterprise data strategy built on:
• Enterprise business objectives/goals;
• Executive sponsorship and involvement;
• Organizational structure that reinforces the importance of data governance;
• Common business entity and measure definitions;
• Mature insurance data model;
• Data governance; and
• Data quality driven at the source of the data entry/origin.
Insurers need to leverage vendors that have solutions and experience in each of these areas to get the most value from their solution investment.
Ben Moreland is senior business architect for Innovation Group. He can be reached for further comment or information via email at firstname.lastname@example.org.
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