Data & Analytics

10:34 AM
Anil Chitale
Anil Chitale
Commentary
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Cultivating Timely & Effective Insurance Data Management

Data is the fuel that feeds core systems, so modernizing data and how it is managed is just as important as the systems themselves.

It is understandable that in the effort to modernize, insurers tend to focus more on their vital core systems, rather than data. However, given that data is the fuel that feeds the systems, modernizing data and how it is managed is just as important, if not more important, than the systems themselves. One might think that once systems have been modernized, keeping data clean and organized should become much easier. Unfortunately, that’s not always the case.

Not only do carriers need to deal with the high volumes of historical data, but the organization’s data is similar to a lawn: It just keeps growing, and if you don’t tend to it, nurture it, and keep it tidy, it can quickly get out of control and ruin the curb appeal of the home.

Having a comprehensive data strategy is a key component of every carrier’s modernization journey. To build that strategy, it may help to disengage the data discourse from conversations over system modernization. That isn’t to say that a carrier should build a data strategy without understanding how future system modernization will affect data goals. It just means that the organization should commit to managing data well, regardless of which systems are being replaced and when.

Cleaning the data house
Data’s role is important to achieving its target system’s goals -- dictating the need for the data to be clean and ready for use once those new core systems are ready for production. Data’s value correlates to how well it is managed. Simply stockpiling data is like dumping your yard cuttings in the compost pile. Over time, the data breaks down, and integrity tends to be lost when there is no plan for safekeeping.

[Zurich North America CIO's Data Journey]

Organizing and cleaning up the data yard starts with a comprehensive census of data sources, which encompass transactional data, master data, public data, third-party data, unstructured social data and more. A plan must then be created to not only catalog and organize the data to protect its integrity, but also to survey the organization regarding its use.

Seeking assistance from data management experts could be helpful to identify the right data models to best facilitate data understanding and analysis across the spectrum of data types and business users. Input regarding current and future data needs should be garnered from all corners of the organization, since data managers will be continually asked to add new sources of structured and unstructured data.

Choosing data management tools
While more water and fertilizer may be a good for an already healthy yard, continually adding more and more data to a carrier’s healthy environment can become too much of a good thing. While big data might be all the rage, digital technology proliferation will either bury carriers under a data compost pile or provide the fuel for growth -- depending in large part upon the tools chosen to deal with it.

Every organization is unique in its needs and methods of building a data infrastructure. Whether you choose tools from one vendor or a collection of vendors, the infrastructure needs to serve at least three purposes: improved data collection and management; effective structuring for business analysis and use; and high-level security that can maintain privacy while also allowing for testing.

[Previously from Chitale: 4 Core System Replacement Considerations]

Modern data management tools are up to the task. New technologies exist that can cull unstructured data to identify meaningful trends, improve decisions, expand configurability, and enhance visibility. Pseudonomization, a process by which companies can privatize data without compromising its visibility or testability, is allowing companies to meet the need for tighter data security.

It is with data tools that carriers will see a competitive advantage begin to take form. When you're looking for tools, it’s best to choose partners who have rich assets in the extraction, transformation, and lowering of data (ETL). Not unlike traditional data migration, ETL assets may save a carrier the time and money it takes to build from scratch.

Selecting the data champion
The newest mower and trimmer won’t cut the lawn by themselves, nor will the latest data tools deliver much value without the right human resources. It takes a dedicated person or team of people to employ the data tools effectively to manage and transform data for use. Additionally, these data champions must be governed by the organization’s data needs and a fixed calendar. Because the data never stops growing, data strategies need to pay special attention to timing and routine.

At all stages, the key is to build simplicity into the process to ensure that data can be managed and maintained for the long term. Like your lawn, your data requires constant attention to keep it healthy, weed free, and an asset to deliver the best business curb appeal -- now and for the foreseeable future.

Anil Chitale, Senior Vice President & Chief Product Evangelist, leads the P&C division at MajescoMastek. Anil was the Co-founder of STG and a principal architect of the STG suite of products. View Full Bio

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Kelly22
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Kelly22,
User Rank: Author
11/20/2014 | 1:25:00 PM
Re: Data management
Interesting point about how an excess of data can be too much of a good thing. As they receive more and more data, insurers will need to choose the right tools to handle all that information. Data is useful, but only if it can be leveraged to help make actionable decisions. The right tools can not only protect data, but sift through it to identify key trends that will help make decisions and develop business strategies.
Anshu Biswas
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Anshu Biswas,
User Rank: Apprentice
11/8/2014 | 12:54:20 PM
Data management
Nice article. I see a constant struggle of the gold copy of the data. Data duplicity is a growing concern and a strong governance to restrict growing data duplicity is the key.
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