MetLife Uses NoSQL For Customer Service Breakthrough
MetLife uses 10Gen's MongoDB database to quickly integrate disparate data and deliver a consolidated view of the customer.
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Just about every company with the combination of lots of customers and lots of points of customer interaction aspires to build the proverbial 360-degree customer view. All too many fail, with disparate systems and data being the usual culprit in failed attempts to gain a consolidated customer view.
Developing an integrated customer view has been on the wish list at insurance giant MetLife for at least 10 years, but it recently took a fresh approach to the challenge by choosing a NoSQL database as the platform for bringing together data from more than 70 separate administrative systems, claims systems and other data sources. It moved from pilot to rollout in 90 days -- breakneck speed in an industry used to measuring IT projects in months and years.
"We had 60 different teams working together as one group, and they were working nights and weekends not because they had to but because they were excited and wanted to," says Gary Hoberman, MetLife's senior VP and CIO of regional application development.
The choice of NoSQL for the project makes sense because these databases can ingest structured, semi-structured and unstructured information without requiring tedious, expensive and time-consuming database-mapping or extract, transform and load (ETL) processes to normalize all data to a rigid schema, as required by relational databases.
[ 10Gen's Matt Assay will be a keynote speaker at the upcoming E2 Conference in Boston, June 17 - 19. ]
Like any big company, MetLife has a profusion of product lines and supporting systems. Some systems are home grown, some are commercial software products and some are commercial or home-grown apps gained through acquisitions. Many systems have to meet complex federal and state regulatory requirements imposed on the annuity and individual- and group-insurance products that MetLife sells.
Ripping out, replacing or otherwise touching these mission-critical systems of record was out of the question. So how could MetLife access information from these diverse sources? NoSQL databases have emerged in recent years as a diverse and scalable option.
MetLife also confronts its share of data-quality and data-diversity challenges within systems. By definition, life insurance and annuity products are long lived, but as healthcare and the insurance business have evolved, so, too, have data-collection requirements and standards. Today's policy records, for example, have many more fields of data than the records behind policies issued in the 1990s, 1970s or 1950s. Look across the corpus and you have what might be described as ragged or sparse data with missing fields -- another argument for NoSQL.
Finally, MetLife deals with semi-structured and unstructured information, such as images of health records and death certificates. This contributed to MetLife's selection of MongoDB -- 10Gen's open source document database -- over other NoSQL alternatives such as Cassandra, which MetLife is testing in other applications.
"Everything we know about a customer and everything we know about a policy stores into a single JSON [Java Script Object Notation] document," says Hoberman, one of three top IT execs at MetLife who report up to the Global CIO. "Any other database wouldn't allow us to view customers as a single record without caring about structure at all. With Mongo, we can bring a group policy and an individual policy together without any [data] normalization, and we use a Web services layer and the application to render the best view of that data."
MetLife worked with software development firm Infusion to select the database and together they envisioned an interface akin to a Facebook Wall. The screen shows a customer profile listing all products owned on the left together with a reverse-chronological timeline of events on the right. The event feed shows all interactions with contact centers, logins to various websites, and in-person interactions with insurance agents, claims specialists, employer administrators and other touch points.
Once the key technology and interface decisions were finalized late last year, it took just two weeks to build a prototype and seed it with 2 million fake customer records to prove that it would scale.
"Within a few weeks of building the prototype, we were in front of the executive group of MetLife presenting a live demo, and the excitement in the room was tremendous," according to Hoberman, who says the project was given an immediate green light.
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