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Governed Data Integration for Financial Services

ABSTRACT: A rising number of financial services firms are adopting the discipline of governed data integration to build 360-degree customer views.

Sponsored by Semarchy

Banking, insurance, and other types of financial services institutions face severe headwinds in the 2020s. Reduced switching costs and price competition encourage customers to jump between rivals. Siloed datasets lead to duplicative, inaccurate views of the business, hurting efficiency and impeding go-to-market programs. They also raise the risk of non-compliance with privacy laws and a web of financial regulations.

To survive and thrive in this challenging environment, FinServ companies must get control of their data. They must consolidate and streamline their data environments, improve data quality, and foster tight collaboration between business and IT departments. With a solid data foundation in place, they can build the necessary 360-degree customer views that drive sales campaigns, customer-service programs, and digital product design.

Governed Data Integration Can Help

A rising number of financial services firms adopt the discipline of governed data integration to build this foundation. Governed data integration is the convergence of three processes: discovering data across the enterprise, integrating it for analytics or operations, and governing it with master data management (MDM) and cataloging.

Governed data integration platforms help design, execute, and monitor all these tasks as part of automated workflows, using a graphical interface, monitoring, and alerting. As individual tools merge into these multimodal platforms, they enable cross-functional teams to simplify processes and democratize data access. They also improve data quality to support both operations and analytics. This blog will describe how two FinServ companies used these platforms to (1) create 360-degree customer views and (2) consolidate data from an acquired business unit.

Drilling In

Let’s drill into the ways in which governed data integration brings together data discovery, data integration, and governance to enable 360-degree business views for FinServ companies.

Governed Data Integration

Discover. Data engineers and possibly data analysts or data scientists search, expose, and profile various tables, files, and images in their sprawling environments. They evaluate these assets based on metadata such as file names, tags, lineage, usage statistics, and schema.

Integrate. Next the data engineer ingests and transforms data from sources such as legacy databases, software as a service (SaaS) applications, or IT system log files. They reformat, merge, filter, or (re) structure the datasets to prepare them for analytics or operations.

Govern. Data engineers and data stewards cleanse data by identifying and fixing errors. Then they implement MDM by matching and merging duplicate records. Finally, data engineers, data stewards, and other stakeholders catalog the associated metadata to support governance policies.

Case Studies

Now let’s consider two case studies, derived from actual Finserv companies that use Semarchy.

Case study #1: 360-degree customer view. This FinServ company offers health, life, and property insurance as well as retirement and estate planning to individuals throughout Europe. Siloed datasets prevented this company, which we’ll call SecurePlan, from providing full value to its installed base of customers. Their retirement and estate planning divisions maintained separate databases with duplicative and incompatible customer records, preventing collaboration. Operating in isolation, they forced customers to repeat themselves and listen to off-target sales pitches. This hurt SecurePlan’s efficiency, customer satisfaction (CSAT), and sales.

Governed data integration helped SecurePlan build a stronger, unified data foundation. Their data team ingested customer records from 10 siloed databases into a new cloud database, standardized their formats, then matched and merged records to create a 360-degree customer view. They also ingested clickstream logs from their website and demographic information from external brokers. This new foundation drives effective cross-selling campaigns, for example to bundle life insurance with retirement and estate planning services for policyholders over the age of 55. SecurePlan also can activate new customers faster, resolve CSAT issues faster, and identify potentially fraudulent activity with greater accuracy.

Case study #2: consolidation after an acquisition. This FinServ company, which we’ll call USave, offers insurance and commercial banking services to more than 100 million customers in Europe. After acquiring a smaller bank, USave had to consolidate databases quickly to present a single face to their account holders, avoid CSAT issues, and minimize customer churn. Their data team profiled and ingested their acquired customer records into their master database. Then they transformed, matched and merged those with existing records to create a 360-degree view of shared customers and prospects. In this way, governed data integration helped USave realize the efficiency and cross-selling benefits of its acquisition. It also reduced the effort of demonstrating compliance with industry regulations.

Summary: governed data integration is a continuous effort

As in all industries, FinServ companies will never manage to consolidate all their data. New divisions, databases, and applications will continue to arise, thanks to innovative teams and M&A activity. But FinServ companies that combine data discovery, integration, and governance processes into a governed data integration program can get ahead of the curve. They can streamline their data environment, gain efficiency, and engage customers in a more intelligent way. They also can position themselves to absorb the next inevitable data silo that arises.

Kevin Petrie

Kevin is the VP of Research at Eckerson Group, where he manages the research agenda and writes about topics such as data integration, data observability, machine learning, and cloud data...

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