DATA LAKES AND WAREHOUSES – software

Banking institution consolidates customer data with Master Data Management solution.

Overview

The banking institution had varied customer data across six different systems, each managing specific business needs of the customer, like commercial, residential, collateral, accounts, trades, and support.

Users did not have any insight into the customer relationship across the bank, thus hindering their ability to make critical financial decisions.

A scalable platform was needed to define the customer as a single entity and allow all its interactions with the bank to be retained and displayed securely at any time​.

A Master Data Management process was needed to avoid duplicate customer records and match/merge the data while transforming it into a Data Warehouse that captured all customer interactions.

What benefits could your organization realize with a single source of truth for all of your data

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Our Solution

One Six Solutions provided the MDM strategy, design, and architecture and built out the C360 solution that included a cloud-based Data Lake, Data Warehouse, and a rules engine. The BI needs were also addressed.

A set of new technology and tools were introduced to the customer that best fit their needs. The technical team was trained on best practices to utilize the new tools and an operational model was established to support the solution.

Data was ingested at varying frequencies per system using different Fivetran connectors and direct near real-time database connections using Snowflake data share​.

Strategic direction was provided to expand the use of the new tools in the other areas of the organization.

One Six Solutions took advantage of many robust cloud data architecture solutions to create the right arrangement for our client’s needs:

Snowflake’s data platform served as the host to collect data into the Data Lake across various systems. A C360 Data Warehouse served the BI and Customer information needs of this bank

Fivetran was used to capture data across a variety of data sources to move data into the Data Lake. In addition, Snowflake Data Share provided access to raw data for a Snowflake-based system

Matillion was used as the rules engine to cleanse data and provide match/merge rules for the customer record and transform that into the Data Warehouse

Our client’s existing investment in PowerBI was used to drive analytics and display Customer 360 information across the institution

The Results

The customer’s relationship with the bank was consolidated into a single platform utilized by various entities like customer support, call centers, and decision-makers to offer products and services.

The bank was able to identify their most valuable customers, say, a customer with a large portfolio of commercial real estate, or extensive savings, requesting a residential mortgage.

The Master Data rules helped define the customer as a single entity, thus avoiding duplicate entries of customer records. The customer can tweak the rules engine as needed for any unique match/merge scenarios that were not covered