Business Goals
Our client is a UK-based online investment management company headquartered in London that offers personalised investment portfolios to customers. They use technology to create and manage diversified investment portfolios tailored to individual investment goals and risk tolerance. They provide various investment products and services, including ISAs, pensions, and general investment accounts. The company has won several awards for its innovative approach to investment management.
Over the years, the company has grown significantly and expanded its operations to multiple regions. As a result, the company’s customer data was stored in various systems, making it difficult to get a complete view of the customer. The lack of a single source of truth for customer data was causing data inconsistencies and making it challenging to provide personalised customer experiences.
Technology Challenges
Our client operates in many cities across the UK. Each unit has its repository and version of customer data. These business units were not managed or governed by any organisation-wide data policy. Hence the data required cleaning and validation before being put into the golden repository. Some of these business units had their legacy data stores synchronised with the organisation’s CRM.
Salesforce Sales Cloud was their primary platform for managing sales, customers and leads. SAP CRM was used by some business units, and some used legacy CRM/data stores. So, they required seamless integration between various records systems to create a single source of truth of the customer data.
How NeosAlpha helped
Our MDH implementation was divided into the 4 following critical stages,
- Data profiling: The first step was to profile the customer data from various systems to identify any data inconsistencies or errors. It helped us to identify potential data quality issues and provided insights into the data that can be used to improve data accuracy, completeness, and consistency.
- Data modelling: The next step was to create a data model that defined the customer data structure in Boomi Master Data Hub.
- Data integration: The third step was integrating customer data from various systems into Boomi Master Data Hub. Boomi’s integration capabilities were used to extract data from the source systems and transform it into the required format before loading it into Boomi Master Data Hub. In addition, Boomi’s out-of-the-box application connectors for Salesforce and SAP made it easier to connect to these systems and pull data from them.
- Golden Records: Using Boomi MDH’s data quality and matching rules, we configured the business logic required to identify and eliminate duplicates. Records that post-processing needed by a data steward were stored in the quarantine.
Results
- Reduced the time and effort required to manage customer data by automating data integration and governance processes.
- Offered customised value propositions to the customers.
- Improved data accuracy helped the business prevent data duplication and inconsistency, leading to errors and confusion.
