Untangled business data leads to trusted information
The Situation
A large financial establishment has separate divisions for supporting individual/personal investments from workplace/employee investments. There is potentially a lot of overlap among the individuals in these two separate divisions. They wish to increase cross-selling of services to individuals, understanding their full investment relationships to the company as individuals and as employees of their workplace clients. They merge the two divisions in support of that sales goal.
The Problem
Effective cross-selling has not been realized two years after the merging of the company divisions. Both divisions have their own corporate vocabulary. Terms like “customer”, “client”, “account”, “member”, and “market” have different meanings within sub-groups of the merged division. The systems that support each former division reinforce terminology differences, sometimes adding even additional twists in meanings – i.e. terms within the previous divisions were not consistent within the division! Special lists of individuals are compiled and excluded from analytics systems processing lest there be a mistake by sales or marketing in calling on these particular high net worth individuals. In-house terms as basic as “Market” refer to support groups within the company, not to the external marketplace. Confusion reigns and exception coding abounds when a for-profit client is classified with a Market Code of “Tax Exempt” simply because their support is provided by an internal group that handles tax-exempt clients.
MV360 Solution
Metaview360 worked with the Data Stewards to create an effective company vocabulary for customers and markets which would eliminate previous ambiguities and errors. We conducted workshops with intact groups to teach them how to derive the true semantics and accompanying standard names for the terminology they use in the business and in the systems. We mentored the Data Stewards through publication of the new standards as a shared source of approved definitions. Standard names and descriptions are now enforced via the source-to-target data mappings when data moves from the transaction systems to the Data Warehouse environment.
The Result
Many of the existing ambiguities were eliminated from the business vocabulary. E.g. a client company is now classified according to industry-standard classification codes based on the type of business products and services they created, AND they are associated with a particular support group within the financial institution. It was no longer acceptable to mash multiple semantic meanings into one “Code”.
The Data Governance organization was empowered to speak the truth to the business. They educated executive steering committee members, articulating how certain “codes” that were imposed from above were hurting the business. Data Governance made the case that multiple concepts required multiple semantic codes which would enable the business units to finally obtain the analytics they needed – and at less cost. If monolithic legacy systems could not be reliably changed, data could be associated with the new, meaningful codes as it moved from the transaction systems to analytic environments.