Data Strategy, Data Governance, Data Quality, MDM, Metadata Management, and Data Architecture
Data domains can be defined as "areas of data management, control, and knowledge." But how do we separate data into domains? One common method is to categorize data by subject area. For example, in academic institutions, subjects might include students, courses, faculty, financial aid, research, admissions, etc. Another way to organize data is by corporate functions and business units. For example, we could have domains for sales, marketing, finance, HR, legal, and compliance. Additionally, we could define domains for various business units. For instance, a company might have one business unit responsible for retirement solutions, another for insurance solutions, and yet another for investment management. However, if we choose to organize data by corporate functions, it’s important to note that these functions can change quickly during reorganizations. As a result, we might need to scramble to adjust data domains if the corporate structure changes. Another approach is to group data by geography. For example, we might have regions like EMEA (Europe, Middle East, and Africa), North America, Latin America, or Asia-Pacific. In large enterprises, it is common to organize data based on various combinations of these methods. For example, we could combine geography and subject areas. This might result in domains such as EMEA customers, APAC customers, and LATAM customers. Reality is that different data could be collected in different regions for the same subject. Also, the policies governing the data often vary across regions. Another typical scenario is combining corporate functions and subject areas. So, within a ‘Retirement Solutions’ business unit we can separate data for Plan Sponsors, Plan Participants, and Beneficiaries. Yet another scenario involves structuring data in a way that aligns with various business processes within a particular business or function. Within the 'Retirement Solution' business segment, we can define typical business processes and organize data accordingly. For example, we will have a data domain for client onboarding, which includes data needed to set up a client account. Then, there will be data domains for plan administration and recordkeeping. One important thing to note is that the breakdown by data domain must reflect the business reality of the company. It should never be a breakdown too far removed from the reality on the ground (for example, the breakdown suggested by ChatGPT 😀 ). Another key point to remember is the definition of data domains: 'areas of data management, control, and knowledge.' Therefore, set up domains in such a way that ownership and accountability can be assigned.