Data Storage and Operations includes the design, implementation, and support of stored data, to maximize its value throughout its lifecycle, from creation/acquisition to disposal. Data Storage and Operations includes two sub-activities:

• Database support focuses on activities related to the data lifecycle, from initial implementation of a database environment, through obtaining, backing up, and purging data. It also includes ensuring the database performs well. Monitoring and tuning are critical to database support.

• Database technology support includes defining technical requirements that will meet organizational needs, defining technical architecture, installing and administering technology, and resolving issues related to technology.

Database administrators (DBAs) play key roles in both aspects of data storage and operations. The role of DBA is the most established and most widely adopted data professional role, and database administration practices are perhaps the most mature of all data management practices. DBAs also play dominant roles in data operations and data security.

April 2024 Newsletter.pdf

To build the models, modelers often rely heavily on previous analysis and modeling work. They may study existing data models and databases, refer to published standards, and incorporate any data requirements. After studying these inputs, they start building the model. Modeling is a very iterative process (this Figure). Modelers draft the model, and then return to business professionals and business analysts to clarify terms and business rules. They then update the model and ask more questions (Hoberman, 2014).

Abstraction is the removal of details in such a way as to broaden applicability to a wide class of situations while preserving the important properties and essential nature from concepts or subjects. An example of abstraction is the Party/Role structure, which can be used to capture how people and organizations play certain roles (e.g., employee and customer). Not all modelers or developers are comfortable with, or have the ability to work with abstraction. The modeler needs to weigh the cost of developing and maintaining an abstract structure versus the amount of rework required if the unabstracted structure needs to be modified in the future (Giles 2011).

Abstraction includes generalization and specialization. Generalization groups the common attributes and relationships of entities into supertype entities, while specialization separates distinguishing attributes within an entity into subtype entities. This specialization is usually based on attribute values within an entity instance.

Subtypes can also be created using roles or classification to separate instances of an entity into groups by function. An example is Party, which can have subtypes of Individual and Organization. 

The subtyping relationship implies that all of the properties from the supertype are inherited by the subtype. In the relational example shown in this figure, University and High School are subtypes of School.

Subtyping reduces redundancy on a data model. It also makes it easier to communicate similarities across what otherwise would appear to be distinct and separate entities.

This figure illustrates a dimensional physical data model (usually a star schema, meaning there is one structure for each dimension).

Similar to the relational physical data model, this structure has been modified from its logical counterpart to work with a particular technology to ensure business questions can be answered with simplicity and speed.

A variant of a physical scheme is a Canonical Model, used for data in motion between systems. This model describes the structure of data being passed between systems as packets or messages. When sending data through web services, an Enterprise Service Bus (ESB), or through Enterprise Application Integration (EAI), the canonical model describes what data structure the sending service and any receiving services should use. These structures should be designed to be as generic as possible to enable re-use and simplify interface requirements.

This structure may only be instantiated as a buffer or queue structure on an intermediary messaging system (middleware) to hold message contents temporarily.

A physical data model (PDM) represents a detailed technical solution, often using the logical data model as a starting point and then adapted to work within a set of hardware, software, and network tools. Physical data models are built for a particular technology. Relational DBMSs, for example, should be designed with the specific capabilities of a database management system in mind (e.g., IBM DB2, UDB, Oracle, Teradata, Sybase, Microsoft SQL Server, or Microsoft Access).

This figure illustrates a relational physical data model. In this data model, School has been denormalized into the Student entity to accommodate a particular technology. Perhaps whenever a Student is accessed, their school information is as well and therefore storing school information with Student is a more performant structure than having two separate structures.

A dimensional logical data model is in many cases a fully-attributed perspective of the dimensional conceptual data model, as illustrated in this figure. Whereas the logical relational data model captures the business rules of a business process, the logical dimensional captures the business questions to determine the health and performance of a business process.

Admissions Count in this figure is the measure that answers the business questions related to Admissions. The entities surrounding the Admissions provide the context to view Admissions Count at different levels of granularity, such as by Semester and Year.

March 2024 Newsletter.pdf

A logical data model is a detailed representation of data requirements, usually in support of a specific usage context, such as application requirements. Logical data models are still independent of any technology or specific implementation constraints. A logical data model often begins as an extension of a conceptual data model.

In a relational logical data model, the conceptual data model is extended by adding attributes. Attributes are assigned to entities by applying the technique of normalization, as shown in this figure. There is a very strong relationship between each attribute and the primary key of the entity in which it resides. For instance, School Name has a strong relationship to School Code. For example, each value of a School Code brings back at most one value of a School Name.

A conceptual data model captures the high-level data requirements as a collection of related concepts. It contains only the basic and critical business entities within a given realm and function, with a description of each entity and the relationships between entities.

For example, if we were to model the relationship between students and a school, as a relational conceptual data model using the IE notation, it might look like Figure 46.

Each School may contain one or many Students, and each Student must come from one School. In addition, each Student may submit one or many Applications, and each Application must be submitted by one Student.

The relationship lines capture business rules on a relational data model. For example, Bob the student can attend County High School or Queens College, but cannot attend both when applying to this particular university. In addition, an application must be submitted by a single student, not two and not zero.

Recall Figure 40, which is reproduced below as Figure 47. This dimensional conceptual data model using the Axis notation, illustrates concepts related to school: