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Data Modeling and Data Governance Analysis: Computer-Based Patient Record System

Customer relationship management (CRM) refers to practices, strategies and methodologies that companies use to manage and analyze customer interactions and data throughout the customer lifecycle, with the goal of improving customer service relationships and assisting in customer retention and driving sales growth (Hanseth & Lyytinen, 2016). Data modeling is a process used to define and analyze data requirements needed to support the business processes within the information systems in organizations while data governance is the execution and enforcement of authority over the management of data (Storey & Song, 2017). In building a successful customer relationship management system, it is important to analyze the data needed to make future prediction. Data governance is important as it helps to ensure security of data, proper management of data and usability of information obtained about a customer, hence the need for data modelling. It is also important to ensure that security of data is maintained to ensure integrity of information as discussed in this paper.

Data Modeling and Data Governance Analysis for the Customer Relationship Management System

Data modeling is the process of creating a data model for the data to be stored in a database (Moral-Benito, Allison, & Williams, 2018). Data model emphasizes on the data needed and how it should be organized instead of what operations need to be performed on the data. When designing the enhanced entity relationship (EER) diagram and logical database for CRM, it is not important to understand how data is implemented in the database management system rather, focus should be on what the system contains and how the system should be implemented regardless of the database management system( DBMS) ( Deng et al., 2014)

Enhanced Entity Relationship Diagram

The CPR under development will need to have data on customers collected and organized in a well-defined manner showing all entities that a customer is most likely going to interact with and how those entities are going to be organized to interact with other entities. Once the data has been organized, the CRM can then go ahead and collect the data and use it to train the model that is going to be used to produce the results showing how customer behavior can be used to better the relationship between the online system and the customers, and be able to keep current customers, acquire lost customers who were won over by competitors and attract new customers through advertisement and proper marketing of our products. Figure 1 below is a diagrammatic representation of how the customer will interact with the rest of the entities that are in the business.

Enhanced Entity Relationship Diagram

Figure 1: Enhanced entity relationship diagram

Description of the Enhanced Entity Relationship Diagram

The customers’ data is organized into several entities. These entities include ‘customers’, ‘staff’, ‘order’, ‘products’, ‘categories’ and ‘reviews’. The enhanced entity relationship diagram can be expanded further; however, the most important entities are as shown in shown in figure 1 above.

The entities in figure 1 has attributes that give a description of the entity. The common attributes are name and id. The name attributes give an identity to each of the contents in the entity while the id attribute gives a unique identification of the entity from other entities. Other attributes include: type and date modified.

The advantage that enhanced entity relationship models have over normal entity relationship diagram is the fact that EER can promote generalization and specification as  shown in figure 1 where entity “Person” is a super class and is inherited by ‘staff’ and ‘customer’. A constraint is placed on entity ‘Person’ using a double line to ensure that an entity ‘person’ cannot exist on its own unless inherited.

The EER model cannot exist without showing the relationship each entity has to another entity. In the figure above, the relationships are: a customer makes an order and an order is made by a customer relationship; an order contains many products and products are contained in an order relationship; a product belongs to a category and a category has many products; a customer can make many reviews and a review is made by a customer.

Organizing entities in such a way helps the CRM team to be able to produce reports about factors such as:

• The most bought product
• The most bought category of products
• The most bought product by a particular group of customers
• The most active customers
• Customers with most number of orders
• Customers who has been with the business the longest

These factors are helpful in coming up with well-designed data set that can be used in training a predictive model to be able to come up with predictions about the customer behaviour.

Logical Database Design

Using the information presented in the enhanced entity relationship diagram, it is possible to design a logical database model showing what kind of data and information the system contains and how that data is organized (Elmasri & Navathe, 2016). Logical data model does not require the knowledge of the database management system because the main objective is organization of data rather than implementation. The figure 2 below is a depiction of the logical data model.

Figure 2. Diagram showing logical data model

The Logical Data Model Explained

Using the EER model, the relationship between the entities can be designed and the unique attributes that allow the relationships to be created be declared. In in figure 2 above: 

• The customer may place one or many orders and an order is made by one customer making the relationship a one to many.
• An order may contain one or more products while a product may be contained in one or more orders making the relationship optional many to many
• A product belongs to a particular category while a category has one or many products making the relationship a one to many.
• A customer may make many reviews; a particular review is made by a particular customer making the relationship a one to many.

Physical Data Model Explained

A physical data model (or database design) is a representation of a data design as implemented or intended to be implemented in a database management system. In the lifecycle of a project, it typically derives from a logical data model, though it may be reverse-engineered from a given database implementation (El-Mallawany, 2016).

Using the logical data model designed above, the implementation of the database design can be explained as shown in figure 3 below:

• The attributes of the entities described above will also represent the attributes in the database design
• The entities in the logical design will represent the class of the database design
• The data types of the attributes will now be redefined
• String will be represented by varchar
• Integer will be represented by int
• Boolean will be represented by a specific Boolean choice
• Attributes that returns a null value will be represented using a void
• Relationships will further be defined and categorized as either mandatory or optional

Figure 3. Diagram showing the database design

Data Governance

Data governance is the management of usability, integrity, security and management of data used in an enterprise/organization. It includes a well-defined set of procedures and a plan to execute these procedures (Merino, Caballero, Rivas, Serrano, & Piattini, 2016). A business benefits from data governance because it ensures data is consistent, trustworthy and secure. This is very important in the development of a customer relationship system as data is needed to make business decisions, optimize operations, create new products and services and improve profitability.

Technical Plan that Addresses the Data Architecture Management Function for Data Governance

The system has been set to allow customers to be able to search for any products of their choices without authentication and authorization. When they find the product or products that interests them, then they can be able to buy the products at the exact moment or to add the products to a wish list that they can be able to use to purchase the products at a later time. However, when the customer wishes to purchase a particular product(s), the system is designed to authenticate that the user is an existing customer. If they are already members, then they are prompted to log in. Once they have entered their credentials, the information is compared with the existing information in the database for when they signed up earlier, if it does not match, then no authorization is given to the customer. However, if the credentials matches with the information stored in the database, then the permission is given to the customer to proceed. If the customer is not registered, then they are asked to provide their credentials before proceeding further. Using a role model access control, different roles are created, but the main ones are customer with standard rights and permissions and administrators with all rights and permissions, meaning they can add or delete or update any product they wish. In addition, they have permissions to alter any information in the system. The customer may contact the admin through a contact page by providing their email address which the administrator will use to give a response to them.

Conclusion

In conclusion, data modeling allows an organization to work out a plan before offering it up to users while data governance as Sandwell said in ‘Enterprise Data World 2016 Conference’, creates trust in the data, so that end-users see it as a valuable, accessible resource for decision making, therefore, it is important to have a professional data model and a good data governance strategy.