Data Modeling for System Analysis

By: Varuni Mallikaarachchi

Data Modeling

A data model is a description of how data should be used to meet the requirements given by the end user (Ponniah).  Data modeling helps to understand the information requirements. Data modeling differs according to the type of the business, because the business processes or each sector is different, and it needs to be identified in the modeling stage. Initial step is the analyzing the situation, gather data. Data modeling process starts with requirement gatherings. When developing the proper data model it is important to communicate with the stakeholders about the requirements. Data modeling is the act of exploring data oriented structures. This can be used for variety of purposes. One of the important functions of data modeling is that, it helps to understand the information requirements. Especially this makes both developers and end users lives easier. As mentioned above, data modeling helps the end users to define their requirements, and the developers are able to develop a system to meet those specified requirements.


Figure 1: The Systems Engineering Process - [7]

Data model is a conceptual representation of data structures required for a database and is very powerful in expressing and communicating the business requirements (Learn Data Modeling). It visually represents the nature of data, business rules that are applicable to data, and how it will be organized in the database. There are three main designs for the data model, namely conceptual design, logical design and the physical design (Itl Education Solutions Limited). Data model is used by both functional team and the technical team in a project. Functional team consists of the business analysts and the end users, and the technical team consists of the developers and the programmers. There are data modelers who are responsible for designing the data model which meets the expectations of the functional team, and provide requirements for the technical team (Chuck Ballard, Dirk Herreman, Don Schau, Rhonda Bell, 1998).

Levels of Data Models


Figure 2: Levels of Data Models [22]

History of Data Models
In 1970s, Peter Chen invented and introduced the entity-relationship modeling technique. In 1980s the object modeling techniques started applying to representing information requirements of an organization. Then the unified modeling language (UML) was introduced to replace the object modeling methods. (Hay, Requirements Analysis: From Business Views to Architecture)

Data Modeling Process
Data modeling process starts with analyzing the situation. Here the analysts are able to gather requirements, when designing a proper data model it’s important to communicate with the stakeholders about the requirements. Data modeling is the act of exploring data oriented structures, which can be used for multiple purposes. Mainly data modeling is a communication tool among users, which considers as the blue print of the database system. (Merson, Paulo F.)

Data Analysis
The techniques of data analysis can impact the type of data model selected and its content. For example, if the intent is simply to provide query and reporting  capability, a data model that structures the data in more of a normalized fashion would probably provide the fastest and easiest access to the data. Query and reporting capability primarily consists of selecting associated data elements, perhaps summarizing them and grouping them by some category, and presenting the results. Executing this type of capability typically might lead to the use of more direct table scans. For this type of capability, perhaps an ER model with a normalized and/or denormalized data structure would be most appropriate.


Figure 3: Several methods of data analysis [5]

A data model consists of three different phases. (West)Those are:
Structural part – Consisting a set of rules
Manipulating part – Types of operations allowed, such as updating, retrieving, and changing the database
Integrity part – which validates the accuracy of data.

Figure 4: West & Fowler has identified many benefits of a data model. Above figure depics the details of these benefits of using a data model. [25]

There are four types of data models identified:

Conceptual Data Models
According to Hoffer et al. Conceptual data model is a representation of organizational data. The purpose of a conceptual data model is to show as many rules about the meaning and interrelationships among data as are possible. Conceptual data modeling is typically done in parallel with other requirement analysis and structuring steps during system analysis. This is carried out throughout the systems development process. This is useful for both planning and analysis phases in the systems development life cycle (Valacich). Conceptual data model contains about10 - 20 entities and relevant relationships known as group entities. Conceptual data modeling is the most crucial stage in the database design process. Peter Chen states entity relationship model as a “Pure Representation of reality”


Figure 5: Conceptual Data Modeling Process

According to Jarrar, Demey, and Robert, identifies two main differences of conceptual data schemes and ontologies which should be taken into consideration when reusing the conceptual data modeling techniques for building ontologies. Paper further discusses that the successful conceptual data modeling approaches, such as ORM (object role modeling) or EER (Enhanced entity relationship model) became well known because of the methodological guidance in building conceptual models of information systems. (M Jarrar)

Enterprise Data Model (External Data Model)
An Enterprise Data Model is an integrated view of the data produced and consumed across an entire organization.  It incorporates an appropriate industry perspective.  An Enterprise Data Model (EDM) represents a single integrated definition of data, unbiased of any system or application.  It is independent of “how” the data is physically sourced, stored, processed or accessed.  The model unites, formalizes and represents the things important to an organization, as well as the rules governing them. (Ponniah) (Noreen Kendle)

Figure 6: Enterprise Data Modeling Structure [19]

Logical Data Model
The logical data model is an evolution of the conceptual data model towards a data management technology such as relational databases. Actual implementation of the conceptual model is called a logical data model. To implement one conceptual data model may require multiple logical data models. Data modeling defines the relationships between data elements and structures

Figure 7: Logical Data Model

Physical Data Model

Physical data model is a representation of a data design which takes into account the facilities and constraints of a given database management system. Physical data model represents how the model will be built in the database. A physical database model shows all table structures, including column name, column data type, column constraints, primary key, foreign key, and relationships between tables.

Figure 8: Physical Data Model

Multidimensional data modeling
Multidimensional structure is defined as “a variation of the relational model that uses multidimensional structures to organize data and express the relationships between data. According to Jensen et al. multidimensional models view a central data element for the given domain, which uniquely defined by a combination of dimension values (Christian S. Jensen)

Newspeak – Tower of Babel Dilemma in Data Modeling
This is the fundamental design problem for information systems. Creating a standard model for the whole company with different data interpretation of an organization, this is known as the Newspeak solution. Allowing multiple and incompatible models to coexist can lead to Tower of Babel problem. Because of the conflicts the system designers can either create an enterprise wide data model or create multiple models to meet each requirement (Federico Fonseca). Problems can arise due to miscommunication, and when the information system is not working the way it was designed.

Agent based models
An agent-based model (ABM) (also sometimes related to the term multi-agent system or multi-agent simulation) is a class of computational models for simulating the actions and interactions of autonomous agents (both individual and collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo Methods are used to introduce randomness. ABM's are also called individual-based models. Nigel Gilbert has defined Agent-based Modeling as a new analytical method for social sciences which is quickly becoming popular. Further, agent based modeling is a computational method that enables a researcher to create, analyze, and experiment with models composed of agents that interact within an environment.
There are nine techniques will help to model a agent based system, these techniques include,
Preciseness, accessibility, expressiveness, modularity, complexity management, excitability, refinability, analyzability, and openness (Gilber)

Importance of Agent based modeling in systems analysis:
In the paper by Osinga, states how an agent-based model has used as a modeling method to investigate the relationship between system level and agent level behavior.
There are three business modeling types:

Agile Modeling and Analysis Techniques
Agile Modeling: Agile modeling is  a collection of values, principles, and practices for modeling software that can be applied on a software development project in an effective manner. Agile modeling includes creating several models in applying right artifacts for the situation, and continue to move forward.

Best Practices of Agile Modeling

Figure 9: Best Practices of Agile Modeling

 Agile Analysis

The purpose of analysis is to understand what will be developed, why it should be built, estimate the cost, and prioritize the developing process. The main difference is that the focus of requirements gathering is on understanding your users and their potential usage of the system, whereas the focus of analysis shifts to understanding the system itself and exploring the details of the problem domain.  Another way to look at analysis is that it represents the middle ground between requirements and design, the process by which your mindset shifts from what needs to be built to how it will be built. According to the author, there are three major challenges related to roles and responsibilities including conflict of team structure and agile principles, applying product owner role in a large and complex context, and lack of business theme priorities (Ilkka Lehto)


Figure 10: Agile product planning and development

KANBAN approach to Data Modeling and Analysis

KANBAN: Meaning "visible record" in Japanese, it is a system of notification from one process to the other in a manufacturing system. Kanban cards, which may be multicolored, based on priority, are stored in a bin or container that holds the items. They describe the parts, supplier and quantity. When the bin is emptied, the Kanban is used to order more. A two-card Kanban system uses "move" cards to relocate items from one workplace to another and "production" cards to replace the material when it is used or sold (Nelson-Smith). A simple analytics model – for the data analysis purpose we can monitor the analytics of a website visitor. This is another use of data models, because each step using analytics tools, the owner of the website is able to monitor the success of failure of a website/product.

Figure 11: A simple analytics model

Lean approach, common on Kanban teams, to requirements management.  There are several key differences between the agile approach to requirements/work management and the lean approach:


Figure 12: Lean Data Modeling


Data modeling is generally performed in the context of an information systems project with relevant methodology and tools.  Also data modeling is useful in representing and documenting data. Data model can be used as a map to go from start to finish. It’s the modern GPS for both IT and business professionals who are involved in a project to navigate on the correct path.
Advancing the process by eliminating the non value added steps will make it lean, as well as reduce the unwanted steps.


Works Cited

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