4.5 Data Gathering Techniques for Each Application Type

 In software development, understanding the specific data gathering techniques applicable to different types of applications is crucial. Each application type—such as Transaction Processing Systems (TPS), Decision Support Systems (DSS), Group Decision Support Systems (GDSS), Executive Information Systems (EIS), and Expert Systems (ES)—has unique characteristics that influence how data is collected. Below, we will explore these techniques in detail, highlighting their strengths and weaknesses, while providing a comprehensive overview.

Overview of Data Collection Techniques

Collecting information about software applications can be achieved through various methods. The effectiveness of each technique can vary significantly based on the application type and the specific context. The primary techniques include:

• Interviews
• Group Meetings
• Observation
• Questionnaires
• Temporary Job Assignments
• Document Review
• Software Review

These methods can be tailored to gather the most relevant data for each application type, ensuring that developers acquire the necessary insights to inform their work.

Data Collection Techniques and Their Attributes

The following table summarizes the attributes of different data collection techniques in relation to various factors like time orientation, structure, completeness, ambiguity, semantics, and volume.

Table 1: Data Collection Techniques Overview

Technique	Time Orientation	Structure	Completeness	Ambiguity	Semantics	Volume
Interview	All	All	All	Varies	All	All
Meeting	All	All	All	Varies	All	All
Observation	Current	Unstructured	Incomplete	Varies	Varies	Crude measure
Questionnaire	All	Structured	Complete for questions asked	Low	Fixed but may vary	Individual volumes only
Temporary Job Assignment	Current	Unstructured	Incomplete	Low to Medium	Varies	Period of observation
Internal Documents	Past to Current	Unstructured	Incomplete	Low to Medium	May vary	N/A
External Documents	Mostly Current to Future	Unstructured	Incomplete	Low to Medium	Relatively fixed	N/A
Software Review	Past to Current	Structured	Complete for software	Low to Medium	Fixed	Maybe

Explanation of Each Technique

1. Interviews

   • Description: One-on-one discussions that allow for in-depth exploration of user needs and experiences.
   • Strengths: Provides comprehensive insights and can cover all types of information.
   • Weaknesses: May be influenced by interviewer bias and can be time-consuming.

2. Group Meetings

   • Description: Facilitated discussions among stakeholders to gather collective insights.
   • Strengths: Encourages diverse input and can surface consensus quickly.
   • Weaknesses: Dominant voices may overshadow quieter participants, and discussions can veer off-topic.

3. Observation

   • Description: Directly watching users interact with the software to understand workflows.
   • Strengths: Offers real-time insights into user behavior and problems.
   • Weaknesses: Results may be inconsistent and dependent on the observer’s interpretation.

4. Questionnaires

   • Description: Structured forms with predefined questions distributed to a large audience.
   • Strengths: Efficient for gathering data from many users, allowing for quantitative analysis.
   • Weaknesses: Limited to the questions asked and may not capture nuanced information.

5. Temporary Job Assignments

   • Description: Analysts immerse themselves in user roles to gain firsthand experience.
   • Strengths: Provides deep insights into user challenges and workflows.
   • Weaknesses: Can be time-consuming and may not represent typical work conditions.

6. Internal Documents

   • Description: Review of existing documentation, such as policies and procedures.
   • Strengths: Offers historical context and understanding of established processes.
   • Weaknesses: Documents may be outdated or incomplete, leading to inaccurate conclusions.

7. External Documents

   • Description: Analysis of industry reports, regulations, and market studies.
   • Strengths: Provides insights into trends and standards outside the organization.
   • Weaknesses: May not be directly applicable to specific organizational contexts.

8. Software Review

   • Description: Examination of existing software to understand its design and functionality.
   • Strengths: Reveals constraints and capabilities of current systems.
   • Weaknesses: Documentation may be lacking, and the software may be outdated.

Data Types and Application Types

Each application type has distinct data characteristics that influence the choice of data collection techniques. Below is a summary of the application types, categorized by their data attributes.

Table 2: Application Types by Data Type

Application Type	Time Orientation	Structure	Completeness	Ambiguity	Semantics	Volume
TPS	Current	Structured	Complete	Low	Fixed	Any
Query	Past, Current	Structured	Complete	Low	Fixed	Any
DSS	All	Structured	Varies	Low to Medium	Varies	Medium to High
GDSS	Current to Future	Unstructured	Incomplete	Medium to High	Varies	Low
EIS	Future	Unstructured	Incomplete	Medium to High	Varies	Low to Medium
Expert System	Current based on Past	Semi-structured	Incomplete	Medium to High	May vary	Low

Detailed Examination of Each Application Type

1. Transaction Processing Systems (TPS)

   • Characteristics: These systems handle day-to-day operations and require structured, current, and complete data.
   • Data Collection Techniques: All methods are effective, with interviews and meetings being the most common due to their ability to elicit detailed responses rapidly.

2. Query Applications

   • Characteristics: Focus on both past and current data to generate reports and summaries.
   • Data Collection Techniques: Similar to TPS, benefiting from all techniques, especially interviews and document reviews to ensure completeness and accuracy.

3. Decision Support Systems (DSS)

   • Characteristics: Designed to assist in complex decision-making processes, often utilizing varied data types.
   • Data Collection Techniques: While all techniques are applicable, questionnaires and interviews are particularly useful for gathering diverse user needs and insights.

4. Group Decision Support Systems (GDSS)

   • Characteristics: Facilitate collaborative decision-making among groups, often dealing with unstructured data.
   • Data Collection Techniques: Primarily reliant on interviews and external documents to understand group dynamics and requirements.

5. Executive Information Systems (EIS)

   • Characteristics: Provide high-level executives with insights into organizational performance and external trends.
   • Data Collection Techniques: Interviews and external documents are key, as EIS are tailored for specific executive needs and may not benefit from traditional methods like questionnaires.

6. Expert Systems (ES)

   • Characteristics: Mimic human expertise and reasoning processes, often working with semi-structured data.
   • Data Collection Techniques: Interviews and observations are crucial, as they help extract tacit knowledge from experts that may not be readily articulated.

Table 3: Data Collection Techniques by Application Type

Technique	TPS	Query	DSS	GDSS	EIS	Expert System
Interview	X*	X	X	X	X	X
Meeting	X	X	X	X	X	X
Observation	X	X	X	Limited	Limited	X
Questionnaire	X	X	X
Temporary Job Assignment	X	X	X
Internal Documents	X	X	X	Limited
External Documents	X	X	X	X	X	X
Software Review	X	X	X	Limited	Limited	Limited

*Boldface indicates the most frequently utilized method for each application type.

X: This indicates that the specific data collection technique is suitable and can be effectively used for that application type.

X*: The bold "X" denotes the most frequently used or primary method for that application type, highlighting its importance in the data gathering process.

Conclusion

Understanding the interplay between data collection techniques and application types is essential for effective software development. Each method has unique strengths and weaknesses that can enhance or hinder the information-gathering process. By carefully selecting the appropriate techniques based on the application's characteristics, software engineers can ensure they gather comprehensive and relevant data to inform their development efforts. This detailed approach not only enhances the quality of the software but also aligns it more closely with user needs and organizational objectives.