Question

Explain the below:

Techniques of data collection, Examples of issues with data collection, Overview of some topics in data management and Overview of defining metrics

Answer 1

Answer:

1. Techniques of data collection:

a) Focus Groups/ Group Interviews

b) Surveys/Questionnaires

c)  Observations (laboratory or field)

d) Interviews

2. Examples of issues with data collection:

1 Poor quality assurance.

2 Wrong investigation methods.

3 Choosing an inappropriate time.

4 Insufficient financing.

5 Issues with logistics.

6 Lack of equipment and facilities.

7 Lack of qualified personnel.

8 Poor quality control.

9 Errors in individual data items.

10 Systematic errors.

11 Violation of protocol.

12 Problems with individual staff or site performance.

13 Fraud or scientific misconduct.

3. Overview of some topics in data management:

Data Governance:

• It is the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal data standards and policies that also control data usage. Effective data governance ensures that data is consistent and trustworthy and doesn't get misused.
• At micro level, its focus is on an individual company. It enables an organization to ensure that high data quality exists throughout the complete lifecycle of the data, and data controls are implemented that support business objectives.

Data modelling and Design:

• It is the first step in the process of database design. A data model refers to the logical inter-relationships and data flow between different data elements involved in the information world.
• It also documents the way data is stored and retrieved. It is the process of creating a data model for an information system by applying certain formal techniques.

Data Security:

• It means protecting digital data, such as those in a database, from destructive forces and from the unwanted actions of unauthorized users such as a cyberattack or a data breach.

Data Integration:

• It involves combining data residing in different sources and providing users with a unified view of them. For example, customer data integration involves the extraction of information about each individual customer from disparate business systems such as sales, accounts, and marketing, which is then combined into a single view of the customer to be used for customer service, reporting and analysis.

4. Overview of defining metrics:

• A metric contains a single type of data and are the measures of quantitative assessment commonly used for assessing, comparing, and tracking performance or production.
• Good metrics have three key attributes that are their data are consistent, cheap, and quick to collect.
• It can be classified into three categories such as product metrics, process metrics, and project metrics. Product metrics describe the characteristics of the product such as size, complexity, design features, performance, and quality level. Process metrics can be used to improve software development and maintenance.
• Project metrics describe the project characteristics and execution.
• Some metrics belong to multiple categories such as the in-process quality metrics of a project are both process metrics and project metrics.