Week 3 DQ Responses

Q1. When is Gray Hat Hacking Ethical?

Note: 300 words with intext citations and references needed.

Q2. Read the below paragraph and write about 150 words with intext citations and references needed.

Data rich, information poor can be described as a situation in which an organization has substantial amounts of data but is lacking the procedure to derive some meaningful information from it. Today's technologies provide organizations with ample data, but keeping data is not the same thing as having the crucial grade information that one needs to make use of it (Baltzan, 2017). Business intelligence integrates business analytics, data mining, data visualization, data instruments and infrastructure, and the best techniques to help companies make more data-driven decisions. After collecting data, the organization should look to extract the important parts of it. They should filter the data to reduce it to only the required parts. Data Aggregation is the collection of data from various sources. It is the process where raw data is gathered and presented in an analytical form. This data is unfiltered. Data cleansing is the process of removing the data that is not required from the dataset. Data that is removed is usually incorrect, corrupted, repeated, or incomplete. Having clean data will enable an organization to take better decisions as the data set will be of the highest grade. This will also increase the overall productivity of a business (Baltzan, 2017).

Q3. Read the below paragraph and write about 150 words with intext citations and references needed.

Business intelligence is generally defined as the process of data warehousing using technology. With the use of software products that are exclusively dedicated to aid in decision making, it may not be categorized in the data warehouse category. It is an activity for ensuring decisions are viable and helps in assisting decisions taken by managers (Stanciu, Mihai & Aleca, 2009). Data aggregation is very useful to make an informed decision and hence in decision making. A case example: The use of data aggregation in the electricity domain states that there could be abnormal data obtained from an electric meter due to failure of the meter or even theft (Zhang & Han, 2022). If the abnormal data is not filtered out, the results would be inaccurate and hence hinder the decision making of the power system and harm the user. Therefore, cleaning the data or filtering the abnormalities is essential during data aggregation. The method proposed in a research paper suggested the use of “lightweight matrix encryption” which helps filter out abnormal data and provides data in an acceptable range (Zhang & Han, 2022).