Answer all questions and ensure that each answer is no longer than 500 words (any writing above 500 words for any one question will not be marked).

Answer ALL questions.

Question 1:

Describe Blockchain technology and explain how it can benefit data security. (25 marks)

Blockchain technology is used to securely store data by creating an immutable digital ledger. The ledger is composed of blocks linked together using cryptography, which makes the system impossible to hack, mismanage, or edit.

Each block of a blockchain consists of three components: the data, the hash of the block, and the hash of the previous block. The data is specific to the information being transferred, such as the sender and receiver of a bitcoin transaction. A hash is like a unique identifier that keeps the block intact; any change in the block will cause the hash to change, alerting the network to tampering. Furthermore, the hash of the previous block is a crucial element in the security of the blockchain. When the data in a block is altered, this too will change the hash of the block, enabling the network to detect any changes.

Blockchain technology ensures data security by implementing cryptography, decentralization, and consensus principles. By ensuring all blocks in the blockchain are connected via a cryptographic chain, tampering becomes extremely difficult. Furthermore, decentralization allows for multiple computer systems and ensures that a single system hacking will not lead to an entire network breach. Finally, the consensus of the network ensures that all transactions are transparent, and user approval is required at every stage, making the process safe and secure.

Question 2:

Explain the Extract-Transform-Load process for Data Warehousing. Compare and contrast Data Warehouses vs. Data Lakes. (25 marks)

The Extract-Transform-Load process is used to extract data from various sources, transform it to make it more useful and load it into the data warehouse system. The process consists of three stages.

The first stage, extraction, involves taking data from different sources and moving it to the staging area so that it may be formatted to fit the data warehouse system. The data may be in various formats, so it's essential to extract it into the staging area before it can be transformed.

In the transformation stage, the data is cleaned and made more useful by applying standard rules to the data. Data cleansing is accomplished by resolving inconsistencies and missing values, and verification is performed to ensure that unusable data is discarded, flagging anomalies.

Finally, in the loading stage, data is loaded all at once, which is completed via full loading, or it is loaded at scheduled intervals, represented by incremental loading.

Data Warehouses and Data Lakes have their own unique characteristics. Data Warehouses are used to store structured data and are apt for data mining, supporting processes such as business intelligence reporting. The data is optimized to support business queries, providing users with high levels of data protection and consistency. In contrast, Data Lakes store both structured and unstructured data and are utilized for ad-hoc data analysis. Since Data Lakes aren't optimized for speed, they don't provide a high level of data consistency.

The availability and flexibility of data lakes make it easily accessible and manipulatable, with no set structure involved. In contrast, data warehouses contain structured data that serves a specific purpose.

A business dashboard provides a visual and tangible representation of essential data, including key performance indicators and other important factors, through the use of unique features such as charts, tables, and gauges. This graphical representation can simplify large amounts of data, making it more easily interpretable and enabling the identification of significant trends. Using this tool can aid in the decision-making process, as it can identify the popularity of products in the market, the busiest times of the year and more, leading to informed decisions such as increasing production or hiring more staff.

Additionally, dashboards can highlight underperforming areas that require immediate attention. By drawing attention to these performance metrics, organizations can then make decisions about whether to promote the product or remove it from the product portfolio if it's performing poorly.

Davenport and Ronanki (2018) have classified three types of Artificial Intelligence for business use. Robotic Process Automation (RPA) is the first type, which uses software robots to impersonate human interaction with digital systems. These robots are efficient and do not require breaks, which reduces costs and increases productivity and efficiency. This leads to a rise in customer satisfaction as robots deliver better quality products faster than humans.

The second type is Cognitive Insight, which entails the use of AI algorithms to detect patterns in large sets of data to deliver accurate models and forecasts that can assist across multiple sectors, such as reducing costs and making the hiring process more efficient.

Lastly, Cognitive Engagement relies on deploying Artificial Intelligence in the form of chatbots, intelligent agents, and machine learning to interact naturally with customers and employees. This speeds up response times for customers' queries, leading to better satisfaction overall.

Overall, Artificial Intelligence has revolutionized business processes and brought substantial benefits in terms of cost reduction, productivity increase, and customer satisfaction.