Business 5.9 Flashcards
What is a database
computerised system that makes it easy to store, search and select raw information that can be synthesised. The two types are qualitative (employee residential addresses) and quantitative (costs, prices, etc).
What is Cybersecurity
the practice of protecting systems, networks, and programs from digital attacks.
What is Cybercrime
refers to any illegal activity carried out using computers or the Internet by deliberately and maliciously targeting computers, computer networks or networked devices. This can be carried out by individuals or organisations, usually done for financial reasons (profit), but in some cases can be caused by political or personal motives.
What is Critical infrastructure
refers to the essential and interrelated physical structures and facilities needed for the effective functioning of a business.
What are Artificial neural networks (ANN)
form of intelligent machine-learning algorithm that simulate the human brain
e.g. (→ Google uses it make its ‘watch next’ recommendations for YouTube videos.
→ E-commerce, chat bots, etc.)
What are data centres
networks of centres that store, process, and distribute data
What is cloud computing
data centres on the internet instead of a physical place.
What is virtual reality
is the use of computer technologies to create a simulated 3D experience.
Uses:
Uses of virtual reality
- Marketing: Innovative products that are designed to engage with their target markets.→ IKEA, customers can virtually place furnitures in their homes.
- Operations management: Model product prototypes using simulations with VR software, minimise errors in R&D stages, help adapt designs as needed.
- Human resource management: Train employees more effectively.
Limitation of VR
- Can be expensive for businesses to use. However, costs are coming down over time.
- Clunkiness of VR headsets may be awkward user experience, truly immersive experiences has not yet fully realised yet.
- Ethical, security-related and legal issues.
What is Internet of things
network of devices that can collect and process data
Using mobile phones as sensors to collect and share data from our vehicles via applications like Google Maps or Waze is an example of using IoT. It informs about the traffic conditions of the different routes, estimated arrival time, and the distance from the destination while contributing to traffic monitoring.
What is AI
computer systems that can perform functions traditionally handled by humans
it is made up off several ANN’s put into one big thing (AI)
Commerce: use of chatbots, customer support
Transportation: self-driving vehicles
What is big data
Extremely large data sets that are analysed to reveal trends and patterns in consumers’ behaviour
Example of big data use
Quality management: business can analyse data about consumer preferences and they can use this data to improve quality of product make sure qualty standards is in line with consumers needs and wants
Location decision: big data about suppliers location and delivery times can help to make better location decisions
BEA: analysis consumers consumers spending patterns, income levels, and levels of originations output can help to determine the bets price price, target price
analyzing demand fluctuations an predicting different kid of disturbances in supplies of raw materials can hep with production planning
analysing past data about emergencies can help with contingency planning
analyzing past data and making sure that past mistakes are not repeated again can help to minimize wastage of resources in R&D
What are loyalty programmes
measures taken to encourage customers to make repeat purchases
e.g. airline miles, banking app cashbacks, reward points
advantages of loyalty programmes
Increased customer retention
Low cost (compared to attracting new customers)
Obtaining more data from customers
Word of mouth promotion
disadvantages of loyalty programmes
having to deal with big data (dealing with servers, people and experts) its not free and not cheap, the more data colelcted th emore expensive it will be
competition with other loyalty programmes from competitors
any forms of discounts leads to reduced profits
What is digital taylorism
Digital Taylorism refers to the use of management information systems (MIS) to monitor the behaviour and performance of employees
Advantage of digital taylorism
Efficiency - Digital Taylorism can significantly improve efficiency in the production of goods and improved service processes.
Cost savings - It can lead to cost savings in the long term by automating repetitive tasks, reducing the need for extensive manual labour, and minimizing errors in production processes.
Precision and accuracy - Automation through digital technologies ensures precision and accuracy in tasks, minimizing human errors and inconsistencies in production or service delivery.
Standardization - Digital Taylorism promotes standardization of work processes, ensuring that tasks are performed consistently and according to predefined standards.
Data-driven (scientific) decision making - The use of digital technologies allows for the collection and analysis of large amounts of data, enabling firms to make informed decisions and identify areas for improvement.
Productivity gains - Automation of routine tasks can result in increased overall capital productivity, as digital systems and MIS can work continuously without the need for rest breaks.
Disadvantage of digital taylorism
Monotony - The division and automation of tasks can cause monotony so reduce job satisfaction, resulting in lower productivity and higher labour turnover.
Skills erosion - The focus and reliance on automating tasks can lead to less emphasis on developing diverse skills among workers, potentially eroding the overall expertise and skills set of the workforce.
Resistance to change - Workers may resist the implementation of digital Taylorism due to concerns about job security, loss of control over tasks, being micromanaged, and fear of being replaced by machines.
Limited scope for creativity - Overemphasis on automation and standardization may stifle creativity and innovation as workers may have limited opportunities to contribute ideas or suggestions.
Depersonalization - The reduction of human involvement in tasks and decisions may lead to a depersonalized work environment, affecting the quality of customer service and employee relationships.
Ethical concerns - The use of digital Taylorism raises ethical concerns related to job displacement, unequal distribution of power, and the potential to exploit people in the workplace.
Examples of digital taylorism
CCTV cameras: ca nhelp monitor what employees are doign at work, see if they are involved what they should not be involde it
Screen monitoring, company laptop, the company can see when you accessed and what you did on laptop
Clock-in and clock out can see when employees have entered work and left workplace
Recording voice calls, this is use of data to improve performance
email monitoring
what is data mining
management process of using data for predictive analysis and forecasting purposes. It is the use of management information systems to find trends, patterns, and correlations from large data sets, and using the findings to make predictions about future situations. Hence, it is sometimes referred to as knowledge discovery in data (KDD).
Data mining relies on other aspects of management information systems (MIS), such as databases, data analytics, big data, and machine learning to discover patterns and relationships (correlations) in these large data sets so as to inform business decision making. Data mining enables managers to make sense of past trends in order to make informed predictions of the future, rather than relying on management decisions and corporate strategies to be based on intuition and guesswork.
Advantages of data mining
They help manager and decision makers to predict future situations.
Effective use of data allows businesses to understand their customers better, which helps to improve customer relations.
Being able to make more informed decisions enable businesses to increase sales revenue.
Improved risk management as data mining can be used to detect fraudulent activities and unusual financial transactions. It helps firms to identify potential risks and enhance security measures to protect their assets.
Data mining techniques cut wastage and inefficiencies in operations management, thereby helping businesses to reduce costs, e.g., it enables firms to improve sales forecasting and optimize stock (inventory) levels.
Overall, data mining methods enable businesses to reduce risks and exposure to fraudulent behaviour.
Data mining disadvantages
Privacy issues are a growing concern due to the increasing amount of data about private individuals on platforms, such as social networks, e-commerce, online forums, and smartphone apps.
Security issues surrounding hackers gaining access to big data of customers, including data on personal and financial information, credit card fraud, and identity theft.
Personal data can be collected and misused, including the unethical sale of private information to third parties. The information can be used unethically to take advantage of vulnerable people or to discriminate against a group of people.
Data mining is challenging and complex. Finding the right or required data is a time consuming and difficult task given the huge volume of data present, which are also generated continuously.
It can be highly expensive, including the need to invest in advanced data mining technologies and hiring specialist technicians. Staff training about the use of mined data may also be required, which further increases costs.
