Data Analytics

• Data analytics is the management process of examining and scrutinizing raw data to find meaningful trends and patterns to support business decision-making.

Developments in information communication technology (ICT) have made it easier for businesses to collect, collate, analyze, and share data. However, in its raw form, the data do not really mean much, so businesses use aspects of management information systems (MIS), such as data analytics, to gain competitive advantages by turning raw data into meaningful information.

• Data overload means there is too much data available for managers to know what to do. This causes inefficiencies and, therefore, delays management decision-making.

• Descriptive data analytics is a type of data analytics that examines what has happened.

• Diagnostic data analytics is a type of data analytics that examines why something has happened.

• Predictive data analytics is a type of data analytics that examines what is likely to happen.

• Prescriptive data analytics is a type of data analytics that examines what should be done.

Database

Data are raw facts or statistics, whereas information is the organization and interpretation of those facts or statistics from the given data. Data can come in the form of numbers, graphs, texts, figures, and images. It is a raw form of knowledge or information, so it does not carry any real significance or purpose on its own, i.e., the data must be organized, processed, and interpreted to have any real meaning. There are two main types of data:

• Quantitative data - Refers to data in numerical form, such as prices, costs, and sales revenue.

• Qualitative data refers to data in descriptive (non-numerical) form, such as employees’ names and residential addresses or customers’ opinions.

Information is the knowledge gained through studying (analyzing) and interpreting data. Essentially, information is the interpretation or perception of data. Only when the data are collated and organized in a useful way will the data provide information that is beneficial to managers and decision-makers.

Table 1 - Differences between data and information

A database is a computerized system used by businesses to store, organize, search, select, process, and retrieve data and information.

 Advantages of using databases

• A database manages data efficiently so that the required data or information can be easily searched and retrieved. This helps the business to function smoothly.

• Without an efficient database system, businesses risk losing any competitive advantage they might have and experiencing a data breach (the loss or theft of important data). This can, therefore, hinder the firm's operations and growth strategies.

• As with all aspects of an effective management information system, databases can improve a firm's operational efficiency, productivity, and decision-making.

 Disadvantages of using databases

• Data can become overwhelming (data overload), making it more difficult and costly for businesses to organize, manage, and process it.

• Not all managers understand the value of data in the decision-making process, so they are unlikely to be able to manage data most efficiently.

• Databases are prone to cybercrime, a deliberate and malicious attack on computer hardware or software, including databases. The security of data stored in databases has become an increasingly important matter for businesses. Hence, firms need to spend more time and resources on data security (the protection of data against disclosure, damage, theft, or unauthorized access).

Data are the raw facts or statistics from which information is generated.

A data breach refers to the loss or theft of important data, usually due to an inefficient database system.

Data security is the protection of data against disclosure, damage, theft, or unauthorized access.

A database is an organized collection of data stored and retrieved electronically using a local computer or networked computer server.

Businesses use databases to store, organize, search, select, process, and retrieve data efficiently.

Databases enable managers to access, manage, and update data quickly. 

Information refers to the organization and interpretation of facts or statistics from the given data.

Qualitative data refers to data in descriptive (non-numerical) form.

Quantitative data refers to data in numerical form.

Cybersecurity and cybercrime

Cybercrime refers to any form of illegal activity carried out using electronic methods to deliberately and maliciously attack computer hardware or software, including computer networks, devices, and critical infrastructures. Most cybercrime is committed by hackers (or cyber criminals).

• Computer malware - A computer malware or virus is a malicious code or program that, once activated, infects a computer system and changes how it works or stops the device from functioning.

• Cyber-extortion - This means the cybercriminal demands money so as to prevent a threatened cyber attack.

• Data breaches—A data breach (also known as a data leak) is a security violation in which sensitive, protected, and/or confidential data is viewed, copied, transmitted, or stolen by an unauthorized person.

• Identity theft - This occurs when personal data and information are stolen and used illegally, such as in banking and credit card fraud.

• Online scams - This refers to fraudulent behavior using Internet technologies, such as email fraud.

• Phishing is the unethical and illegal act of using reputable business names, telephone numbers, emails, and websites to deceive people into revealing personal information, such as passwords and credit card numbers.

Cybersecurity refers to the policies, processes, and procedures used to safeguard an organization's computer systems and networks from unwarranted attacks, such as information disclosure, data theft, or physical damage.

Critical infrastructures

Critical infrastructures are the crucial computer systems, structures, networks, and facilities required for the effective functioning of an organization in the modern and digital corporate world. They consist of both physical infrastructures within an organization's management information systems (such as artificial neural networks and data centers) as well as non-physical infrastructures (such as cloud computing) that power modern business operations.

Artificial Neural Networks (ANN)

Artificial neural networks (ANN) are a feature of critical infrastructure and refer to advanced computing systems designed to simulate how the human brain processes and analyzes data and information. ANN relies on learning algorithms that can acquire knowledge, solve problems, and make decisions independently by processing new data as it is received.

Data Centers

Data centers refer to the physical facilities or the locations of computer systems with networks and structures that support organizations in accommodating their telecommunications and data storage and processing systems. They are designed for the secure storage, management, and dissemination of large amounts of data.

Cloud Computing

Businesses can use cloud computing rather than a local server in a physical location (such as data centers) or a personal computer. 

Cloud computing (sometimes referred to as cloud services) is a virtual, computer-generated online space that enables users to store, organize, manage, process, and retrieve data safely and efficiently.

Cloud services do not require any external storage equipment. This disruptive technology represents great advantages for companies because it allows users to enjoy management information tools from any location in the world by just connecting to the Internet from their mobile devices.

There are three categories (or types) of cloud computing:

• A private cloud is a cloud service that a business sets up, or at least controls, for its personal use in managing data, such as storage and database services.

• A public cloud is a service managed by an external provider, such as Amazon, Apple, Dropbox, or Microsoft.

• A hybrid cloud is a cloud service that optimizes the benefits of using a public cloud with the added security and controls of using a private cloud.

Table 1 - Differences between data centers and cloud computing

Virtual reality

Virtual reality (VR) is an artificial, computer-generated environment or world accessible to businesses and consumers in a seemingly real-world way. It includes interactive simulations using highly sophisticated computer equipment. For example, surgeons can practice different operations in virtual reality, and pilots can practice their craft in simulated adverse weather conditions using VR technologies. VR is also a rapidly emerging technology that is transforming the way students learn in schools, including physical education. For example, students can experience various sports in a safe and controlled virtual environment without the costs, risks, and physical requirements associated with the sport in the real world. This is particularly beneficial for students who are unable to participate in physical sports due to injury or other physical limitations.

Virtual reality provides workers with the practice they need, albeit in a computer-generated world, so they become familiar with different scenarios they are likely to face in the real world. Replicating these situations in VR helps employees to know what to do in reality should these circumstances arise. Employees are also able to react in a safer way than if they were experiencing the situation for the first time in real life.

 Advantages of virtual reality in the workplace

• VR helps to reduce wastage and accidents in the workplace. It creates a safer working environment for employees to train and develop their skills to perform better at their jobs. For example, VR can be used to recreate any scenario, such as falling objects in the workplace or other unsafe situations. This helps the employees be more prepared in the event such scenarios arise in reality, rather than experiencing them for the first time without knowing how to react.

• VR is highly flexible and can be used for a very broad range of training purposes. For example, hotels can use VR for a range of routine and complex hotel operations, such as procedures and processes to check in guests, cleaning a guest room,  providing room service, and handling a wide range of guest inquiries.

• Training in VR enables employees to be 100% focused on the task at hand. In the real world, training is often disrupted by other interactions and distractions in the workplace. This makes training more efficient and cost-effective.

• Hence, virtual training can help to keep costs down without yet still giving workers the near-reality experience they need to develop their talents.

 Disadvantages of virtual reality in the workplace

• The accelerating pace of VR can make it challenging for a business to keep up with technological advances. Equipment can also become obsolete quite quickly.

• Investing in the latest VR hardware and software can be expensive. However, there is no guarantee that the investment will be successful or that customers will desire and be willing to adopt VR technologies.

• Research has shown that some employees suffer from motion sickness when wearing VR headsets. This limits VR’s effectiveness and potentially wide-reaching applications for the organization.

The Internet of Things

The Internet of Things (IoT) refers to any Internet-enabled device that enables people to store, share, and transfer data with other electronic devices that use embedded sensors. It consists of a giant network of connected devices ("things" or objects) that collect and share the most relevant data with users based on real-time information to help address the specific needs of the consumer. The data are used to detect patterns, make recommendations, and identify possible problems before they occur. 

Businesses use IoT applications to improve their operational efficiency and productivity. For example, they use the IoT to record, monitor, and track customers’ spending habits, as well as enhance supply chains and improve stock management (inventory control). For example, a smart building - be it an office or a shopping mall - uses sensors and automated processes to control the building’s temperature (air conditioning or heating), ventilation, security systems, and lighting. The building's car park may use smart parking sensors to determine occupancy of the parking lot, which is communicated to motorists (such as indicating how many spaces are currently available, using red lights to show occupied spaces, and green lights to show where an empty lot is available for parking).

The Internet of Things was coined in 1999 by Kevin Ashton, an Executive Director at the Massachusetts Institute of Technology (MIT). Ashton stated that:

“Today computers, and, therefore, the Internet, are almost wholly dependent on human beings for information. Nearly all of the data available on the Internet was first captured and created by human beings by typing, pressing a record button, taking a digital picture, or scanning a barcode. If we had computers that knew everything there was to know about things, using data they gathered without any help from us, we would be able to track and count everything and greatly reduce waste, loss, and cost. We would know when things needed replacing, repairing, or recalling and whether they were fresh or past their best.”

In 2002-2003, Walmart and the US Department of Defense (DoD) were the first large organizations to embrace Ashton’s model of tracking inventory using the Internet of Things.

The IoT covers a very broad range of devices, including:

• Government agencies integrate IoT sensors for air quality monitoring by identifying pollutants.

• Smart microwaves that automatically cook food at the right temperature and for the right length of time.

• Smart traffic light systems streamline traffic efficiency and public transportation based on variations in traffic conditions and flows.

• Self-driving cars use highly complex sensors to detect objects in their path.

• Wearable fitness devices measure the number of steps the user takes each day, their sleep patterns, and their heart rate. The data is then used to suggest bespoke exercise plans tailored to the user’s needs.

• Farmers use IoT technologies to improve agricultural output and pest control. Data analytics is used to track soil moisture levels, climatic changes, and plant health to increase crop yields.

• Global Positioning Satellites (GPS) aligned with smartphone apps and computer hardware and software in motor vehicles.

• The Ring smart doorbell home security system is linked to the user's smartphone and lets homeowners know when the doorbell is pressed, regardless of their location. It also lets them see who it is and speak to it.

• Smartphones can be linked to countless apps that enable users to connect to their home appliances, such as smart lights, thermostats for heating (or air conditioning), home entertainment systems, and home security systems. All of these can be operated remotely so long as there is an Internet connection.

Artificial intelligence

Artificial intelligence (AI) is defined in the Oxford English Dictionary as “The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” It is a way that computers can be programmed to learn from data to perform certain tasks, such as facial and voice recognition. AI is an area of computer science that develops the ability of smart machines to perform tasks rather than natural or human intelligence, such as motion or voice-activated commands on smart devices.

AI enables computers and IoT devices (the Internet of Things) to mimic human behavior and actions, such as becoming familiar with different situations, learning from experiences, processing information to solve problems, and using data to inform decision-making. In theory, AI enables businesses to make rational decisions based on data rather than relying on human emotions and biases that can result in irrational choices and outcomes.

• Apps that support commuters with virtual updates and alternative bus and train routes, showing people where to interchange and which platform to use.

• Drive-assist functions in motor vehicles can break automatically in emergencies and help drivers park in tight spaces (a feature of self-driving cars).

• Facial and voice recognition systems allow users to access online banking, complete online purchases, and even open security doors.

• Security systems can use aerial drones that are automatically launched when an alarm is triggered, streaming live video to a private security team.

• In the UK, some police forces have tested predictive policing tools, i.e., using AI to predict where crimes are likely to happen and the probability of people reoffending. AI technology could help reduce pressure on police officers and improve public safety.

• Online search engines, social media platforms (such as YouTube), streaming service providers (such as Netflix), and e-commerce businesses (such as Amazon) provide recommendations that users are likely to be interested in, including social media feeds.

• Satellite navigation systems use the Global Positioning System (GPS) to provide live travel assistance to motorists and travelers using a smartphone or other satellite navigation device. Examples include Google Maps, Apple Maps, Bing Maps, and Waze (which is owned by Google).

• Smart assistants (often referred to as "chatbots") that provide help with inquiries about banking, insurance, healthcare, travel, and tourism are also covered. This also covers the use of marketing chatbots.

• ChatGPT (Chat Generative Pre-Trained Transformer) is a language model developed by OpenAI that generates human-like text responses to questions and prompts.

• Smart home appliances, such as smart fridge freezers (that auto clean and auto defrost when needed) and smart vacuum cleaners (that use sensors to detect when and where rooms need to be cleaned), can be used without human input.

• The use of predictive text functions when typing a message on a smartphone, tablet, or computer.

Artificial intelligence uses other aspects of management information systems (such as critical infrastructures and data analytics) to process large volumes of data faster and more accurately than humans can. It relies on big data and automated statistical analysis, enabling machines to collate, analyze, understand, and learn from data through specifically designed coding and algorithms. Therefore, AI relies on machine learning.

Machine learning is the use of computer systems, algorithms, and statistical models to enable electronic devices to memorize and adapt on their own without following direct instructions. As a dimension of artificial intelligence, it enables computers to learn and determine results based on patterns in large data sets to imitate intelligent human behavior and decision-making. For example, advanced machine learning is being used by social media businesses to tackle issues related to fake news, hate speech, online scams, and dishonest advertising—all in real time.

The use of AI has revolutionized and will continue to transform the way in which businesses conduct their activities and develop their relationship with customers. In general, AI and machine learning have enabled businesses to know more about customers and to improve their ability to respond to their evolving needs. For example, AI enables service providers such as Amazon, Instagram, Netflix, Spotify, TikTok, and Twitter to track users’ data to determine their preferences and likes in order to adapt content accordingly.

However, as AI technology rapidly advances, there are possible negative impacts, too. For example, analysts expect AI could cause mass unemployment for customer service agents in multiple industries. AI technology and machine learning would deal with customer queries, so businesses may well cut call center staff in order to reduce costs. For now, at least, it is unlikely that AI will replace people in roles that require critical thinking and empathy.

Former Google CEO Eric Schmidt also raised concerns about IA and its potential threat to democracy because of the misinformation that could be spread on social media platforms. Furthermore, there are concerns associated with AI and the risks of exacerbating bias, widespread misinformation, and the potential of infringing privacy rights.

Big Data

Big data refers to access to extensive amounts of unprocessed (raw) and processed (structured) data from a broad range of sources. Due to the huge volume of data available, the data are often complex, so sophisticated computer systems are used to capture, process, and analyze them. Such tasks would be beyond the ability of humans without the use of technology to manage the process.

In general, business decision-making can be improved when large amounts of meaningful data are available. Market analyses show that big data as a service market was valued at $12.74 billion in 2020 but is forecast to increase to $93.52 billion by 2028 (which represents a compound annual growth rate of 28.2%). The reason for this projected growth is that big data can help businesses in numerous interrelated ways, including:

• Making more informed business decisions based on facts, trends, and logic.

• Understanding their customers in better ways, thereby supplying goods and services that meet their changing needs.

• Improving business activities and operational efficiency.

• Generating additional revenues and profits.

There are five key characteristics of big data, referred to as the 5Vs, developed by Doug Laney (2001), a management and technology consultant. He defines big data as:

"Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation."

• Volume - the large amount of data generated. Volume (and variety) can come from numerous sources, such as smartphones, tablet computers, streaming services, e-commerce databases, and social media platforms.

• Variety - the diversity of different types of data, enabling multiple perspectives and comprehensive insights into an issue.

• Velocity - the speed at which data are generated and stored, often live.

Two additional Vs were later added to Daney’s original model:

• Veracity - the extent to which the data are accurate, including the ability to separate inaccurate data.

• Value - the extent to which the data are useful for supporting problem-solving and improving decision-making.

Customer loyalty programs

• Customer loyalty measures the extent to which customers consistently repurchase products from the same business.

• A customer loyalty program is a marketing approach that rewards customers who purchase a particular brand or use the services of a particular organization on a recurring basis.

• Customer retention measures customer loyalty by determining the extent to which existing customers will stick to the same brand when making future purchases rather than switching to a rival brand.

Customer loyalty measures the extent to which customers consistently repurchase products from the same business. Loyal customers also choose the goods and services of the business over its competitors. A common way that businesses compete is through the use of customer loyalty programs. These are marketing strategies designed to retain customers by using a rewards program that gives loyal customers direct benefits, such as reward points that can be redeemed for purchases at discounted prices. An example is the frequent flyer customer loyalty programs used in the commercial airline industry. Other rewards or benefits may include free merchandise, coupons, or priority access to new product launches.

Strong customer loyalty exists when customers are committed to a certain business and make repeat purchases time and time again. Marketing strategies designed to cultivate loyal customers through the use of customer loyalty programs can give organizations a competitive advantage. As reported in the Harvard Business Review, "Succeeding with current customers goes a long way towards earning their next purchase.” While such programs cost money, they can reap multiple benefits in the long run. By contrast, if a customer walks away and switches to a rival brand, their business could be lost forever.

However, customer loyalty programs are more than just about offering discounts or rewards to customers. Management information systems (MIS) are being increasingly used by businesses to gather and process more data about their customers. This helps these businesses create more appealing customer loyalty programs in order to attract prospective customers as well as retain existing ones. Businesses use purchase history and customer-provided data (such as their shopping habits) in order to improve their marketing and to create timely and relevant offers to their customers. The rewards program can also help a business to increase its customer base.

 Advantages of customer loyalty programs

• Customer retention—Customer loyalty programs reward customers for repeat purchases, so they help improve customer retention (the opposite of which is referred to as brand switching). Customer loyalty programs are also used to improve customer value, engagement, and experience. Essentially, customer retention is relatively cheap, while customer acquisition is expensive.

• More spending - Loyal customers are likely to spend more money, thereby generating higher sales revenue for the business. This is partly due to the rewards program that entices customers to spend more. Research by Accenture, the multinational information technology services and consulting company, has found that members of customer loyalty programs typically spend up to 18% more than other customers. Furthermore, it is generally easier to sell new products to existing customers.

• More customer referrals—Word-of-mouth marketing can be extremely powerful. If customers enjoy the benefits of a customer loyalty program, they are more likely to convince their family and friends to join. For example, Dropbox offers additional free cloud storage space to users who refer new customers to the product. Furthermore, happy customers are more likely to write positive reviews on social media platforms, which further helps to promote and reinforce the brand or product. Hence, such programs can help to broaden the customer base.

• Cost efficiency - It can be significantly cheaper to retain happy customers (using customer loyalty programs to reward devoted customers) than to search for new customers.

• Revenue stream - Some customer loyalty programs charge upfront membership fees, which provides the business with an important source of income. For example, Amazon Prime charges a monthly fee, which allows subscribers to make frequent and repeat purchases without having to worry about additional shipping (delivery) costs. Members also get priority dispatch of their purchased products.

• Customer-generated content—Customer loyalty programs also involve collecting an immense amount of data, such as the customer’s frequency and purchasing habits. This helps businesses promote highly relevant goods and services that meet their customers’ needs.

• Improved customer engagement—It is far easier for a business to connect with loyal customers and engage them with direct marketing. Customers are more receptive to receiving emails, social media adverts, and other marketing content from the brands that they are loyal to. Customer loyalty programs enhance customer engagement (such as VIP product launch events) and help strengthen the bond between the firm's brands and its customers.

• Higher profitability—Ultimately, developing reward schemes that generate customer loyalty is generally better for the business. This is because the combined benefits outlined above help the business grow and keep its profits high.

 Disadvantages of customer loyalty programs

• Higher costs—Businesses may need to be wary of the costs of giving away free products and other extras or rewards to customers. Essentially, customer loyalty programs cost money to plan, implement, and review. Furthermore, the business needs to spend time and resources collecting enough data to get a reasonably reliable idea about customers in order to develop loyalty programs or reward schemes. Ultimately, the higher costs are likely to lead to higher prices being charged for the firm's goods and services.

• Time—Customer loyalty can take a significant amount of time to develop and nurture. It takes time to plan and implement effective customer loyalty programs carefully and to develop and refine them.

• Competition—While offering customer loyalty programs can help a business attract more customers, competitors will likely also offer schemes that reward devoted customers. In general, customer loyalty diminishes as competition increases. To keep the brand or product relevant and desirable in the minds of buyers, the business must provide authentic value to its customers to keep them coming back.

• Excess expenditure - Customer loyalty programs can encourage customers to overspend and even raise the level of consumer debt (if they use credit card payments or loans to make their purchases. This raises ethical issues about businesses actively promoting the use of customer reward programs.

The use of management information systems (MIS), such as data analytics and big data, enables a business to better understand its customers in terms of their spending habits, sales patterns and trends, and customer preferences. The data and information gathered from MIS are important for developing customer loyalty and broadening the business’s customer base.

Digital Taylorism

Digital Taylorism refers to the use of management information systems (MIS) to monitor the behavior and performance of employees. It describes the management approach that relies on digital technologies to improve productivity by managing and monitoring employees and the tasks they perform in the most systematic and organized ways. This involves the use of digital technologies to streamline and automate tasks so as to improve efficiency and productivity.

The approach comes from Frederick W. Taylor’s (The Principles of Scientific Management) works from the 1920s. Taylor used scientific methods (such as observations and time and motion studies) to determine the most efficient ways to increase output and improve productivity. In the same way, management information systems provide insights into what employees are doing and how well they are doing it.

In terms of digital Taylorism, the principles of scientific management used to improve efficiency and productivity are based on three rules:

1. Split complex tasks into simple ones.

2. Measure everything that workers and managers do.

3. Link the pay of workers and managers to their performance.

The difference between scientific management and digital Taylorism is that the former requires managers to observe the work of employees and managers, whereas the latter relies on computerized systems to do so. For example, a business can use digital technologies to track:

• How long an employee spends on a particular website

• What employees search for on their computers

• The contents of emails sent by employees

• How long a worker takes to complete certain tasks, such as production or delivery times

• The duration taken by employees on their rest breaks

• Absence and punctuality rates.

The vast amount of data generated from management information systems can be used to set performance targets for employees and to measure the extent to which an employee achieves these goals. MIS can be used to support and empower employees in terms of efficiency and productivity gains. For example, the data gathered can be used to provide the training needs of each individual in the workplace. Used appropriately, digital Taylorism can lead to increased operational efficiency and productivity as well as lower production costs for businesses.

However, digital Taylorism can also help managers have greater command and control of workers. This is because the data and information generated can be used to monitor each worker’s level of output, productivity rate, and efficiency in getting things done. In the worst-case scenarios, workers can have their remuneration deducted or even dismissed if performance targets are not met.

Advantages and disadvantages of digital Taylorism

Data mining

Data mining is the management process of using data for predictive analysis and forecasting purposes. It involves using management information systems to find trends, patterns, and correlations in 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).

The term comes from the mining industry, in which mining for gold (or other precious minerals and resources) involves digging through earth and rock for valuable parts. In the same way, data mining involves sorting through large data sets to find usable and valuable information to improve business decision-making. This is done by extracting and processing raw data from large data sets; data mining allows managers and decision-makers to discover patterns, relationships (correlations), and trends.

Data mining is used for many business functions, including:

• Advertising campaigns

• Artificial intelligence

• Budgeting

• Customer loyalty programs

• Crisis Management and risk management

• Cybersecurity

• Fraud detection

• Marketing planning

• Medical diagnosis

• Research and development

• Quality management and quality assurance

• Sales forecasting

• The Internet of Things (IoT)

• Virtual reality

Data mining relies on other aspects of management information systems.

• Advertising campaigns

• Artificial intelligence

• Budgeting

• Customer loyalty programs

• Crisis Management and risk management

• Cybersecurity

• Fraud detection

• Marketing planning

• Medical diagnosis

• Research and development

• Quality management and quality assurance

• Sales forecasting

• The Internet of Things (IoT)

Virtual reality allows us to make informed predictions of the future rather than relying on management decisions and corporate strategies based on intuition and guesswork.

 Advantages of data mining

The main benefits of data mining techniques can be outlined as follows:

• They help managers 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 enables businesses to increase sales revenue.

• Improved risk management, as data mining can detect fraudulent activities and unusual financial transactions. It helps firms identify potential risks and enhance security measures to protect their assets.

• Data mining techniques cut wastage and inefficiencies in operations management, thereby helping businesses reduce costs. For example, they enable firms to improve sales forecasting and optimize stock (inventory) levels.

• Overall, data mining methods enable businesses to reduce risks and exposure to fraudulent behavior.

 Disadvantages of data mining

The main drawbacks of data mining techniques can be outlined as follows:

• 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.

• There are security issues surrounding hackers gaining access to customers’ big data, including 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 is 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.

Benefits, risks, & ethical implications of MIS

The term Management Information Systems (MIS) is a collective term used to describe the advanced computer technologies and technological innovations that influence business decision-making and stakeholders of a business.

Technological innovation refers to the partial or full replacement of an existing technology by one that improves a firm's productivity, product quality, and market competitiveness. For example, machine Learning (a discipline within the field of artificial intelligence) allows computers to learn for themselves, such as from data analysis, and carry out tasks autonomously.

Management information systems (MIS) and technological innovations have benefits, risks, and ethical implications on business decision-making and stakeholders. The points are outlined below.

The benefits of MIS on business decision-making and stakeholders (AO3)

• Improved decision-making - Using advanced computer technologies such as artificial intelligence enables a business to automate and improve decision-making. For example, data analytics can provide managers and employees with an in-depth understanding of an organization’s performance, allowing them to make more informed decisions. Furthermore, predictive data analytics can help to provide insights into potential future outcomes, thereby allowing for more confident decision-making.

• Better operational efficiency - MIS and technological innovations can be used to streamline processes and reduce costs due to improved operational efficiency. This has the potential to improve the profits of the business as well as provide better services to its various stakeholder groups.

• Improved customer services - MIS and technological innovations can be used to track customer data, such as purchasing habits and preferences, thereby providing vast amounts of data. This provides this business with valuable insights into customer behaviors and their changing needs and wants. Furthermore, MIS and innovative technologies can provide a business with real-time customer feedback, such as through the use of chatbots (or virtual assistants). In turn, this can be used to improve customer services and product offerings, as well as enhance customer loyalty programs.

• Enhanced competitive advantages - By using the latest MIS and technologies, businesses can gain more insights into their competitors and the markets in which they operate, thereby identifying potential threats as well as opportunities for innovation. For example, a business can use predictive data analytics to gain a better understanding of consumer behaviors and trends, which can then be used to develop new products that better meet the changing needs and preferences of customers.

The risks of MIS on business decision-making and stakeholders (AO3)

• Cybercrime refers to any form of illegal activity carried out using electronic methods to deliberately and maliciously attack computer hardware or software, including databases, computer devices, and critical infrastructures. With large volumes of big data and sensitive information about customers and employees, there is always the potential risk of unauthorized access, malicious attacks, or data theft.

• Set-up and maintenance costs—It can be extremely costly to install and upgrade management information systems, such as artificial intelligence, customer loyalty programs, cybersecurity, and virtual reality (VR). Additionally, staff must be trained to use the MIS and technologies correctly and efficiently, although the costs of training can be high.

• Regulatory compliance—There are legal and regulatory risks associated with the use of MIS and technological innovations. For example, the General Data Protection Regulation (GDPR) requires all businesses operating in the European Union (EU) to protect their customers’ data. Failure to comply with the regulations can lead to significant financial penalties.

Ethical considerations of MIS on business decision-making & stakeholders (AO3)

Ethics is the academic discipline or study of moral philosophy. In Business Management, it means doing the right thing. A business must consider how it will gain the trust of its customers when it asks them for their personal data. MIS and technological innovations can have both positive and negative ethical implications for businesses and their stakeholders.

The UK's Information Commission's Office (ICO) suggests the following tips regarding data protection:

The ideal time for a business to think about data protection is during the first few months of trading.

A business should make sure it only collects and holds what it needs.

Hence, a business should know what personal data it has about customers, suppliers, and employees (such as the name, date of birth, and home address of its employees) and tell these stakeholders what it plans to do with the data.

The data must be securely stored (such as in a secured database or cloud computing platform) and disposed of safely and appropriately.

Businesses must respect people's data privacy rights.

Other ethical considerations of MIS and technological innovations on business decision-making and stakeholders, such as employees, include the following points:

Improved decision-making—MIS and technological innovations can help businesses make more informed decisions and better serve their stakeholders ethically. For example, MIS allows businesses to collect customer data to understand their needs and preferences better, leading to improved customer service. Such decisions are made more objectively, without the potential for human bias in the decision-making process.

Data manipulation refers to the deliberate misuse of data to influence a particular decision or outcome. Hence, data manipulation is unethical, especially if it is intended to mislead or deceive. Examples of data manipulation include deliberately and intentionally adding or removing data, changing data values, or misrepresenting the data.

Lack of human touch—MIS and innovative technologies rely on automated processes and make decisions based on algorithms and computer programs, which can eliminate human input. This means emotions and empathy are absent in decision-making, which can be unethical even if it follows the organization’s protocol and policies.

In order to protect the interests of all stakeholders, businesses need to consider cybersecurity measures and contingency plans to minimize the potential risks of using advanced computer technologies. This is a priority that businesses must give to safeguard their customers and employees, not just on ethical grounds but also on a legal basis.