5.9 Management information systems

Full video class on YouTube, summary and notes on Instagram, class extracts on TikTok, text below. Have fun!

The main point of this class is to learn a bunch of IT stuff and see how it relates to businesses and operations management in particular.

In this part of class, we are discussing things that you probably already know: data analytics, databases, cybersecurity and cybercrime. If you feel like you can define all of these things and apply them to different businesses, just move on to the next section. If not, it might be a good idea to read this section.

Data analytics refers to analysing raw data to make judgements about that information. It’s similar to Internal Assessment (IA) or Extended Essay (EE): first, you (researcher) collect raw data, and then you analyse it, trying to make sense of it and the trends and patterns that you identify. Businesses analyse all sorts of data: customers’ preferences, income levels, queueing times, repeat purchases, etc. There are 4 types of data analytics:

• Descriptive analytics simply say what happened (for example, sales improved or queueing time decreased),
• Diagnostic analytics help to identify the reason for different phenomena (for example, change in spending patterns or ageing population),
• Predictive analytics help to make future plans (for example, extrapolation of past data or identifying trends),
• Prescriptive analytics are used to create an action plan (e.g. setting an objective, determining a strategy).

There will not be IB questions about types of data analytics. However, it can help you to understand how data analytics work for different businesses so that you are able to define this concept well.

Database is a structured set of data. Before the digital age, databases were stored physically in boxes in huge archives. Now it is all computerised. Before data analytics can happen, data about nearly anything relevant to a given business (customers’ preferences, income levels, queueing times, repeat purchases, etc.) needs to be recorded and stored. There are many types of databases that differ in the way how different pieces of data relate to each other. I have selected the 3 most common types of databases:

• Relational databases record data in tables, where relationships are clearly established (for example, each column in the table has a characteristic and in every line there is a record under the given characteristics),
• Non-relational databases record unstructured or semi-structured data (for example, a set of unrelated data such as customers’ favourite drink and the day when they come to a bar),
• Hierarchical database record information in tree-like structures (for example, organisation charts or decision trees),
• Network database is similar to hierarchical but more enhanced and flexible (for example, it can help to record a matrix structure in a project-based organisation where one subordinate may have two supervisors at the same time, unlike traditional hierarchical structures).

Again, you are not expected to be an expert on databases to get a 7 in BM. As long as you know what database is why businesses use them, it’s good enough. Types of databases are provided for you just to get smarter, not to drill your BM exam skills. Relax.

Cybersecurity is protection against the criminal use of electronic data. Cybercrime means criminal activities carried out via internet. Not only good things get digital, crime goes digital as well. Businesses have to keep that in mind and think of cybersecurity measures when they collect and store data digitally, sell online, establish communication channels. If data becomes available to wrong people, it might put many stakeholders at risk. Some of the most common forms of cybercrime are:

• Hacking
• Phishing
• DoS (denial-of-service) attacks
• Ad/click fraud

For the third time, the objective here is to define cybersecurity and cybercrime in the business context. If you can do it, move on. It would not hurt to learn some types of cybercrimes by following the links above though.

The assessment objective of this part of class is to explain 5 concepts. In the previous part, objective was to define, now it is slightly more that that. What you should learn is to define and elaborate on each of the five concepts in more detail than in the previous part of class.

Critical infrastructure is the system and network of assets that are required for operations. Without critical infrastructure, businesses can not function normally. There are many elements in critical infrastructures but we will only learn the ones that IB prescribed: ANN, data centres and cloud computing:

• Artificial neural networks (ANN) are systems that simulate the human brain. Basically, these are computers that “think” in a way similar to human’s. ANNs are able to learn on their own experience and deal with requests that go beyond the scope of basic computer programmes. Examples of ANNs are chatbots on the websites of different companies or personalised recommendations in internet shops (once you buy something, ANN suggests other products that you might be interested in).
• Data centre is a network of servers that store, process and distribute data. This is a physical space where equipment that stores data is placed. The largest data centre in the world is China Telecom data centre which is takes up space of 1 million square metres!
• Cloud computing is a network of remote servers that manage data via internet (for example iCloud or Google Docs are examples of cloud computing). Cloud computing requires physical data centres as well, but the main point here is that it implies that data that is stored in data centres can be accessed remotely from any device that has internet connection.

Virtual reality (VR) is a simulation of 3D environment. In order to be able to elaborate on what it is, I suggest you refer to how VR can be used by businesses. Feel free to do a quick research and extend the list below so that it corresponds to your personal interests:

• Training and education: doctors (especially surgeons) can practice their skills using VR without risk to harm real human beings; same thing applies to pilots or operators of all sorts of machines and equipment.
• Architecture: before the actual building is built, architects create several 3D models of all potential projects to help visualise what the outcome might look like in the given environment.
• Media and entertainment: VR is a relatively new trend in gaming and in animation.
• R&D: VR allows researchers to develop 3D models of their products before producing the actual prototype; it saves money and time because products (and their prototypes) can be tested before their physical version is made, which minimises mistakes and improves the quality of the final product.

The internet of things (IoT) refers to networks of devices than can collect and process data. Similar to VR in the previous paragraph, in order to be able to elaborate on what IoT is, I suggest you refer to how it can be used by businesses. Feel free to conduct a quick research and extend the list below so that it corresponds to your personal interests:

• Home automation: smart speakers and other home appliances help not only to make users’ lives more convenient, but also to collect data that will allow to develop better products and maximise revenues.
• Healthcare: wearable devices help to monitor health and, again, collect data that can be used in production of new versions of devices.
• Transportation: cars and traffic control can be automated and not involve any human decisions which may help to increase road safety and improve efficiency of traffic management.
• Agriculture: smart farming, with the help of automatic detectors and machines, allows to cut labour costs and decrease reliance on human labour and mistakes related to human factor.

Artificial intelligence (AI) refers to computer systems that can perform functions that are traditionally handled by humans. AI consists of ANNs (see the beginning of this part of class). And again, similar to VR and IoT in the previous paragraphs, in order to be able to elaborate on what AI is, I suggest you refer to how it can be used by businesses. Feel free to conduct a quick research and extend the list below so that it corresponds to your personal interests:

• Commerce: recommendations, chat-bots, customer support, facial recognition.
• Transportation: self-driving vehicles.
• Search engines: Baidu, Yandex, Google.
• Text processing: Chat GPT.

Big data refers to extremely large data sets that are analysed by businesses in order to reveal trends and patterns in consumers’ behaviour or business processes. Big data is something that goes beyond a typical database (see the first part of this class) and refers to huge amounts of data that are organised in multiple databases. For the last time in this part of class, similar to VR, IoT and AI, in order to be able to elaborate on what big data is, I suggest you refer to how it can be used by businesses. This time, I’ll make references to what you should have already learnt in Unit 5. Feel free to conduct a quick research and extend the list below so that it corresponds to your personal interests. So, big data help business (and production managers in particular) to:

• Adjust production methods by analysing the trends in patterns in logs and reports of production lines.
• Improve quality management by analysing data about consumers’ preferences or production line reports.
• Make location decisions by analysing geographical data about suppliers and distribution channels.
• Set the right target price by analysing consuming patterns, income levels and levels of output.
• Carry out production planning by analysing demand fluctuations versus different parameters and predicting the potential disturbances in supplies of raw materials.
• Plan for contingency by identifying patterns in past data regarding emergencies.
• Minimise wastage of resources in R&D by analysing past data and making sure mistakes from the past are not repeated again.

The assessment objective of this part of class is to evaluate quite a lot of concepts that relate to management information systems (MIS). In the previous parts, objectives were to define and explain, now it is slightly more that that. What you should learn is to define, elaborate on each of the concepts, and evaluate them (discuss pros, cons, risks, and ethical implications on decision-making and stakeholders).

Loyalty programmes are measures taken to encourage customers to make repeat purchases. For example, airline miles (frequent flyer programmes), banking apps cash-back, reward points, buy X get one free, subscriptions, etc.

On the one hand, loyalty programmes result in increased customer retention, lower costs (compared to attracting new customers), obtaining more data from customers, WOM promotion. On the other hand, managing loyalty programmes implies having to deal with big data (see previous part of class), competition with other loyalty programmes of competitors, and, eventually, reduced profits, because loyalty programmes in this or that way offer discounts in exchange for repeat purchases, thus decreasing potential profits that organisations could have earned otherwise.

Many organisations use data to manage their employees. This is often referred to as Digital Taylorism — modern application of Taylor’s Scientific Management. The key features of Taylor’s ideas are performance-related pay, division of labour, objectivity and absence of emotion in decision-making. Some examples of digital applications of Taylorism are CCTV cameras, measuring employees’ screen time, clocking-in and out, recording voice calls and monitoring professional emails. All of these activities are used to measure and control employees’ performance at a workplace and objectify the expectations and rewards.

We have already learnt what Taylor’s Scientific Management is in Unit 2. Please review this class to review.

On the one hand, Digital Taylorism allows to easily measure productivity, control employees and decrease reliance on human managers. It helps to identify areas for improvement using collected data and it records evidence of employees’ performance. Besides, it sets clear expectations about rewards for employees. However, it might imply: lack of trust between employees and managers, violation of privacy and ethical issues in case organisations collect data without prior notice, confidentiality issues if data leaks and becomes available to outsiders.

Data mining is the process of analysing big data in order to generate new information that assists decision-making. It is not the same as data analytics (see the first part of this class): analytics refer to making sense of data, while mining refers to extracting valuable information from data (hidden patterns and trends). An example of data mining could be video streaming platforms (Netflix, HBO, Kinopoisk, etc.) that identify viewers’ preferences in different seasons in order to create popular content. So, these platforms have data about what customers watch and data about when they watch it. They can correlate those two parameters and mine new information about preferences in different times of the year. Another example could be companies that identify characteristics of their successful employees, which assists HR managers in recruitment and selection.

On the one hand, data mining helps with consumer profiling because it provides insights and allows organisations to understand consumers better. It helps to create products that correspond to market needs and wants. Additionally, data mining assists strategic planning, HRP, cash flow forecasting and budgeting, marketing planning and contingency planning. Basically, data mining, big data and data analytics greatly support all aspects of business planning. On the other hand, similar to Digital Taylorism, data mining may result in violation of privacy, ethical issues and confidentiality issues, if data leaks. Besides, it is time-consuming and expensive as it requires experienced data scientists and equipment for storing and processing data.

Management information systems (MIS) refer to structures that serve the purpose of collecting, storing, processing and analysing data for the sake of efficient management. This term includes everything we learnt in this class: data analytics, databases, cybersecurity, critical infrastructure (including ANNs, data centres and cloud computing), VR, IoT, AI, big data, loyalty programmes, Digital Taylorism and data mining.

The assessment objective for MIS is to discuss benefits, risks and ethical implications of MIS and technological innovation on decision-making and stakeholders. In order to achieve this objective, compile all the advantages, risks and ethical issues, associated with all components of MIS into one table:

Make sure you can define all of these:

1. Data analytics
2. Database
3. Cybersecurity
4. Cybercrime
5. Critical infrastructure
6. Artificial neural networks (ANN)
7. Data centres
8. Cloud computing
9. Virtual reality (VR)
10. The internet of things (IoT)
11. Artificial intelligence (AI)
12. Big data
13. Loyalty programmes
14. Digital Taylorism
15. Data mining
16. Management information systems (MIS)