5.9 - Management Information Systems

Databases, Data analytics, and Data centres

• Database → An organized collection of info that’s stored in an electronic system

  • This info can be processed and filtered for a specific purpose

• Data analystics → Refers to the science of analysing data to establish patterns, trends, and behaviours to draw conclusions

• Databases are analysed by data mining, a process of searching for and finding patterns and trends in large data sets

• Data centres → Buildings or sub-sections of buildings that have servers, support computer networks, and run the systems needed to provide digital technologies or services

Cloud computing

• Data centres make cloud computing possible

• It involves data storage and networking of computers, software, databases, and servers and allows info to be stored + accessed from anywhere in the world where there’s a network

Artificial Neural Networks (ANN)

• Deep learning mimics human intelligence by connecting computing systems and nodes, like the neurons of a human brain

• These connections are called ANNs

• They function as sophisticated systems w/ inputs, processes, and outputs

• Large quantities of data are gathered by management tech systems

• These inputs are used to train algorithms, and w/ your data in an algorith, a computer system can make an appropriate decision or action

• These computer systems can spot patterns and trends in data, which they can use to make decisions better and faster htan humans

Cybersecurity

• Cybercrime is criminal activity done by using computers, networks, and digital technologies

• Most of it takes place or ends up on the dark web

  • A world wide web network accessed through specialist encrypted web browsers that keep users largely anonymous

• Cybersecurity involves the use of tech and systems designed to block access to tech systems by criminals

• Used to protect against:

  • Hacking

  • Ransomware

  • Distributed denial of services attack

Virtual Reality and the Metaverse

• Metaverse refers to digital worlds where ppl can work, play, and gather together

• Can be accessed through VR

  • The use of computer technologies to create a simulated 3D experience

Uses of VR in Business

Marketing

• Businesses are developing creative, innovative products and processes in and for the metaverse, designed to engage their target markets, extend the product life cycle of existing products, and open up new markets

Operations Management

• VR enables new product and process innovations

HR Management

• VR is used to train employees more effectively

Limitations of VR

• Depending on how its used, it can be expensive for businesses to use

• The cost to the consumer of hardware needed to access the products needs to be considered

• Awkward for consumers to use

• Also has ethical issues, security-related issues, and legal issues

Internet of things, AI, and Big Data

Internet of Things

• Describes connections of physical objects using software that lets them communicate and exchange data

• Sometimes called ‘smart’ devices or systems

Artificial Intelligence

• The ability of a computer-controlled robot to carry out tasks previously done by humans

• In manufacturing, sensors and robots work in synchronicity w/ machines to produce goods in an automated factory

• Generally, AI can:

  • Improve decision-making w/ use of data

  • Automate processes

  • Develop new markets

  • Provide virtual assistance

Big Data

• Refers to large amounts of data collected by advanced tech

• The volume and variety of data gathered is impossible for human to process

• The more data these systems process, the more they can improve decision-making for businesses

Customer Loyalty Programmes

• An extremely valuable asset for a business

• It’s easier, less time-consuming, and less costly for businesses to retain existing customers than for them to replace leaving customers or acquire new ones

Uses of Consumer Loyalty Programmes

• Rewards customers that repeatedly purchase products from a business

• Digitalization has improved the efficiency of custoemr loyalty programmes

  • Data mining and data analytics let businesses make more accurate sales forecasts from consumer buying patterns

Limitations and risks

• Not guaranteed to work, and may not always be appropriate

• Many businesses can’t afford sophisticated loyalty programmes

• Not useful for companies that don’t see much repeat business

• Also no longer a good differentiation point

• The interpretation and use of customer loyalty data has also created some ethical concerns

  • Loyalty cards support consumer profiling, so data collected may influence types of products or services available to certain customers

Digital Taylorism

• Uses digital techonologies to monitor every aspect of employee performance

• Managers and owners can analyse worker behaviour in detail, using data to analyze every aspect of performance

• Allows management to use sensors to track performance by tracking locations, timing, driving, delivery success, delivery rates, and more

• May be used to administer piece-rate pay and could act as a monetary incentive to workers, making sure their targets are met for added bonuses