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Digital business Digital business refers to digital technologies to transform tranditional business processes, operations and customer interactions. Key characteristics: • Leverage digital platforms • data-driven decision making • personalised customer experiences, products or services (Netflix, Amazon, Google) • agile and innovative business moodels and revenue streams Increasingly vital due to rapid advancements of technology and changing consumer behaviors. Digital business models often disrupt traditional industries and challenge existing players (e.g. Webjet, Uber, AirBnB) What are Digital Technologies? • Electrical equipment and devices • Applications • Infrastructure • Networks Digital Techologies store, generate, process and transport data across interconnected networks. Digitisation Is the process of converting information from a physical form into a digital one. For example: Scanning a paper document to turn it into a digital file or converting old photos into digital images. It helps store, share and use information more easily on computoers and other devices. Digitalisation Means using technology to improve how business works. Its not just about turning paper files into digital one, its about using new techonologies to make things faster, efficient, and better for customers. Companies change the way they operate, find new ways to work, and use digital tools to stay competitive in todays world. Digital Tranformation Is when businesses use technology to completely change how they work and serve customers. It is more than just using digital tools, its about improving proceeses, creating new ways to operate, and staying competitive in a fast-changing digital world. For example, a store moving from physical shops to an online business or bank using mobile apps instead of only in-person services. Digital Transformation Is about leveraging digitalisation to improve business operations and gain competitive advantage. Example: An ERP System is an ecosystem of interconnected business applications and Data. ERP helps organisations manage and automate core businesses processors across departments, such as supply chain, HR, finance and manufacturing. All aspects of business are internally intergrated. Answering Questions • Define the concept involved • Describe how the concept occurs in the case • Explain the evidance thhat supports the answer • Answer: Have you answered the question proberly? • Everything must be in your own words, anything else is plagiarism Big Data Definition "Big Data is high volume, high velocity and high variety information assets that demand cost-effective, innovaive forms of information processing that enable enhanced insight, decision making, and process automation" (Gartner, 2017) "Data is BIG, when the size of data becomes part of the problem" (Magoulas, 2019) Volume- Amount of Data, Scale Variety- Range of data types and sources Velocity- Speed of the generation of new data and... Analysis- big data requires cost-effective, innovative forms of information processing to enable insight and decision making. Charateristics of Big Data: Variety Structure data- can include: numbers, labels, formulae. Typically it fits neatly into a spreadsheet. Unstructured data- Can include: SMS, MMS, audio, video, radio waves, GPS, anything that doesnt fit neatly into a spreadsheet. Typically, big data is Semi-structured data as it is a combination of structures and unstructured. Velocity Relates to speed, data is being generated quickly and needs to be processed in real-time to useful. Real-time analytics is required, new tools, skills and methods. Late decisions means missing opportunities. E.g. Streaming data, data such as digitised maps, weather, patterns, GPS locations, customer history, reviews, and other data is being analysed using algorithms to give uber drivers real-timeinformation such as length of drive, proximity to passengers, and to calculate the fare. Volume- Data Volume is increasing exponentially Velocity- Data needs to be analysed quickly Variety- Different types of structured and unstructured data. Where does data come from? Big Data in Healthcare Benefits • Preventitive vs reactive • Reduced healthcare infrastructure costs • Reduced death, longer life expectancy • fewer cases of preentable diseases • Less strain on emergency services Challenges • Privacy issues • Digital divide: technology accessibility • Data security issues • Less responsibility for health • Increased popultion/ life expectancy • Ethical challenges • Descriptive Analytics • Explains what has happened (historic) • using data agregation and data mining to provide insight into the past. • Describe and summarise raw data to make it interpretable by humans • Decriptive analytics are useful because they allow us to learn from past behaviors and understand how they might influence future outcomes. Diagnostic Analytics • Explains why something happened (historic) • E.g. for social media marketing campaign, you can use diagnostic analytics to access sudden changes to site visitor activity. • takes the insights found from what descriptive analytics and drills down to find the causes of those outcomes. Predictive Analytics • Explains what might happen in the future • use statistical models and forcast technique to understand the future based on probabilities. • combined transactional and historical data such as ERP, CRM,HR and POS systems to identify patterns in the data and apply statistical models and algorithms to capture relationsips between various data sets. • Use predic likely outcomes Prescriptive Analytics • Recommends an action based on the forecast (future) • use optinisation and simulation algorithms to advice on possible outcomes and recommend best outcomes. • attempt to quantify the effect of the future decisions in order to advise on possible outcomes before the decisions are made. • links with autonomous decision making, artificial intelligence, automation and robotics. By allowing machines to make decisions and take actions. Defining Cloud Computing • The practice of using a network of remote servers hosted on the internet to store, manage, and process data, rather than a local server or a personal computer. • computing processing power • data storage • networking • development platforms • deployment platforms • business processors • Cloud computing is the enabling technology of all digital business. Key Features • On-demand access to recourses • scalability- easily scale up or down based on needs • Cost efficiency- pay only for what you use • Accessibility- access from anywhere with internet connection Why it matters: • Cloud computing enables businesses and individuals to be more flexible, efficient and collaborative while reducing infrastructure costs. • The basic idea is that businesses rent IT services instead of owning the hardware or software. • Key"as a service" models: • Iaas- Infrustructure as a service Rent servers, storage, networking, security (AWS, Microsoft Azure) • PaaS- Platform as a service Rent tools to build and run apps (Google App Engine) • SaaS- Software as a service Use ready-made apps deployed over the internet(Gmail, Microsoft 365) • AlaaS- Artificial Intelligence as a service Access AI tools and models (like image recognition, chatbots, or language processing) • BaaS- Blockchain as a Service Use blockchain infrastructure and development tools through cloud provider. • AaaS- Analytics as a Service Perform data analytics and visualisation in cloud, without needing your own data infrastructure • IoTaaS- Internet of Things as a Service Access IoT capabilities such as sensors and connected devices through cloud provider. What is IoT? The Internet of Things (IoT) is a network of single-task physical objects embedded with software, sensors and network connectivity that enables these objects to collect and exchange data autonomously

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