E-Business Notes PDF

EVALUATION Exam: It will be multiple choice, one correct answer. -⅓ if incorrect, +1 if correct. Session 1: e-Business and e-Commerce - Origin and basic concepts Definition: E-business or electronic business is the realization of business processes on the Internet. Business vs e-Business - What’s new: Marketing via email, newsletter or social networks. - Content management system (ex: Moodle). - Inventory Tracking Software The Marketspace Model Key Factors in eBusiness Customer relationships are the priority - The store is always open - Simplicity is a virtue (make things as simple as possible) - Product customization is key: Recommender systems (based on previous purchases for instance) - Promotions aim to seduce customers (ex: through cookies) - Price customization and distribution (ex: Amazon) - Regional trends (based on where the person is located) - Network economy: the cost of adding a new customer is linear. Connectivity is what’s important. E-Commerce Definition: Purchase and sale of goods and services, or the transmission of funds or data, through an electronic network such as the Internet. Types of E-Commerce - Business-to-business (B2B): Business focus on selling to other businesses or organizations. (It’s the safest option for people and there’s no need of a high investment). - Business-to-consumer (B2C): Retail sales between businesses and individual consumers. Consumers gather information; purchase physical goods, such as books and clothing; purchase information goods, such as electronic material or digitized content, such as software; and, for information goods, receive products over an electronic network. (Direct access to consumers, but it’s more difficult because each customer is different). - Consumer-to-consumer (C2C): Consumers sell products and personal services to each other with the help of an online market maker to provide catalog, search engine, and transaction-clearing capabilities so that products can be easily displayed, discovered, and paid for (ex: eBay). - Business-to-government (B2G): Transactions with the government. The Internet is used for procurement, filing taxes, licensing procedures, business registrations, and other government-related operations. (it’s different from others because of what they pay and the requirements they have). - Consumer-to-business (C2B): Private individuals who use the Internet to sell products or services to organizations and individuals who seek sellers to bid on products or services (ex: Elance). - Mobile commerce: The purchase of goods and services through wireless technology, such as cell phones, and handheld devices. - Peer-to-peer (P2P): Internet users to transact digital resources directly without having to go through a central server (ex: MakerDao). (it does not rely on an entity, it’s between customers/people only). The dimensions of electronic commerce Characteristics of e-Commerce - Indestructibility or non-subtraction. - Transmutability. - Reproducibility. - Transfer mode: Delivery versus interactive products. Furthermore, pull allows for customization, in contrast, push does not. - Opportunity: Time-dependence versus time-independence. The store never closes. - Intensity of use: Single-use products versus multiple uses (ex: WhatsApp’s one watch contents) - Operational use: Executable program versus fixed document. (ex: apps) - Externalities: Positive versus negative. Price & Value Session 2: The digitalization, uses and areas of electronic commerce Digitalization of the phones: - Smartphones capabilities (apps made them possible) Digital innovation: - Is the creation or improvement of products, services or production methods through digital technologies. - The idea is to integrate digitally enabled computational solutions and artifacts into the process and result of the innovation. Digital Innovation is about: - Hardware and software (ej: An iPhone of WhatsApp) - Content (ej: Google Maps, e-books, or Kindle) - Infrastructure (e.g. new architecture, protocol or algorithm) - New business models (e.g. delivery apps, Vinted, all apps related with the GPS or the camera) - Observable consumer behavior A vertically integrated industry Vertically integrated industries in which closely coupled technologies reinforce the producer-consumer paradigm. Producers: 1. High fixed cost infrastructure. 2. Information flow control is concentrated. 3. There were incentives to hinder (delay) innovation. Consumers: 1. Mass market needed to provide economic return. 2. Niche interests ignored. 3. Strong controller hand. The digitalization process What happens when the information is digitized? 1. The information becomes immaterial. As a simple collection of 0s and 1s, the digital information is decoupled (independently) from the particular medium on which it is stored (hard disk, optical disk, etc.) and the particular signal carrier that encodes bits (whether magnetic polarities, voltage intensities, light pulses, or radio wave modulation). 2. Digital information can be reproduced and distributed at an insignificant and high-speed cost (therefore, it is immune to the economy of analog media). 3. Digital information can be accessed, used or reproduced without noise or degradation. Characteristics of digital information 1. Digital technologies are re-programmable. Both the content (information) and the behavior of computers (software applications) are represented as bit strings. This allows technologies to accept new instruction sets and modify their behavior. 2. Digital technologies are self-referential. (we can search for things) 3. There is a continuous integration of heterogeneous and heterogeneous (=different, diverse) technologies through the homogenization of digital data (what is called digital convergence). Digital convergence leads to market convergence - DIGITAL CONVERGENCE 1. An exponential increase in the processing and storage capacity of the devices and the bandwidth of the networks. (becoming better and better) 2. An exponential decrease in the cost of processing, storage and bandwidth has led to a convergence of devices. 3. The rise of IP networks has led to a convergence of networks. - MARKET CONVERGENCE 1. The mutual dependencies between industry structures and technical infrastructures are broken 2. Looser links between devices, networks and types of information 3. Disruptive potential blur boundaries between industries 4. Convergence of four industries: Information Technology, Telecommunications, Consumer Electronics and Entertainment The digitization of the sectors - Digitalization: The purpose of digitalization is the transformation of the sociotechnical structures that were previously mediated by artifacts or non-digital relationships into a mediated by artifacts and digitized relationships. - New artifacts: Digitization goes beyond a simple technical process of coding various types of analog information in digital format (digitalization) and involves the organization of new sociotechnical structures with digitized artifacts, as well as changes in the artifacts themselves Waves of Digitalization - First Wave: Conversion of analog content and services into digital content without fundamental changes in the structure of the industry. Cost reduction with “more of the same” services. a) 1G transition to 3G cellular network; emergence of CD in the music industry. - Second Wave: Separation of devices, networks, services and contents that have been closely coupled in the past. Digitized content and services can now be provided through a general-purpose IP network (for example, the Internet). a) Voice over IP (Skype), or digital TV. - Third Wave: Emergence of new products and services through the ”mash-up” of different media. The devices, networks, services and content that were created for specific purposes are now being remixed in order to reuse their use. a) Nike +: RFID chip in a pair of shoes b) Google Maps + Booking.com The digitalization 1. Emergence of a generic model of digital services architecture (consisting of four layers). 2. Architecture: the arrangement of functional elements, the mapping of functional elements to physical components, and the specification of interfaces between components. 3. Distributed digital innovation: The layered architecture of digital technology has implications for digital innovation, since it can now be distributed. Summary 1. In the analog world: a) Close coupling between device type and analog information. b) Clear industry limits linked to these devices. 2. Digitization of sectors (digital and market convergence). 3. Digital innovation consists in the recombination of technological capabilities and is distributed. 4. Layered architecture of digital products and services. TYPOLOGY OF BUSINESS ACTIVITY AND SPECIFIC LEGISLATION Types of companies: How can you classify companies? Possible classifications - According to ownership or ownership capital - Depending on the size of the company - Depending on the nature of productive economic activity - According to activity in which the company inscribes - Second field of action - According to socio-economic characteristics - According to the legal form: 1. Ownership is exercised by a natural person a) Individual entrepreneur 2. Collectives without legal personality a) Community of goods b) Civil society 3. Ownership is exercised by a legal entity (companies): one firm can be owned by another firm (Ex: Google is owned by Alphabet) The legal form of the company (in Spain) COMPANIES - Non-Mercantile Companies: a) Community of goods b) Civil society - Mercantile Companies: a) Collective society b) Limited partnership c) Limited liability company d) Limited company new limited company e) Anonymous company f) Labor society - Special Companies: a) Cooperatives + Not saving sensitive data from our customers This is: - Credit card - Where the customer lives - Anything else which the customer can be tracked. DIGITAL RELATIONSHIPS WITH CUSTOMERS AND CHANNELS RELATIONSHIPS WITH CUSTOMERS What is it? The customer relationship an organization chooses are based on the company’s business model and have a major impact on the overall customer experience DIGITAL RELATIONSHIPS WITH CUSTOMERS: 6 CATEGORIES 1. Personal Assistant: - This type of relationship with the client is characterized by the human touch. - Customers have the opportunity to interact with a sales representative while making their purchase decision, or with a customer service representative for after-sales services. 2. Dedicated Personal Assistance: - This type of relationship takes personal assistance to the next level by assigning dedicated customer service representatives to the customer. It is most likely to happen when selling high value/pricing products. (ex: cars, banks, buying Apple products…) - This type of relationship requires time and delicacy to develop and is characterized by the fact that the representative knows the characteristics of the client that he uses to personalize the client’s experience with the company. - Banks often assign a single point of contact to important clients with a long-standing relationship with the bank and high net worth. 3. Do it Yourself: (ex: IKEA) - The Do-It-Yourself model has become increasingly popular as organizations seek cost reduction measures that are reflected in the prices offered to customers. - In this type of relationship, the company provides all the tools that a customer needs to serve himself. 4. Automated Services: (ex: fast cash register) - Automated services are the next level of self-service by providing machinery and processes that increase convenience for customers to perform services themselves. - These types of services are usually much more personalized and use a customer’s shopping and online behavior to create a profile, which is then used to provide customer suggestions to improve their shopping experience. - Therefore, automated services can be compared in many ways to personal assistance due to the customization that goes into the experience. 5. Communities: - In today’s social media-driven environment, communities are a way for businesses to understand their consumers, gain insight into their habits and perspectives, and create a platform where customers can share knowledge and experiences. - In this way, the company not only forms a personal relationship with its customers, but these ties are reinforced by the additional relationships that customers establish with each other. a) Glaxo SmithKline is an example of this type of relationship. b) When the company launched a new weight loss drug, it provided customers with a platform to form communities that helped them understand the issues faced by overweight people. 6. Co-Creation: - In today’s social media-driven environment, communities are a way for businesses to understand their consumers, gain insight into their habits and perspectives, and create a platform where customers can share knowledge and experiences. - In this way, the company not only forms a personal relationship with its customers, but these ties are reinforced by the additional relationships that customers establish with each other. - Glaxo SmithKline is an example of this type of relationship. - When the company launched a new weight loss drug, it provided customers with a platform to form communities that helped them understand the issues faced by overweight people. There are 3 main areas: 1. Customer acquisition: The process of persuading a customer to select your organization’s product over the different available options in the market. Customer acquisition tactics: - Content marketing: a) It may be done using limited resources. b) It consists of generating blogs, podcasts, videos: anything that people want to consume that promotes the brand. c) The more people are exposed to it and share it, the higher your content will rank in search results, which is one of the most effective ways to get your product noticed by your target customer. - Search Engine Optimization (SEO) and Search Engine Advertising (SEA) a) It is the process of improving the quality and quantity of website traffic to a website or a web page from search engines. b) SEO targets unpaid traffic rather than direct traffic or paid traffic. c) Search Engine Advertising (SEA), also known as PPC or Pay-Per-Click. d) It is the process of advertising directly on Google and other similar search engines. When using SEA, search engines will show ads for your company in the search results above the organic results. - Newsletter o Email Advertisement a) The resulting traffic directed to an organization from content marketing and SEO or SEA can provide a potential customer resource. b) When customers sign up to receive information and news about your product, the company can acquire a customer without having to invest in an extensive sales force. - Social media marketing and Social purchase a) Use the key aspects of social networks to increase sales. b) Despite the popularization of social networks as a customer acquisition tool, you cannot depend exclusively on them to publicize your product in the market. c) User communities are the ones that promote purchases and raise the customer’s estimate of value. - For example, when a friend makes a purchase, it is notified to all their friends. - When the notification comes from a trusted source, it becomes a recommendation and perhaps prompts other users to make a similar purchase. - Coupons It is a marketing tool aimed at increasing sales of a product or service and customer loyalty. - Coupons can be obtained through the network to obtain discounts on the product. - This technique includes sending emails with personalized codes to a group of selected consumers. - Analytics a) It is not enough to publicize your products through the means mentioned above. b) If companies are not using the data obtained from one or more of these resources and analyzing it to better understand their customers, they are not making the most of the investment they have made. 2. Customer retention: It refers to the long-term relationship that a company establishes with its customers. The more repeat customers a company has, the more assured the champions who will market their products and help them win additional customers. Worth it? It is 6 to 7 times more expensive to acquire a new client than to conserve an existing one. The probability of selling to an existing customer is 60-70%. The probability of selling to a new customer is 5-20%. Why? Some statistics - The increase in withholding rates by 5% increases profitability by 25-95%. - An increase in customer retention of 5% increases profits up to 125%. Understanding of the customer’s life value (CLV) - Customer Life Value: CLV is the recognition that a client is not only a single transaction, but a relationship that is much more valuable than the disposable exchange. Stand-UP - Customers are more loyal to brands that they identify with or that they feel represent traits and characteristics they would like to emulate. - Therefore, it is imperative that a company select what its brand represents and communicate it to its customers. Use positive social proof Websites that provide customers with data showing how using your product will improve their social standing are more likely to help the business retain customers in the long term. Invoke the inner ego - Customers automatically lean more towards a product based on how much it reflects the qualities they feel exist in themselves. - This is called implicit selfishness and can be a very effective weapon. - Companies only need to know their customers from the inside out, have a complete understanding of the language they speak, their wants, needs and desires in order to connect with them and show them how the company and its products are an extension of themselves. Reduce pain points and friction If you address a pain point for your client or solve a problem for them, you will retain it for much longer. Realize that the budget is negligible - Most companies are reluctant to give back to customers without realizing that giving them a discount, even a small one, the discount will surprise the customer and keep them coming back for more. - Surprise the customer by giving him an advantage like a discount or a free add-on, unexpectedly, he will stay with the customer for longer. Make it personal By providing personalized service to clients, you increase your chances of creating a regular customer. Get people started - Loyalty programs are more likely to be used if businesses overcome initial customer resistance and ensure that customers automatically sign up for such schemes. - Once the ball is rolling, customers are more likely to stay the course. Make the ideal clients VIP Humans are competitive by nature, and studies have supported this observation by showing that people appreciate being assigned a particular client class if there is a class below them in the program. Label your customers Customers are more likely to keep coming back if partnering with your brand puts a label on them.. 3. Upselling: Companies are always focused on increasing their sales and often use a strategy called upselling, which requires representatives to convince the customer to buy more of their company’s products. How? - By using a combination of linguistics, product packaging and overall price reduction, and selling dependent products, companies ensure that a customer buys as many of their products as possible. - Know the customer’s profile, focusing especially on their economic, demographic, and social aspirations preferences. - This helps the seller to customize his tone to the customer’s taste. Be aware! - Companies often offer incentive programs that reward employees who manage to increase their sales through the upsell technique, and ask others to emulate the techniques and tactics these employees use. - These incentive programs are kept strictly secret because if a client knows about them, it can break the relationship of trust with the company. Example - Asking a customer if they would like to add a drink or chips to their order at a fast food restaurant. - Convincing a customer who is repairing her laptop that she should have more RAM or a larger hard drive installed. - Suggest to a customer to fix their phone that they should upgrade to a newer version of the phone. CHANNELS Channel is a medium through which an organization communicates with the chosen customer segments about the products and services it provides. - Most companies have a different medium to attract a customer and separate strategies on how to retain them. - It is advisable to list separate channels and customer relationships for each different customer segment. Purposes of a typical channel: - Educate the customer segment. - Provide customers with an opportunity to study and evaluate the organizations value proposition. - Provide customers with the facility to buy their chosen products or services. - Provide the customer with after sales services. How to select a distribution channel? The number of customer segments or the size of the market you are targeting. How to select a distribution channel? Investment - Investment required by the distribution channel – these will include an analysis of the different associated costs such as absolute value cost, cost per customer, fixed and variable costs and the profitability each channel option brings to the table. How to select a distribution channel? Product type - If the product is standard, in which case the same version will appeal to all customer profiles and can be sold through an external channel. - If the non-standardized product that must be adapted to the customer’s needs and for which the company must have direct contact with the customer. How to select a distribution channel? Amount of control required over the distribution channel: - The distribution channel can be characterized by open communication and a free flow of information. - The channel if there is the possibility of competition from the distributor, a much more closed relationship. How to select a distribution channel? Factors contributing to channel flexibility: - Take into account how long it will take to establish a healthy relationship with the distributor. - The duration of the relationship. Omnichannel implies the integration and orchestration of multiple channels The main objective is to use multiple channels to reach the customer. CHANNEL PHASES Phase 1: Awareness How do we educate customers about the characteristics of the products and services we have? - This is the marketing and advertising phase. It is how you let your customer know about your value proposition - Value proposition: characteristic that differentiates you from your competitors and makes you unique for customers. Phase 2: Evaluation How can we aid customers in evaluating our Value Proposition? - This is the promotion or ‘Try me before you buy me’ phase. The customer will evaluate, read about or use your product and form an opinion about it. A good company will educate customers with other competitors in the market and help them to evaluate their choices. In this way, you make your value proposition clearer to them and why you are a better option than your competitors. Phase 3: Purchase How can we help customers in buying their preferred product or service? - This is the sales process Phase 4: Delivery How do we deliver the promised value proposition to the customer? - This is the fulfillment stage and defines how the product will reach the customer. Summary Article Focus: The rise of the platform economy and its impact on businesses. Key Points: - The platform economy is booming, with platforms expected to handle 30% of global economic activity. - Platforms offer advantages in efficiency and network effects, leading to faster growth compared to traditional businesses. - The report might discuss challenges like regulation, data privacy, and the dominance of large platform companies. - Businesses need a strategy to adapt to the platform economy, whether by collaborating or competing. MANAGING PLATFORMS Executive report: 81% of executives say platform-based business models will be core to their growth strategy within three years (Accenture, 2016) What is a platform? - A flat raised area or structure - A long, flat raised structure at a railway station, where people get on and off trains. The concept of platform has existed for many years in the offline world: - Instead of planning new pedestrian plazas by the usual bureaucratic means, New York City’s department of transportation just marks an area on a street with temporary materials and then lets local organisations, architects and citizens decide what to do with it. The programme has a small urban oasis with big potted plants and shaded seating. - In the physical world, platforms can be simply something to stand or build on, like a New York City street. They can also be basic inputs for many other activities and products. Railways allowed services such as mail order to develop; the power grid brought forth a plethora of electrical household appliances; and standardised containers boosted global trade. Even Barbie dolls can be seen as platforms for all kinds of profitable add-ons (e.g. shoes, handbags) Platform and ecosystem A platform consists of two major elements (platforms and complementary modules/apps): - The firm(s) leading the platform are called platform owner - The modules or apps refer to complementary products or services that connect to the platform to extend its functionality. MAIN COMPONENTS OF A PLATFORM Core Elements: - Digital platforms create value by providing services that facilitate interactions between different parties - If you remove one sides, the other has no reason to remain in the platform - Notice that two-sided business models don’t need money from both sides, just value being exchanged (e.g. Google doesn’t charge you for search or email, but you are paying in kind with data and attention) - If you are not the customer, you are the product NETWORK EFFECTS Characteristics: - With network effect the value of the system increases almost exponentially rather than linearly. - Once such network effects are triggered, the platform can enter a self-reinforcing cycle. - While network effects create high barriers to entry into platform markets, they also create a hard-to-assail position once they are in place. Same side effects (side 1): the more users we have in one side, the more value is created. Developers side effects (side 2): the more developers, the more value is going to be extracted from the users. Types of networks effects: Same-side Cross-side Negative Adding someone decreases Adding someone decreases the attraction to all existing the appeal for all existing users users on the same side on the other side Positive Adding someone increases the Adding someone increases the appeal for all existing users on appeal for all existing users on the same side the other side Power Law: Characteristics: - The value of the platform depends on the number of others that use it (Metcalfe’s law) - A network effect refers to the effect that a user of a product or service has on the value of that product for other people. - Value increases with n2. - The minimum number of adopters after which the effects of the network are manifested is known as critical mass or inflection point - Once the platform reaches the critical mass or the inflection point, the effects of the network become a potentially self-reinforcing positive feedback loop Network effects: Example Characteristics: - WhatsApp has a very strong network effect, so the likelihood that someone who’s used the service for a year and whose friends are all using the service will balk after a year at paying $1 to continue subscribing is low. - At a $1 per year, WhatsApp is reasonably priced for any user that’s able to afford even a cheap Android device. A basic SMS plan will easily cost as much as WhatsApp charges for a year and will also be volume capped. It’s a good value. Analysis: - Direct network effects occur within a single group of people, for example: end-users - Apple harnesses the power of cross-side network effects via their App store. - The App Store brings together two distinct groups: a) iPhone/iPad owners. As the number of iPhone/iPad owners increase, the value to developers also increases, leading to an increase of developers entering the App Store market b) Developers. More developers leads to an increase in available apps which creates more value for its users Platform ecosystem A digital platform involves several elements: - Core platform that can be owned/controlled by one or multiple firms - The modules (e.g. complementary apps) - Interfaces (that enable the interaction between modules and the core platform) - All constituting an ecosystem. Examples: - iPhone vs Android - WhatsApp vs Telegram Platform ecosystem: Complex Systems Why? - Platforms are a common feature of complex systems, whether economic or biological. - The core building blocks are kept stable so that the other parts can evolve more rapidly by combining and recombining them and adding new ones. - That is what is happening in the startup world: new firms combine and recombine open-source software, cloud computing and social networks to come up with new services. In fact, many of these new services are application programming interfaces (APIs)—mini-platforms that form the basis of another digital product, allowing for endless permutations. MULTIHOMING COST It refers to when a platform participant on either side participates in more than one platform ecosystem. (companies try to create barriers between apps) (multihoming cost: cost from moving to one place to another) - An app developer who simultaneously develops her app for Android and iOS is multihoming on those platforms. An end-user who owns both a Android and an iPhone is multihoming on these platforms. - The software industry has relied on exclusivity contracts and incompatibility to coerce developers to single-home. However, intense market competition, in platform markets where a clear winner is yet to emerge, increases the likelihood that developers will multihome. They can place their bets on multiple competing platforms and avoid the downsides of being on a losing platform. - For end-users, the ongoing costs of establishing and maintaining multiple platform affiliations is usually a deterrent to multihoming. The higher these costs, the lower the likelihood that an app developer or end-user will multihome A WINNER-TAKES-ALL MARKET A market in which the best performers are able to capture a very large share of the rewards, and the remaining competitors are left with very little. (Ex: Google Search, Whatsapp) A Winner-takes-all market: Is a platform a WTA market? How to determine if a platform is a Winner-takes-all market: - Cross-side network effects are strong. When network effects are strong, users will want access to all potential transaction partners. A sub-scale platform will be of little interest to them unless it provides the only way to reach certain partners. - Multi-homing costs are high. When user multi-home (i.e. when they affiliate with multiple platforms), they increase their outlays. When multi-homing are high, users need a good reason to affiliate with multiple platforms. - Demand for intrinsically inimitable, differentiated features is limited. If there is limited demand for special features, then users will converge on one platform. By contrast, if segments have unique needs that are intrinsically difficult or expensive to serve through a single. (Ex: With cereal, people prefer them to be either soft or crunchy, not something in between, and so, that mid-option is deleted → it’s easier having a middle option on the digital field rather than the physical) THE PENGUIN PROBLEM A first managerial tension in dealing with platforms: - The role of user expectations is crucial in getting penguins to move off of ice floes and in the successful adoption of new network technologies. - Economists Joseph Farrell and Garth Saloner labeled this scenario ’excess inertia’ and more colloquially, the ’penguin problem’. Hungry penguins gather at the edge of an ice floe, reluctant to dive into the water. There is food in the water, but a killer whale might be lurking, so no penguin wants to dive first. In such circumstances, individual rationality may lead a group to forfeit attractive opportunities, for example, a predator-free meal or an innovative new networked product. - Playing the waiting game Questions: - What? No one moves unless everyone moves, so no one moves - When? It arises when potential adopters of a platform with potentially strong network effects stall in adopting it because they are unsure whether others will adopt it as well. - How? Subsidize early adopters; influence perceptions and expectations. The chicken-or-egg problem A second managerial tension in dealing with platforms: - A platform cannot attract app developers unless it has a large base of end-users, and a large base of users is unlikely to join unless a platform has a large variety of apps available that end-users perceive as valuable. Neither side will join the other - End-users derive a large part of their value of a platform from the apps that they can use on it. This means that the absence of available apps will decrease the perceived attractiveness of a platform. - The reason app developers are going to perceive a platform as valuable only if it has a large installed base of end-users is simple: an app has large up-front fixed costs but low variable costs. - Producing the first copy can be a costly endeavor, but making additional copies is almost costless. Questions: - What? In order to attract one side of the network, you need to have built another side. And vice versa too. Neither side will join without the other - When? Determine how to get the different sides - How? 1. Price to one side below the rate it would otherwise change if that side were viewed as an independent market. 2. Discount the price-sensitive site and charge the price insensitive side. For instance, credit card companies bring consumers to their platform by offering frequent flyer points and cash back rebates; but this allows them to charge merchants will join without the other. The seesaw problem Questions: - What? Provide the proper degree of autonomy and integration - When? Define the architecture of the platform - How? Distribution of decision rights 1. Control portfolio 2. Pricing Strategy: The role of platform architecture I Tiwana (2014): - Architectural choices irreversibly preordain the evolutionary trajectories open and closed to a platform’s ecosystem - Platform ecosystems are intentionally designed complex systems composed of many interacting parts - Platform architecture specifies what these parts are, how they connect, and what they can and cannot do. - Architecture is a platform’s DNA that imprints evolvability. - Early architectural choices by a platform owner (i.e. the way the core of the platform and the interfaces are organized): 1. Are almost impossible to reverse later on 2. Influence who can participate in the ecosystem (who can be 3rd-parties), how can they innovate, and their incentives to innovate - Interfaces (APIs) are the main junction points in a platform, they are the source of: 1. Grow and evolution 2. Strategic tension between platform owners and actual and potential 3rd party applications Strategy: Platforms require a different mindset Strategy: Platform envelopment Definition: Platform envelopment refers to one platform provider moving into another one’s market, combining its own functionality with the target’s, to form a multi-platform bundle. Entering into an adjacent market with a multi-platform bundle Strategy for platform sponsors: 2 Types Two parallel types of strategies to address to become by Gawer and Cusamano (2008): - Coring (First mover: creating a new product that no one knows it exist, and you train the market. Its main advantage is that you are alone, and the disadvantage is that a lot of inversion is needed.) a) Set of activities a company can use to identify or design an element and make it fundamental to a technological system and to a market (search engine from google, the new business model for advertising) b) An element is core if it resolves technical problems affecting a large portion of other parts of the system c) Coring is about creating core control points in the platform - Tipping (Second mover) a) Set of activities or strategic moves firms can use to shape market dynamics and win platform wars when at least two platform candidates compete b) These moves cover sales, marketing, product development, and coalition building c) Tipping is the answer to how win in a platform competition. eBusiness-24/25 Session 05: Managing Platforms 4 Strategy for platform sponsors: Actions to consider Strategies to become a platform wannabes Strategy: Openness A platform is open to the extent that (Eisenmann, 2007): 1. no restrictions are placed on participation in its development, commercialization, or use 2. any restrictions (for example, requirements to conform with technical standards or pay licensing fees) are reasonable and nondiscriminatory —that is, they are applied uniformly to all potential platform participants How to recognize a potential platform opportunity? If you can affirmatively answer at least two of these 4 questions, then evolving into platform is possible Are there unexploited long tails (or niche groups) that are unattractive to directly pursue, but potentially attractive to smaller players in your industry? Do two distinct sides exist? Cross-side network effect is what turns the product into a platform Is one side already on board? - Cross-side network effects are when adding the second distinct group of adopters increases the value of your product or service to your existing and prospective core group of current customers. Can you generate positive cross-side network effects? - Cross-side network effects often arise when the second side you are thinking of produces downstream complements that your core customer group (the first side) can use to enhance whey they can do with your product or service. Converting your product into a platform? The safest approach is to get one side on-board first by offering a product or service that is valuable and attractive by itself. The most successful platforms started as stellarly successful products that subsequently evolved into platforms. The iPhone (precursos of iOS) began life as a standalone products, as did Facebook, skype, amazon, ebay, google, firefox, and dropbox. Only after one side (consumers) adopted it in droved did each of these add a second side to evolve into a real platform. The iPhone added the App store a year later, Facebook and google added advertising, skype and firefox added extensions, and dropbox added apps and API services. The very reason the second side (complementors) found these platforms attractive was that there was already a large group on the first side (end-users). Interfaces - API What is it? An API (Application Program Interface) is a software-to-software interface that enables third-party applications (modules or apps) to interact with the (core) platform. The core platform releases its API to the public so that 3rd-party modules/apps can access the services (e.g. data, functions) of the platform. The value of API’s Expanding reach across platforms and devices. APIs are channel to new customers and markets: APIs used externally unlock the power of partners to use business assets to extend the reach of your products or services to customers and markets you may not easily reach. Enhancing value and extending the brand Fostering innovation. APIs promote innovation: Through an API, people who are passionate about a problem can solve it on their own. Reaching niche markets. APIs create a path to lots of Apps: Apps are powered by APIs. When developers are motivated, they can use APIs and combinations of APIs to create new experiences for end users. The API value chain Business model: The API revenue Business model: A strategy using Open Source Software Managerial implications of platforms Winners in a platform market generally have the best platform strategy, not necessarily the best product. ’Best’ platform Open (but not too open) interfaces: APIs Modular architectures (easy to build on/extend) Compelling complements that result in a vibrant ecosystem Attracting both sides Orchestration ’Best’ product Best standalone value proposition Attracting customers Planning, predicting, customizing products CROWD & COMMUNITIES CROWD What is Crowdsourcing? The term was coined by Howe in 2006 in the rise of Crowdsourcing. - An easy definition: “Crowds are a hit. Millions of people, connected by the Internet, are contributing ideas and information to projects big and small. Crowdsourcing, as it is called, is helping to solve tricky problems and providing localized information. And with the right knowledge, contributing to the crowd — and using its wisdom — is easier than ever.” -The New York Times - A proper definition: “Crowdsourcing is a type of participative online activity in which an individual, an institution, a non-profit organization, or company proposes to a group of individuals of varying knowledge, heterogeneity, and number, via a flexible open call, the voluntary undertaking of a task. The undertaking of the task; of variable complexity and modularity, and; in which the crowd should participate, bringing their work, money, knowledge **[and/or]** experience, always entails mutual benefit. The user will receive the satisfaction of a given type of need, be it economic, social recognition, self-esteem, or the development of individual skills, while the crowdsourcer will obtain and utilize to their advantage that which the user has brought to the venture, whose form will depend on the type of activity undertaken” (Estelles-Arolas and Gonzalez, 2012) Is Crowdsourcing Being Used by Firms? What are the Advantages of Crowdsourcing? - Costs - Speed - Quality - Flexibility - Scalability - Diversity Why to Use Crowdsourcing? Boudreau & Lakhani 2013 What are the motivations of the participants? What is the Landscape of Crowdsourcing? Mechanical Turk Marketplace Crowdfunding COMMUNITIES How does software industry work? How is this problem solved? Knowledge reuse is key in software development, yet it may not be reused Principles of Copyleft Yet, firms were not adopting it The Cathedral and the Bazaar Different Licenses Copyleft - Software licensed under GNU GPL (general public license) - Slang term - Free software - The first example on Copyleft (GNU, a UNIX type operating system) - Creative Commons (CC) A close system to GPL - A GPL-like licensing scheme - Define the license - Allows you to more finely define what other people can do with your content (only use for personal needs, redistribute, sell, etc) - An example is Wikipedia Open Source - Copyleft but can be copyrighted - Free software Examples where Open Source communities are created Another examples What is the benefit for these organizations? How to manage these communities? Norms - Prevision of rewards: encouraging the members. Are the Open Source principles just applicable for the software industry? Strengths vs Weaknesses Strengths - Open collaboration a) Distributed contributions b) Open editing c) Open discussions - Woven in the fabric of the web as an innovation commons (simple rules, open to anyone) - Extensively used - Anyone can challenge Weaknesses - Too open a) Pranks and vandalism are rife b) People get in trouble often c) Siegenthaler incident - Too controlled a) Truth is determined by those who have the most time to spare - The leaders tend to cover their own people Some communities… INTRODUCTION TO DATABASES DATABASE: SQL Basics to database - Humanity stored information from immemorial times - First computerized database - Brief history of databases Main differences: a) The distinction of storing data in traditional files and databases is that databases are intended to be used by multiple programs and types of users. b) In Hierarchical Data model, files are related in a parent/child manner, with each child file having at most one parent file. c) In Network data model, files are related as owners and members, similar to the common network model except that each member file can have more than one owner. d) Relational databases can be defined using the following two terminologies: - Instance – a table with rows or columns. - Schema – specifies the structure (name of relation, name and type of each column) e) Object Oriented Database Model supports the modeling and creation of the data as objects. f) Object relational databases span the object and relational concepts. However, its architecture is not appropriate for web applications. - The evolution of databases approaches SQL Relational databases - A relational database consists of tables (usually many of them). - Rows are called records, columns are called attributes. There are relations between the tables (one-to-one, one-to-many, many-to-many). - There can be constraints, e.g., two attributes add up to 100 in each record, an attribute does not have repeated values, etc. - The database can be queried with SQL commands. Use joins when you want to retrieve information from different tables in a single query (this can be computationally expensive). What are the different types of relationship? Main Characteristics of Relational databases 1. Designed for all purposes 2. Strong consistency, concurrency, recovery 3. Mathematical background 4. SQL stands for Standard Query language 5. Lots of tools to use with (i.e. reporting services, entity frameworks) 6. ACID (Atomicity, Consistency, Isolation, Durability) 7. Vertical scaling (up scaling) What is ACID? A transaction is a sequence of operations that satisfy the ACID properties: 1. Atomicity. All the operations in the transaction are executed or none are. 2 2. Consistency. A transaction changing a data will be consistent on the structure of the database (i.e. constraints, cascades or triggers) 3. Isolation. A transaction will be executed in sequential order even if it is executed concurrently (e.g. multiple transactions reading and writing to a table at the same time) 4. Durability. A transaction once completed will be saved (e.g. in a hard disk in contrast to a ram memory) Vertical vs horizontal scalability (Most recommended to increase on vertical than horizontal) Is vertical storage enough? The evolution of data storage What about horizontal scaling in relational databases The problem is converting linked tabular rows to graph structures, which is known as the Vietnam of computer sciences: - Joins are expensive - Hard to scale horizontally - Impedance mismatch occurs, which are problems raised when mapping object-oriented to relational databases - Expensive (product cost, hardware, maintenance - Weak speed or performance - Low availability - Weak partition tolerance Yet, the evolution of data storage is not enough… Main characteristics of SQL: SQL: advantages - Relational databases are good at structured data with a lot of relations and transactional high-performance workloads (more than 60% of the brands use relational databases) - Relational database are good if data changes frequently, and it has many relations (since then only one write must be performed per change) - Provides the ACID properties - Offerings are proven and mature with a wide variety of tools available SQL engines examples MySQL, SQL Server, Oracle SQL: disadvantages - Difficult to scale: horizontal scaling is difficult/impossible, and vertical scaling is fine but verticality becomes expensive very fast - Fixed schema for organizing data. This can require significant up-front preparation which means that a change in the structure would be both difficult and disruptive to your whole system. - Limitations for lots of (thousands) read and write queries per second (complicated SQL join commands can take too much time) Let's take a closer look at the evolution in the types of data Key Points of NoSQL databases 1. Cluster friendly (it scales horizontally really well) 2. Schema-less. Documents can be created without having defined structure first. Also each document can have its own unique structure 3. 21 century web 4. Open-source NoSQL avoids 1. Overhead of ACID transactions 2. Complexity of SQL query 3. Burden of up-front schema design 4. Database administrator presence 5. Transactions (It should be handled at application layer) NoSQL provides 1. Easy and frequent changes to DB 2. Horizontal scaling (scaling out) A NoSQL database can handle more traffic by adding more servers 3. Solution to impedance mismatch 4. Fast development What is a schema-less data model? In relational databases: - You can't add a record which does not fit the schema - You need to add NULLs to unused items in a row - We should consider the datatypes (i.e. you can't add a string to an integer field) - You can't add multiple items in a field (You should create another table: primary-key, foreign key, joins, normalization...) In NoSQL databases: - There is no schema to consider - There is no unused cell - There is no datatype (implicit) - Most of considerations are done in application layer - We gather all items in an aggregate document Who uses NoSQL? Adoption of different databases What is a transaction? A transaction is a sequence of operations that satisfy the ACID properties: 1. The database consists of collections. Each collection consists of documents 2. Schema-less: Documents in the same collection can have different structure 3. Non-relational: There are no or very few relations between collections (so some information may be repeated) 4. There are several types of NoSQL databases: key-value, column-based, document-based, etc. Characteristics of NoSQL databases NoSQL: advantages 1. Good for non-relational data 2. Schema-less architecture allows for frequent changes to the database and easy addition of varied data to the system. 3. Both horizontal and vertical scaling is possible. Runs well on distributed systems (the cloud) 4. Great performance for mass (simple) read and write requests (avoids overhead of ACID transactions and the complexity of SQL querying) NoSQL: disadvantages 1. Slower response time for sets of data. 2. Writes can be slow if they involve duplicated data (e.g. if a service has to be modified and this service appears in the document of many users). 3. It does not allow the data manipulation flexibility of SQL databases, where you can easily ‘slice and dice’ your data different ways for different purposes. 4. Installation, management and tool sets still maturing. NoSQL: examples MongoDB, Couchbase, Redis, Cassandra The information is distributed in multiple tables, how do we make sense of it? Aggregation: - An aggregate is a collection of related objects that we wish to treat as a unit - Given a (big) SQL database, one can make a NoSQL database from it by taking aggregate documents. - Then these aggregations can be stored in clusters. How does the aggregation retrieves data? This is how an aggregate document from a SQL database may look like: Why is aggregation useful? We are left with a NoSQL database which can be comfortably stored in a distributed way (clusters), possibly in a cloud. Aggregate Data Models We are left with a NoSQL database which can be comfortably stored in a distributed way (clusters), possibly in a cloud. A key-value database, or key-value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays. Key-value Data Model - The main idea is the use of a hash table (python dict) - Access data are called keys - Data has no required format; data may have any format - Data model: (key, value) pairs - Basic Operations: a) Insert (key, value) b) Fetch (key) c) Update (key) d) Delete (key) Example - Shopping cart: During the holiday shopping season, an e-commerce website may receive billions of orders in seconds. Column Data Model What is it? A column store uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row in the same table. Characteristics: - The column is lowest/smallest instance of data - Each row may contain different columns - Information is accessed column-wise (fast aggregations) Example - Sport statistics: Fast access to several statistics of each part of a sport game to recompute in real time match information (display information, betting platforms). Document-based Data Model Document-based database: A document-based database is designed for storing, retrieving and managing document-oriented information. Characteristics: - Semi-structured data - Query Model: document attributes - Aggregations: Divide & Conquer - Unlike simple key-value stores, both keys and values are fully searchable in document databases Example: Graph Data Model Graph databases Graph databases store and navigate relationships. Relationships are first-class citizens, and most of the value of graph databases is derived from these relationships. They use nodes to store data entities, and edges to store relationships between entities. - An edge always has a start node, end node, type, and direction. They describe parent-child relationships, actions, ownership, … - There is no limit to the number and kind of relationships a node can have. Example - Recommendation engines: Graph databases are a good choice for recommendation applications. With graph databases, you can store in a graph relationships between information categories such as customer interests, friends, and purchase history. Comparison and Categorization of Aggregate Data Models CLOUD COMPUTING Cloud computing: you don't want to have a server, but you hire part of it. What is Cloud Computing? Computing facilities provided to the general public. The on-demand leasing of different resources such as CPU, GPU and storage - Adapted to consumers - Service providers A formal definition of Cloud Computing NIST definition of cloud computing. Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction Cloud Computing & related technologies There are four main types of related technologies: 1. Grid Computing: coordination of networked computing resources to achieve a common objective 2. Utility Computing: on-demand provision of resources 3. Virtualization: virtual instead of physical machines 4. Autonomic Computing: self-management A layered model of cloud computing Advantages of cloud computing over traditional means Advantages: 1. No up-front investment 2. Lowering operating cost 3. Highly scalable 4. Easy access 5. Reducing business risks and maintenance expenses Cloud computing characteristics In addition, there are several characteristics that cloud computing provides that are unique: 1. Multi-tenancy 2. Shared resource pooling 3. Geo-distribution and ubiquitous network access 4. Service oriented 5. Dynamic resource provisioning 6. Self-organizing 7. Use-based pricing Cloud Computing Economics Types of clouds - Public clouds: A cloud in which service providers offer their resources as services to the general public. Public clouds offer several key benefits to service providers, including no initial capital investment on infrastructure and shifting of risks to infrastructure providers. However, public clouds lack fine-grained control over data, network and security settings, which hampers their effectiveness in many business scenarios. - Private clouds or internal clouds: Private clouds are designed for exclusive use by a single organization. A private cloud may be built and managed by the organization or by external providers. A private cloud offers the highest degree of control over performance, reliability and security. However, they are often criticized for being similar to traditional proprietary server farms and do not provide benefits such as no up-front capital costs. - Hybrid clouds: A hybrid cloud is a combination of public and private cloud models that tries to address the limitations of each approach. In a hybrid cloud, part of the service infrastructure runs in private clouds while the remaining part runs in public clouds. Hybrid clouds offer more flexibility than both public and private clouds. Specifically, they provide tighter control and security over application data compared to public clouds, while still facilitating on-demand service expansion and contraction. On the down side, designing a hybrid cloud requires carefully determining the best split between public and private cloud components. Business Model It is based on a service-driven business model Every layer of the architecture is a service-driven model: 1. Infrastructure as a Service (IaaS). On-demand provisioning of infrastructure resources via virtual machines. e.g. Amazon EC2 2. Platform as a service (PaaS). It provides a resources such as operating system support and/or software development frameworks. E.g. Google App Engine 3. Software as a Service (SaaS). Provides on-demand applications. E.g. Salesforce.com, Gmail, online games, Microsoft Office 365 Theoretically there is a distinction between PaaS and IaaS, but… Are there any difference between PaaS and IaaS? 1. Infrastructure providers, who manage cloud platform and lease resources according to a usage-based pricing model 2. Service providers, who rent resources from one or many infrastructure providers to serve the end users Transformation of Traditional Business Models Cloud computing companies seek to transform traditional businesses by providing cost-efficient, reliable and powerful computational power. BIG DATA Big data is the generation of value from data sets characterized by huge amounts of frequently updated data in various formats, such as numeric, textual, or images/videos. - Big data is similar to ‘small data’, but bigger in size (roughly, it cannot be stored and processed in a single machine) - Having bigger data requires different techniques, tools and architectures - A data set is ‘big’ if it is beyond the capabilities of generally used conventional data management and analytical methods to capture, store, access, manage, share, process, analyze, and visualize within an acceptable elapsed time Sources of big data - Social media data - Search engine data - Transaction data - Internet of Things (IoT). For example medical data from smart watches - Online customer reviews - Customer purchase history - Transport data - And a long etc. Volume It is the quantity of information “There were 5 Exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days” - Eric Schmidt, Executive Chairman, Google - A PC had around 10 gigabytes of storage in 2000. - In 2018, 2.5 million terabytes of data were created every day. Velocity (ex: smartwatch) It is challenging to process and analyze new incoming massive data in a short period of time. - In 2016, 5.5 million new devices were estimated to be connected every day to collect, analyze, and share data - The enhanced data streaming capability of connected devices will continue to accelerate the velocity. Variety Different elements. - Big data is not only numbers, dates, and strings. It also includes audio and video, unstructured text (log files, social media), 3D data, etc. - Traditional database systems were designed to handle structured data. - Nowadays, the majority of data is unstructured. Veracity It is the quality of the data. - Is the data clean and accurate? Value The ability to transform data into business. - If a company correctly uses big data, its ability to get to know customers and monetize all that information is enormous - They can offer customers what they want or need at the right time. New tools for the same old tasks: how do we store big data? Big data: Data splitted into small servers/pieces, it’s , reduced etc… Hadoop Distributed File System (HDFS): - A distributed file system designed to provide efficient, reliable access to data using multiple geo-diverse nodes or clusters of servers - Data is partitioned into 64MB blocks. Each block is stored in a node (a server) - Blocks are cloned three times to avoid faults - It can be deployed in a traditional onsite datacenter as well as in the cloud - Inspired by Google File System (GFS) New tools for the same old task: how do we manipulate big data? MapReduce: Framework for distributed computing on large data sets on clusters of computers. 1. Divide data into smaller subsets 2. Distribute those subsets to computing nodes 3. Each node manipulates the filtered data in parallel 4. Onsite datacenter or cloud. Big Data Strategy? Firms do not need a big data strategy, but they need a business strategy that integrates big data principles. Big data can be used to - increase customer acquisition - reduce customer churn - reduce operational and maintenance costs - optimize prices and yield - reduce risks and errors - improve compliance, improve customer experience … Big Data Business Model Maturity Index It measures how effective an organization is at integrating data and analytics to power their business model. 1. Business Monitoring. 2. Business Insights. 3. Business Optimization. 4. Data Monetization. 5. Business Metamorphosis. Business Monitoring When a company monitors all the data and uses business intelligence (getting smart data). Leveraging data warehousing and Business Intelligence to monitor the organization’s performance - Only summarized and aggregated data is analyzed: e.g. monthly neighborhood sales instead of individual customer transactions - “What are our best selling cars? What type of customer is buying those cars?” Business Insights Leverage the predictive analytics to uncover customer, product and operational insights buried in data sources (i.e. internal or external). - Apply data science methods to all available data: e.g., consumer comments, e-mail conversations, doctor/teacher/technician notes, work orders, service requests - “The group with the highest propensity to buy fully featured hybrid cars consists of millenials who are online gamers and have the most current smartphones” Business Optimization Deliver actionable insights (e.g., recommendations, scores, rules) to employees, partners, and/or customers to help them make better decisions supporting the targeted business process. - Provide product recommendations to customers (E.g., Amazon, Netflix) - “We should shift our marketing spend from auto magazines to gaming magazines and websites to increase sales of hybrid cars to millenials by 12%” Data Monetization Create new sources of revenue from unused data - Repackage insights to create entirely new products and services that help organizations to enter new markets and target new customers or audiences. - Sell data. E.g. MapMyRun sells GPS data to sport-related companies - “We are now offering an optional add-in car feature: a LED screen that mirrors mobile devices’ screens via USB connection”. Business Metamorphosis Integrate the insights captured about customers’ usage patterns, product performance behaviors, and overall market trends to transform the business model - Retailers moving into the “Shopping Optimization” business by recommending specific products given customers’ current buying patterns - John Deere selling farming optimization instead of farming equipment, or Boeing selling air miles instead of planes, etc. - “We are now licensing our screen mirroring technology. We are now selling to aviation, rail, and hospitality indutries” Big Data Business Model Maturity Index Does technology lead to economic impact and new business models? Organizations do rather than technology itself - Printing Press Expanded literacy (simplified knowledge storage and enabled knowledge dissemination and the education of the masses) - Steam Engine (Railroads and Steamboats) Sparked urbanization (drove transition from agricultural to manufacturing-centric society) - Computers Automated common processes (thereby freeing humans for more creative engagement) - And a long etc. Notice how business metamorphosis is not a new phenomenon Using Big Data to go above and beyond competitive parity: Competitive Differentiation Competitive parity vs competitive differentiation - Competitive parity allows a company to create a baseline that, at least, is equal to the operational capabilities across the industry. - Competitive differentiation is achieved when an organization creates and captures unique value for the end customer. Competitive Differentiation - Most companies use technologies in a way that has existed for a long period of time (e.g. ERP, CRM, or SFA) - This does not provide competitive differentiation, but provides competitive parity - Competitive differentiation can be achieved using existing technology in creative ways. Examples: Competitive Differentiation investments Competitive differentiation involving big data - Google — Ad Serving - Yahoo — Behavioral Targeting - Facebook — Ad Serving - Apple — iTunes - Netflix — Movie Recommendations - Amazon — Recommendations and One-click - Walmart — Demand Forecasting - Procter & Gamble — Brand and Category Management - American Express and Visa — Fraud Detection A new form of thinking… - Don't Think Big Data Technology, Think Business Transformation. Go beyond the 3 Vs of Big Data (i.e. volume, variety, and velocity) to focus on how to create and capture value. - Don't Think Business Intelligence, Think Data Science. Business intelligence focuses on reporting, rather than predicting. - Don't Think Data Warehouse, Think Data Lake. The objective is that saving data is a tool that enables saving, rather than data warehousing that is a cost to minimize - Don't Think What Happened, Think What Will Happen. Think on prescriptive analytics rather than describing what happened (e.g. 'How many sales were done last year', to order X amount of raw material to cover for the sales) DATA SCIENCE What is Data Science? Possible definitions - Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. - Data science is a ”concept to unify statistics, data analysis, machine learning and their related methods” in order to ”understand and analyze actual phenomena” with data. Data Science vs Business Intelligence - Business intelligence is about descriptive analysis (“what happened?”), and standard reporting. In contrast Data Science is about predictive analysis (“what is likely to happen?”), and prescriptive analysis (“what should I do?”) a) How many products did a company sell? - Business Intelligence cares about the past (i.e. what happened?). In contrast to asking about why it happened, and the actions that need to be done a) How many products will I sell next month? [Predictive Question] b) Should I order XXX of raw material? [Prescriptive Question] Ethical concerns in data science Privacy Damage To disallow people to express themselves selectively - An individual’s right to choose which of their activities and facts are shared with others is an important consideration that data science teams need to contemplate. - People can be re-identified from anonymous data using, e.g. zip code, birth date and gender. Discrimination Damage (the data is racist) The unfair treatment of a category - Data science models can be built using data that records a bias, and thus, the model might also have that bias, and as such, systematically disadvantage a societal sub-group - E.g., societal sexism and racism can be reflected in some data-sets (e.g. in text data) - E.g. initiatives that rely on gathering mobile app data may receive more data from wealthy social sectors Dehumanisation Damage - To treat people as data points - E.g. social reputation score systems, such as China’s Citizen Score - In a study, doing harm to more people was judged to be less harmful[?] - Self-driving car dilemma Manipulation Damage - To deceive with data - Use of algorithms, automation, and human curation to purposefully distribute misleading information over social media networks (computational propaganda1 ) Business Intelligence Process Business Intelligence Process: Pre-Build Data Model - A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. - Schema on load a data model must be defined and built prior to loading data into the data warehouse - Data is stored according to the data model in a data warehouse. Business Intelligence Process - Define the Report. Specify the information that has to be obtained or computed from the data (query) - Generate SQL Commands. Transform the query into SQL code. This may be done automatically by a BI tool. Business Intelligence Process: Generate SQL Commands Business Intelligence Process: Create Report The BI tool issues the SQL commands against the data warehouse and creates the corresponding report. Business Intelligence Process: Summary Data Science Process Data Science Process: Define Hypothesis to Test - Identify the prediction to be made or the hypothesis to be tested - Examples: predict risk of credit card default, predict energy consumption of households, find population clusters, etc. Data Science Process: Gather Data - Gather relevant or potentially interesting data from a multitude of sources (internal or external) - Store the data in a data lake, i.e. a system or repository of data stored in its natural/raw format - Examples of data lakes: Azure Data Lake (from Microsoft Azure), Amazon S3 (from Amazon Web Services), Apache Hadoop (open source) - There is no need to follow any data model Data Science Process: Build Data Model This step can include data wrangling procedures such as: - Cleaning data (treat missing values, categorical data, outliers, standardization, etc.) - Finding or creating relevant variables - Transforming and organizing the data into a suitable format for the statistical model to be used. Data Science Process: Visualize the Data Use data visualization tools to: - identify correlations in the data worthy of investigation - find outliers in the data that may need special treatment - identify valuable variables - etc. After this step it may be necessary to go back to the previous one. Data Science Process: Build Analytic Models - Create an informative machine-learning model that predicts the target accurately - Algorithms: linear or logistic regression, decision trees, artificial neural networks, K-means, etc. - Different algorithms can be tried and combined (ensembling) - Highly iterative process. Data Science Process: Evaluate Model Goodness of fit - The goodness of fit of a statistical model describes how well the model fits a set of observations - Methods: confusion matrix, evaluation on a test dataset, statistical tests, etc. Data Science Process: Summary Business Intelligence - Pros & Cons Main BI characteristics: - It relies on a pre-built data model - Data must be structured and it must fit the data model - Provides descriptions instead of predictions and prescriptions - Easier to use for the final user - Requires less technical and scientific background than data science Data Science for the Social Good Ex: Odile hurricane measuring people’s economic resilience to natural disasters ( https://www.youtube.com/watch?v=yExR_Hig4xw ) ARTIFICIAL INTELLIGENCE The world is changing An overview of AI Drake: - ML models used to produce artificial Drake songs - Songs are indistinguishable from real ones - Drake’s music label invoked copyright law to remove songs from major platforms - It’s easy to produce such songs and the r/drizzy subreddit is flooded with clones - ML can elevate a programmer’s project to pop superstar level - AI is going to revolutionize industries - Universal Music Group banned AI Drake to be on the side of history, artists, and human creative expression - Tools like this bring fans’ creative expression to a new level What is AI? Human Intelligence is characterized by: Main properties: - Reasoning - Interaction - Learning - Adaptability - Problem Resolution - Decision-Making Human vs. Artificial Intelligence Human AI Systems - Idea Generation - Memory - Intuition - Velocity - Common Sense - Capacity for calculus - Emotions - Not getting stressed - Creativity - Not getting tired Definition of AI AI is the branch of science which makes machines perform tasks which would require intelligence when performed by humans. Thinking Acting Thinking Humanly Acting Humanly “The exciting new effort to make computers “The art of creating machines that perform think… machines with minds, in the full and functions that require intelligence when literal sense” (Haugeland, 1985). performed by people” (Kurzweil, 1990). “The automation of activities that we associate “The study of how to make computers do things with human thinking, activities such as at which, at the moment, people are better” decision-making, problem-solving, learning…” (Rich and Knight, 1991). (Bellman, 1978) Thinking Rationally Acting Rationally “The study of mental faculties through the use “Computational Intelligence is the study of the of computational models” (Chamiak and design of intelligent agents” (Poole et al, 1998). McDermott, 1985). “AI… is concerned with intelligent behavior in “The study of the computations that make it artifacts” (Nilsson, 1998). possible to perceive, reason, and act (Winston, 1992). History of AI AI Applications Ai examples: - Intelligent households - Autonomous cars - Industrial robots - Service robots - Lawn mower - Mascot robots - Medicine - Professional maintenance Examples of AI usage in different industries Industry AI Usage Healthcare AI-powered medical imaging analysis, personalized medicine, drug discovery. Finance Fraud detection, algorithmic trading, customer service chatbots. Retail Personalized product recommendations, inventory management, supply chain optimization. Transport Self-driving cars, maint, for vehicle route optimization. Education Intelligent tutoring systems, personalized learning, plagiarism detection. Manufacturing Predictive maintenance for machinery, quality control, supply chain optimization. Agriculture Crop monitoring and yield prediction, precision farming, autonomous equipment. Energy Predictive maintenance for power plants, energy demand forecasting, renewable energy optimization. General AI - Tricking ChatGPT Into Building a Dirty Bomb - Automating sets of tasks via agent AI - YouTube summaries powered by ChatGPT - FinChat generates answers to questions about public companies and investors. - The world’s first generative trend forecasting service powered by large language models. - Personal Assistant - Do your CV based on your profile - StabilityAI helps you convert text into images - Durable, an AI Website builde - Boomy, helps you to make original songs in seconds. Other examples: - Free ChatGPT-4 - Access Al tools powered by new ChatGPT-4 for free - Perplexity - ChatGPT, but with voice & real-time search for mobiles - New Nvidia Canvas - Helps you edit videos with great quality in seconds - Facial Express - Real-time detection of your feelings using Al Seenapse - Generate dozens of divergent ideas - MurfAI - Make studio quality voice overs from text in seconds - WavTool - It’s ChatGPT-4, but for Music and editing - DimeADozen - Validate your business idea in seconds - LERF - Super easy to search for real world physical things. - Kickresume - Free Al tool helps you land 5x more job interviews - 10Web - What if you could just describe the website that you want and boom - Wonderdynamics - It’s like having a whole ass studio in your pocket - GEN-2 - It lets you generate videos with nothing but words. - UizardAI - Design stunning websites, apps, mockups in minutes - ColorGPT - Get the hex code of any color in real life using your phone - Rationale - Al-assisted decision making, make rational decisions - Dropchat - Instantly chat with any book by searching using title, author, or ISBN number - Rask - Localise videos in 60+ languages quickly - Microsoft SwiftKey - let’s you use the Bing Al Chatbot in any App - TextLayer - Discover and understand the latest research papers using Al - Toolkit - Easiest way to generate and use Al plugins - Playlistable - Create personalized playlists for any mood or occasion - Wisdolia - Al generated flashcards from any YouTube video, article, or PDF - StabilityGPT - Run stable diffusion directly in ChatGPT - AutoGPT - You can now give a task and it can now autonomously plan, execute, browse the web, and revise strategies to complete tasks - Tripnotes Al - This Al tool will create personalized itineraries to help travelers find the right places to go. - Quillbot - It helps you rewrite text, edit, eliminate grammar errors, change the tone and paraphrase content for free - Rose Al - A tedious process of finding, visualizing, and transforming data is now as simple as a Google search - Koe - Al powered tool that can now transform voices in real-time with latency as low as 60ms on cpu - Stockimg - Generate accurate logos, images, banners, and contents just from texts - Playtext - Read faster using the power of Al - Superchat - Message historical and fictional characters via ChatGPT - Hugging Face - Access ChatGPT-4 for free - GPTGO - ChatGPT combined with Google search - Aomni - Supercharge your research with AutoGPT Insyte-use product details to create the website - Gptrim - Reduce Your GPT Prompt Size by 50% for Free - AlTemplates - Instantly get better, business-ready results with ChatGPT Status of AI Industry races ahead of academia Shift to industry: - Before 2014, most major machine learning models were released by academia - Since then, industry has become the main producer of significant machine learning models - In 2022, there were 32 major machine learning models produced by industry, compared to only three produced by academia - Building cutting-edge AI systems now requires large amounts of data, computer power, and money - Industry has more of these resources compared to nonprofits and academia. Performance saturation on traditional benchmarks Benchmarks saturation: - AI has been producing state-of-the-art results, but year-over-year improvement on many benchmarks is marginal - The speed at which benchmark saturation is being reached is increasing - New, more comprehensive benchmarking suites are being released (e.g., BIG-bench and HELM). AI is both helping and harming the environment AI and environment - New research suggests that AI systems can have significant environmental impacts - BLOOM’s training run emitted 25 times more carbon than a single air traveler on a one-way trip from New York to San Francisco, according to Luccioni et al., 2022 - However, new reinforcement learning models like BCOOLER show that AI systems can be used to optimize energy usage. The number of incidents concerning the misuse of AI is rapidly rising AI misuse rising: - The AIAAIC database tracks issues to the ethical misuse of AI - The number of AI incidents and controversies has increased 26 times since 2012, according to the database - The growth in incidents is evidence of both greater use of AI technologies and awareness of misuse possibilities. The demand for AI-related professional skills is increasing across virtually every American industrial sector AI job demand increasing: - The number of AI-related job postings has increased across every sector in the US for which there is data, except for agriculture, forestry, fishing, and hunting - On average, the increase in AI-related job postings was from 1.7% in 2021 to 1.9% in 2022 - This suggests that employers in the United States are increasingly looking for workers with AI-related skills. What is the AI europe view: The European approach to artificial intelligence centers on excellence and trust, aiming to boost research and industrial capacity while ensuring safety and fundamental rights. While the proportion of companies adopting AI has plateaued, the companies that have adopted AI continue to pull ahead AI adopters pulling ahead: - The proportion of companies adopting AI in 2022 has more than doubled since 2017 - However, adoption has plateaued in recent years between 50% and 60% - Organizations that have adopted AI report realizing meaningful cost decreases and revenue increases, according to McKinsey’s annual research survey Policymaker interest in AI is on the rise AI policymaker interest rising: - The number of bills containing ”artificial intelligence” that were passed into law grew from 1 in 2016 to 37 in 2022, according to an AI Index analysis of legislative records of 127 countries - Mentions of AI in global legislative proceedings have increased nearly 6.5 times since 2016, according to an analysis of the parliamentary records on AI in 81 countries. Chinese citizens are among those who feel the most positively about AI products and services. Americans... not so much Attitude towards AI: China positive, US negative - 78% of Chinese respondents agreed that products and services using AI have more benefits than drawbacks, which is the highest proportion among the surveyed countries, according to a 2022 IPSOS survey - Saudi Arabia and India are the next most positive countries, with 76% and 71% of respondents respectively agreeing with the statement - Only 35% of sampled Americans agreed that products and services using AI had more benefits than drawbacks, which is among the lowest proportion among the surveyed countries. AI Ethics Ethical Guidelines for AI Development and Deployment Fairness and non-discrimination: - AI systems should be developed and deployed in a fair and non-discriminatory manner - Data used to train AI should be representative of the population it will be used on - AI should not unfairly discriminate against individuals or groups based on factors such as race, gender, or age Transparency: - AI systems should be transparent in their decision-making processes - The reasoning behind the system’s decisions should be explainable and understandable to humans. Privacy and data protection: - AI systems should respect privacy and data protection rights of individuals - Personal data should be collected, processed, and stored securely - Individuals should have control over their own data. Accountability: - AI developers and deployers should be accountable for the outcomes of their systems - They should take responsibility for any negative impacts that the system may have on individuals or society as a whole. Safety: - AI systems must be developed and deployed with safety and security in mind - The systems must be tested thoroughly to ensure they do not pose physical or psychological harm - The safety of individuals interacting with AI systems must be taken into consideration Government Implementation of AI Curricula Time Allocated in AI Curricula by Topic Example of AI Education Austria guideline for AI in education: The curriculum aims to prepare students for careers in ICT and AI while emphasizing responsible and ethical use of technology. - Digital basics such as using operating systems and software - Data literacy principles including data collection, analysis, and evaluation - Programming, algorithms, and simulations - Ethical dilemmas and social discourse on AI - Understanding of the democratic process and public statements Key Points - AI is a technology that is transforming many areas. - AI is a fascinating and rapidly evolving field with many potential applications. DIGITAL ASSETS USING BLOCKCHAIN Key Concepts: - Accounting: the origins - Accounting: the double entry - Accounting: the triple entry What is blockchain? Basic concepts: - Blockchain technology is basically a type of distributed data base (through a computerized network) that counted a growing list of data records (blocks) linked together - The database does not allow rewriting, updating or deleting the data that have committed to the series of data/records stored in it (i.e., blocks) - So, it is a distributed data base technology of just reading and writing. Key things: what is written, we cannot change it. The problem of the Byzantine Generals How does Bitcoin work? Can there be infinity of validating nodes? There can be countless validating nodes? What does a single node do? - Proof of Work - Proof of Stake How are Bitcoins Generated? Amateur How are Bitcoins Generated? Professional Smart Contracts What are they? - A smart contract is a computer protocol designed to facilitate, verify, or digitally enforce the negotiation or execution of a contract. - Smart contracts allow credible transactions without third parties - These transactions are traceable and irreversible A paradigm shift (Ex: Centralized→Spotify) What can be done with a community-controlled medium (not controlled by any organization or state)? - Banking system - Personal identification - … A paradigm shift: Web 3.0 The web of value exchange without the need for intermediaries - Web 1.0 → Links → Static websites - Web 2.0 → Likes → Interactive content - Web 3.0 → Tokens → Privacy and ownership of the content Why Web 3.0 Apps are disruptive? Network effects - Metcalfe’s Law Why Web 3.0 Apps are disruptive? Bootstrap the Network Effects via tokens to early adopters Why Web 3.0 Apps are disruptive? The Cantillon Effect APPLICATIONS Store Value: Bitcoin & Ethereum What is a Bitcoin? - Bitcoin is cryptocurrency - It is a decentralized digital currency without a central bank or a single administrator that can be sent from user to user on the peer-to-peer bitcoin network without the need for intermediaries. Is it really a currency? Currency is a unit of account Is it really a currency? Currency can store value Bitcoin: Advantages and Disadvantages Advantages: - Easier to transport anywhere in the world - Easier to insure - Immune to sovereign censorship, confinement or confiscation - Immune to inflation and bank defaults Disadvantages: - Energy consumption (73 TWh equivalent to Austrian consumption) - Not controlled by the state - No anonymity Ethereum - Bitcoin + smart contract. - The silver equivalent of precious metals - Many applications built on top of it - Transition from work test to participation test to be more sustainable (99.9 % estimated reduction) Ethereum, BTC and their environmental impact DEFI: Decentralized finance Financial system: Real world Exchange House: Real World Exchange House: Digital World Futures Market: Real World Futures Market: Digital World Lending & Borrowing: Real World Lending & Borrowing: Digital World Asset Management: Real World Asset Management: Digital World NFT: Non-fungible token The main characteristics of NFT: - It can be sold and traded. - It can be associated with a particular digital or physical. - It contains basic information (author, owner and signature) or the full NFT on-chain. - A license to use the asset for a specified purpose. Why are so valuable? - Scarcity - Historicity - Rarity or Uniqueness - Community - Utility How it can be used? Ownership & License Your ownership of land, houses and apartments is a 100% made-up social convention similar to NFT. What is the process of buying it? Send a wire and sign papers with pen and paper in front of the notary. NFT relationship with JPEG is ”equivalent” to papers and land: - Does that deed automatically build a fence? No - Does that deed give you superpowers? No - Does that deed keep out people, animals, fish, storms or snow from your property? No - Does that deed do anything at all in the physical world where your property is located? Of course not Real Estate Social Conventions: 1. You do not go on someone else’s ”private property” uninvited (”trespassing”) 2. The database of record is the title registry 3. Disputes are handled in court 4. Ultimate enforcement of (1) to (3) is a gun and a metal cage Differences in digital and tangible ownership: Time & Guns - Time. Digital ownership is possible from few year, in contrast to centuries. - Time. We were raised in a tangible ownership, so it is normal for us. - Time. Our kids will take this for granted. - Guns. Ownership is protected mathematically not using violence. How it can be used? - Generative Art - Brand - Social Token - Utility Token - Game Token - Metaverse—Gatekeeper Other applications - 0x: Decentralization of markets - Etherisc: Decentralization of markets - Status: New browser + additional capabilities Blockchain & Logistics: - Automated payments to suppliers - Meat traceability - Electric power microgrids - RFID-based contracts and execution - Frozen Chain - Internet of things (ex: solo driven Ubers) Liquid Democracy Applications risks Wild West: - It is an unregulated market - Scam projects - Hacked Project - Robots rob - Social engineering

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