Module Notes.docx

Types of systems: 1. Management information systems (MIS) 2. Transaction processing systems (TPS) 3. Decision support systems (DSS) 4. Executive information systems (EIS) 5. Enterprise systems (ES) 6. End-user information systems (EUIS) 7. Customer relationship management system (CRM)...

Types of systems: 1. Management information systems (MIS) 2. Transaction processing systems (TPS) 3. Decision support systems (DSS) 4. Executive information systems (EIS) 5. Enterprise systems (ES) 6. End-user information systems (EUIS) 7. Customer relationship management system (CRM) 8. Database management systems (DBMS) 9. Enterprise resource planning (ERP) system 10. Information processing systems 11. File management systems 12. Database systems 13. Data management system 14. **[\ ]** **[Module 1:]**  **Big Data** Big Data refers to data that can't be processed or analysed using traditional processes or tools. - **Volume**: Volume refers to the vast amounts of data generated every second. We are not talking Terabytes but Zettabytes or Brontobytes. - **Velocity**: The Velocity is the speed at which the data is created, stored, analysed and visualised. Every minute we upload 100s of hours of video on YouTube - **Variety**: Variety refers to the different types of data we can now use. In the past we only focused on structured data that neatly fitted into tables or relational databases, such as financial data. In fact, 80% of the world's data is unstructured (text, images, video, sensor data, voice, etc.) **Systems** A system is a collection of interrelated components that function together to achieve some predefined purposes or objectives. The word 'system' is used in many different phrases -- for example, computer system, health system or solar system.  Business problems can be conveniently classified as soft (sometimes called "messy") or hard: - **"System thinking**" as a way of making sense of available qualitative data. - "**Soft" (or "messy") problems** are those associated with crisis or conflict situations, where there is unease between a number of people. - **"Hard" problems** are those where the problem can be clearly articulated. business analytics as a way of making sense of quantitative data. **Systems Thinking** Systems thinking is the process of using the characteristics of a system to understand the real world. - Soft systems thinking recognises the human dimension in situations. This approach usually involves identifying something as a system and subsequently recognising the characteristics and relationships of various components of the system. It leads to gaining an insight into the situation that may not be obtained from focusing solely on the quantitative details of the situation. Visualisation is a key aspect of Soft System Methodology. Once the necessary information is collected, a cartoon or Rich Picture is drawn representing the problem situation, the structures, processes, relationships and issues. A diagram of a system Description automatically generated  A **system** is a collection of interrelated components that function together to achieve some predefined purposes or objectives. Systems typically display nine characteristics as follows: 1. **Component**: a part, or aggregation of parts, of a system, commonly referred to as a subsystem; 2. **Interrelated Components**: the dependency of one subsystem on one or more other subsystems. Subsystems are related and usually interact with each other in order to achieve their pre-declared objectives, within their environment; 3. **Boundary**: the line that distinguishes the inside from the outside of a system and so distinguishes the system from its environment; 4. **Environment**: everything external to a system that interacts with the system; 5. **Interfaces**: points of contact where a system meets its environment or where subsystems meet each other; 6. **Inputs**: whatever a system takes from its environment in order to fulfil its purpose; 7. **Outputs**: whatever a system returns to its environment in order to fulfil its purpose; 8. **Constraints**: limits or conditions within which a system can accomplish its objectives; and 9. **Stakeholders**: person(s) or organization(s) that have a direct interest in the system. Systems can be classified on a continuum between the extremes of **open systems** and **closed systems**. Open systems interact freely with their environments, taking in inputs and returning outputs. Closed systems don't interact with their environments, so changes in the environment do not affect the system. Business problem solvers, sometimes referred to as "systems analysts", use the nine systems characteristics to understand, in a holistic way, business situations. They consider businesses as if they were open systems. This is termed applying "**systems thinking**" to a business situation. **Data** are plain facts. The word "data" is plural for "datum." When data are processed, organized, structured or presented in a given context so as to make them useful, they are called Information. It can be anything like name of a person or a place or a number etc. Data is the name given to basic facts and entities such as names and numbers. The main examples of data are weights, prices, costs, numbers of items sold, employee names, product names, addresses, tax codes, registration marks etc. Data is the raw material that can be processed by any computing machine. Data can be represented in the form of: - Numbers and words which can be stored in computer's language - Images, sounds, multimedia and animated data as shown. **Information** is data that has been processed in such a way as to be meaningful to the person who receives it. it is any thing that is communicated. Information is data that has been converted into a more useful or intelligible form. It is the set of data that has been organized for direct utilization of mankind, as information helps human beings in their decision making process. Examples are: Time Table, Merit List, Report card, Headed tables, printed documents, pay slips, receipts, reports etc. The information is obtained by assembling items of data into a meaningful form. Other forms of information are pay-slips, schedules, reports, worksheet, bar charts, invoices and account returns etc. Information is needed to: - To gain knowledge about the surroundings, and whatever is happening in the society and universe. - To keep the system up to date. - To know about the rules and regulations and bye laws of society, local government, provincial and central government, associations, clients etc. as ignorance is no bliss. - Based on above three, to arrive at a particular decision for planning current and prospective actions in process of forming, running and protecting a process or system Characteristics of information 1. Relevance: is the information relevant? 2. Accuracy: how complete, correct, and secure are the data and information? 3. Timeliness: how quickly is the input transformed to proper output? 4. Economy: what level of resources is needed to provide the required information? 5. Efficiency: what level of resources is required for each unit of information output -- in particular, compared with competitors? 6. Reliability: how reliable is the provided information? 7. Usability: is the provided information understandable? [People use information produces by analytic techniques to generate business intelligence.] Managers better understand their business and the external competitive environment when making decisions.  **Knowledge** Human mind purposefully organized the information and evaluate it to produce knowledge. In other words the ability of the person recalls or uses his information and experience is known as knowledge. For example, "386" is data, "your marks are 386" is information, and "It is result of your hard work" is knowledge. **Knowledge is of two types:** - **Facts based or Information based Knowledge**: The knowledge gained from fundamentals and through experiments. The knowledge is derived from the information contained in fundamental science derived from experiments, rules, regulations commonly agreed by experts. - **Heuristic Knowledge**: It is knowledge of good practice, experience and good judgment like hypothesis. It is the knowledge underlying "expertise", rules of thumb, rules of good guessing, that usually achieves desired results but do not guarantee them. [Today Knowledge Management plays a significant role in the development of an organization.] A **business process** refers to a wide range of structured, often chained, activities or tasks conducted by people or equipment to produce a specific service or product for a particular user or consumer. Business processes are implemented to accomplish a predetermined organizational goal. Business processes occur at all organizational levels; some are visible to customers, while others are not. The three types of business processes are: - **Management Processes**: The processes that govern the operation of a system. - **Operational Processes**: The processes that constitute the core business of the organization and create the primary value stream. - **Supporting Processes**: The processes that support the core processes. Examples include accounting and technical support. **Transaction Processing Systems (TPS)** The purpose of developing and using TPS is to - automate the data collection process (e.g. Bank ATM) - to improve the accuracy and reliability of the transaction data for a business - management of an organisation can easily get an accurate, timely picture of the business operation at a specific time. **Data processing activities:** - Data collection: capture and gather all the data necessary to complete a business transaction. - Data editing: validate the data captured for consistency, completeness and accuracy. - Data correction: re-enter or correct incorrectly keyed data. - Data manipulation: perform necessary calculations and other data transformation related to business transactions. - Data storage: update database(s) within the organisation with new business transactions. - Document or information production: generate output records and reports according to the requirements of management either in hardcopy paper format or in softcopy screen output [Problem-solving is a critical activity for any business organisation.] To effectively solve a problem, a four-phase process (Simon, 1960) is usually involved: - **Intelligence**: identify and define the potential problems and/or opportunities. - **Design**: develop alternative solutions to the problem identified. - **Choice**: select the optimal or good-enough solution with respect to the specific criteria of the decision-making situation. - **Implementation**: take action to put a solution into effect.  **Why use business analytics tools?** Netflix has changed the movie rental industry using business analytics! Netflix collects extensive data using surveys, web site user testing, and brand-awareness studies. When a new customer connects they are prompted to enter genre preferences and rate a selection of movies. The Netflix movie inventory is tagged with various attributes. Customer ratings are compared with these attributes and are also matched to other people who have similar viewing histories. The result -- Netflix can predict movies that each customer is likely to enjoy, and so can create individual customer recommendations. Netflix knows what movies you will like before you and your friends do! **Rich picture** To appreciate a business context or situation, business systems researchers at Lancaster University (UK) have developed an approach to business analysis called the soft systems methodology (SSM). Soft systems thinking and tools can be used to [describe situations perceived to exhibit conflict, uncertainty or unease in relationships between people]. In the first stage of SSM, the client or owner describes the problem situation as they see it. This is one viewpoint. Ideally, however, at this stage viewpoints should be collected from all (or at least representative) stakeholders (i.e. anyone interested or with a vested interest in the situation being resolved and who is involved in the situation under investigation). In this way a business analyst can build up a comprehensive picture of the situation, upon which options for action can be proposed. Visualisation is a key aspect of SSM. Once the necessary data and information (both hard and soft data and information) have been collected from stakeholders, a cartoon or rich picture is drawn representing the problem situation, the structures, processes, situation, relationships and issues. Drawing the rich picture is one of the most enjoyable aspects of soft systems work. All stakeholders can participate in the drawing of a rich picture (or possibly multiple rich pictures). The drawing doesn't need to be a work of art; hand-drawn cartoons using stick figures are sufficient. The rich picture diagram should, however, be self-explanatory and easy to understand, as during the rich picture drawing process, hidden assumptions, conflicts, issues, concerns and worries may come to the surface. Having an awareness of other people's perspectives is a step closer to having a shared understanding. **A guidelines for drawing rich pictures:** 1. Start by drawing a large cloud or bubble to represent the boundary of the problem situation. 2. Add in the people (stick-figures are OK) who are concerned about the situation. 3. Add in the people who operate or control the situation. 4. Add people, organisations, governments, departments, unions---anything that affects the situation from outside. Label the pictures. Show external observers, for example an analyst who has been called in, or the government that sets constraints on the organisation's operations. 5. Make important things BIG. 6. Show relationships between people with lines and arrows. Label relationships with a diagram or words. Conflict relationships are often shown with crossed swords or a cross; loving or close relationships with a heart. 7. Use thought or talking bubbles to show people's views, worries, concerns. 8. Use any symbols you like as long as they are clear and understandable. Some useful symbols include ears (listening), documents, lorries (pickup/delivery), houses, factories, cloud 9 (unrealistic expectations), 1-tonne weight (under pressure), brick wall, black cloud, question mark (uncertainty), exclamation sign (source of wonderment), two hands shaking (friendship), light bulb (bright idea), forbidden sign, axe (sackings), lifesaver ring (help), rope knot (knotty problem). Then, review your diagram for the following: 1. Does it show all the relevant organisational structures that support the situation? Include structures that tend to change slowly over time and are relatively stable---for example, departments, management reporting lines and physical locations. 2. Does it show the primary tasks that the organisation was originally created to perform, and must do to survive? For example, the primary task of a car manufacturer is to make cars. In order to survive, the car manufacturer needs to stay competitive, improve relationships with distributors and customers, improve business operations, and maintain a quality image. Sometimes it is difficult to identify the primary task. For example, is the primary task of the police force to protect the community or to catch criminals? This review process provides the opportunity to look at the situation from a different perspective. 3. Does it show all the processes or activities involved in the situation? In order to carry out the car manufacturer's primary task of making cars, certain activities or subsystems may be required, such as production, sales, stock control and finance. 4. Have you illustrated their issues, worries, concerns and complaints? 5. Does the rich picture represent the climate of the situation? **Business analytic tools for "soft problems:"** Rich picture  **What is a Human Activity Model?** Following the development of a Rich Picture to capture the problem situation in a business, business analysts will often develop a model of the activities that must logically take place in that business to achieve the desired transformation of the inputs to the business into the required outputs. These are termed "Human Activity Models". Such models help the analyst to understand what activities they might expect to see being performed in the businesses they are studying. If such activities are not taking place, or they are being performed only partially, or in entirety but poorly, then the analyst can investigate further and possibly formulate recommendations to improve business performance. These Human Activity Models take the form of [bubble diagrams], in which descriptions of required activities (expressed using a verb followed by a noun) are enclosed in bubbles, and the bubbles are linked to each other by arrows. **How is a Human Activity Model prepared?** 1. To prepare a Human Activity Model the business analyst usually starts with a short statement (termed a Root Definition), to name the human activity that the human activity system model will describe, and to make clear the transformation of input to output that the activities are intended to achieve. For example, the system model shown in Figure 1 would have been developed based upon a Root Definition such as: A system to prepare boiled potatoes: A system to obtain unwashed potatoes, water, cooking utensils and a source of heat, and to prepare the potatoes for consumption, by washing the potatoes and cooking them in boiling water. 2. Identify the verbs in the root definition, or implied by the wording of the root definition (e.g. obtain, wash, cook). These will be the basis of the activity bubbles (e.g. Obtain potatoes; Obtain water; Wash potatoes; \...) 3. Establish the logical dependency of the activities identified. The arrows in a human activity model represent logical dependency (i.e. the activity at the head of each arrow is logically dependent on the activities at the tail of each arrow being completed). For example, in the human activity model of "a system to prepare boiled potatoes" shown in Figure 1, the activity \"wash potatoes\" is logically dependent on the activities \"obtain potatoes\" and \"obtain water\" being performed first. 4. Assemble the activity bubbles identified at Step 2, and order them with arrows demonstrating logical dependency, as established at Step 3. Represent them in diagrammatic form as shown in Figure 1. 4. Ensure that the required input(s) to the human activities (e.g. Unwashed potatoes; Water; \...) and the final output(s) (e.g. Cooked potatoes suitable for consumption) are included in the completed model **Types of business analytic tools for "hard problems":** 1. Reporting tools (Excel -- predictive and prescriptive model) 2. Data mining tools 3. Knowledge management tools  **Knowledge management tools:** These are computer systems that are used to store employee knowledge, with a view to making that knowledge available widely to employees, customers, vendors, auditors etc., so supporting their decision-making. Knowledge management tools differ from reporting and data-mining tools in that the source of their data is [human knowledge], rather than operational facts and figures.  - Some are little more than Frequently Asked Questions lists for call-centre operators to use to answer customer questions. - Some are search engines with powerful indexing algorithms that allow searches to be done on full text documents sitting on company networks. - Some are databases that allow relationships to be built on the fly between documents and other information objects such as webpages. - These allow people to model their understanding of the connections between things so that others can see them. - Some are web portals that allow authorised individuals to add links and other information to the portal that other people may find useful. **[SUMMARY]** - The goals of an organisation provide direction, the technology supports information provision, and people solve the problems. - Introduction to Data, Information, and Knowledge. - Businesses face challenges in decision-making due to unprocessed \"Big Data.\" - Systems thinking is used to describe and visualize complex business situations. - Hard systems thinking tackles well-defined, quantifiable problems. - Soft systems thinking addresses ill-defined, non-quantifiable situations. - Effective decision-makers are crucial for value-added business processes. - Decision-making frameworks involve technology, people, and organizations. - Business managers use systems thinking and business analytics based on the problem type. - Soft problems often involve crises, conflicts, and multiple stakeholders; Rich Pictures are useful here to illustrate a narrative created from a collection of qualitative data. - Hard problems use business analytics tools like reporting, data-mining, and knowledge management tools. **[Module 2]** **Knowledge management** Development of a common language, knowledge sharing, and the identification of experts and gatekeepers are knowledge management processes. Information management and knowledge management are used by people in their everyday lives all the time. The use of analytic models and reporting tools enable operational data to be used as a source of Business intelligence by end-users at all levels of the organisation. **Business analytics forms** 1. Descriptive analytics 2. Predictive analytics 3. Prescriptive analytics There are three forms that the collection, organisation and manipulation of data can take: 1. Descriptive analytics Descriptive analytics is the use of data to understand past and current business performance, to make informed decisions. Descriptive analytics provides insight into the past and answers the question: **"*What has happened?"*** Descriptive analytics is the most commonly used form of analytics, and is the starting point for many businesses. In these approaches businesses seek to categorise, characterise, consolidate and classify data, so converting it to information. Such analysis typically summarises data into [meaningful charts and reports], addressing, for example, budgets, sales, revenue, costs and profits. For example, the Hammer Wines company, an importer and reseller of various drinks, could apply descriptive analytics techniques to their annual sales data to answer questions about: - **the performance of their sales staff** -- For example they could analyse and chart the performance of sales staff against their assigned sales targets and determine how many of their staff are achieving their targets. This might support decisions related to the training needs, and possibly the on-going employment, of sales staff. - **the sales regions that are profitable and those that are not** -- This might identify regions where extra marketing effort is needed, or maybe sales effort in these regions should be abandoned. - **the products that are generating most revenue and those that are selling poorly** -- This might identify products that are not in demand, and so might be the focus of extra sales effort, or maybe cut from the product catalogue. 2. Predictive analytics Predictive analytics is the analysis of past performance in an effort to predict the future by examin­ing historical data, detecting patterns or relationships in the data (i.e. creating a "model"), and then extrapolating these relationships forward in time. Predictive Analytics is being applied to areas as diverse as automotive maintenance and hospitality. Predictive analytics provides insight into the future and answers the question: *"**What do we expect to happen at a future time?"*** 3. Prescriptive analytics (using Excel) Predictive analytics can also answer the question termed "***What if"*** such as: "What will happen if demand falls by 5%?" or "What will happen if our suppliers raise their prices by 10%?". Using various advanced techniques, predictive analytics can sometimes go further than simply extrapolating data into the future. It can detect hidden patterns in large quantities of data and group that data in ways that predict behaviours and uncover trends that are not otherwise apparent. This sort of predictive analysis is built around "predictors" -- [a variable or set of variables that can be measured to calculate the statistical likelihood of future occurrences.] Predictive analytics of this type employs a variety of [statistical techniques, modelling, machine learning and data mining,] which analyse current and historical facts to make predictions about future or unknown events. In business, for example, **predictive models often exploit patterns found in historical, transactional data to identify risks and opportunities**. Bank managers, for example, might predict the chances that a loan applicant will default, or alert their credit card customers to charges lodged against their accounts that are potentially fraudulent. Prescriptive analytics involves **the use of a model where the output of that model can be optimised (maximised) by adjusting the input alternatives**, **so "prescribing" what the best decision is to take now.** Prescriptive analytics has been described as "the third and final phase of business analytics". *Prescriptive analytics attempts, not only to anticipate what will happen and when it will happen, but also to understand [why it will happen]! In so doing, prescriptive analytics can suggest decision options to take advantage of a future opportunity or mitigate a future risk. It will show the implications of each decision option, and so provide a basis for taking the optimum decision now.* Prescriptive analytics can answer the question: **What decision should we take now to maximise the likelihood that we will achieve a desired, specified outcome at a future time?** Prescriptive decision models can be either deterministic or stochastic: - A **deterministic model** is one in which all model input information is known, or assumed to be known with certainty. For example, the Hammer Beverages company might assume that demand for one of its products is known, with certainty, to be 1000 cartons per month. They therefore can take production decisions now, consistent with this known value. - A **stochastic model** is one in which some of the model input information is uncertain. For example, the Hammer Beverages company may have evidence that the demand for one of its products is actually uncertain, with an average of 1000 cartons per month, but that the actual demand is distributed around this average according to a known statistical distribution. Using this information, management could specify the level of risk of being short of stock that they are willing to accept, and then use the model to prescribe the level at which their monthly production should be set now. The **Microsoft Excel** application has some capability to perform Prescriptive Analytics, in particular fitting best fit models to historical data and projecting these models into the future. How can you design and document the processing that a reporting tool such as Excel must carry out to complete such analytics? A first piece of advice -- resist the temptation to "just do it". Time spent designing and documenting the processing to be carried out, before implementing it using a tool such as Excel, will be time well spent. You will ultimately end up completing the implementation faster, and you will produce a better implementation, if you [design before implementing]!  A spreadsheet is simply a computerized version of what accountants have used for years: columns and rows of figures. The advantage of an electronic spreadsheet is the ability to redo the figures quickly and easily. Spreadsheets can also be used to simulate business decisions, for example: if costs increase by 8.5%, this can be input into the spreadsheet to see immediately what effect it has on the bottom line and the manager can consider what business decisions need to be made. Excel will be used as a tool to analyse both the level and the type of sales, to produce reports as required and to aid in the performance management of the sales representatives. It can also be used to assist in strategic planning and decision- making by all the senior management staff. **The major purpose of these Decision Support Systems is the creation of a viable sales and product analysis system.** General guidelines to design a spreadsheet: - Firstly, **resist the temptation to 'just do it'.** - **Identify the output of the project** -- the labels you might want to use. This will help to clarify your objectives. What is it that the finished product should be able to do? - **List the input and calculations** -- What data do you need in order to achieve the above? Clearly state these. Think about the most appropriate functions/formulae that could be used to help with any calculations that are required. - To enable a spreadsheet to be reused over and over again, the design is very important. You don't want to be 'reinventing the wheel' each time. Make sure that the calculations can be dragged down or across to more efficiently utilise your time. - As a general rule, the design should keep the **data separate from the calculations** and the calculations separate from any summaries that are created. This means that you create separate worksheets within the one workbook or file. Each worksheet should have an appropriate label e.g. Wholesale Price List rather than Sheet 1. - Remember data is what goes into a spreadsheet and information is what comes out. **[Module 3]**: Designing Applications to support business analytics applications **Business analytics models** 1. IPO table 2. Flow charts 3. Pseudocode [The Input-Processing-Output] The Input-Processing-Output table The Input-- Processing-- Output (IPO) table records the input, processing, and output of a process, or program module that must be implemented using a business analytics tool (e.g. Excel). IPO chart are important because as business analysts, we need to understand the context, and see context to communicate that with stakeholders. Creating an IPO table can be understood as a three step process: 1. **Identify the desired outputs***:* Decide what you are trying to achieve and what it is that you are trying to calculate. Write down these desired outputs 2. **Identify relevant inputs***:* What data do you need to include so that the output can be determined? Write down these inputs. 3. **Identify the processing that must take place** (e.g. calculations): to convert the relevant inputs to the required outputs. Write down the processing instructions (for turning the inputs into outputs). Checking the IPO table Once the IPO table has been prepared there are two formal checks that should be applied: 1. Check that the statements in the "Processing" column are expressed using English language statements. Do not use the names of functions that are features of the reporting tool (e.g. Excel) that is to be used to carry out the processing. 2. When expressing the processing, make explicit references to the inputs listed in the "Input" column, and the outputs generated as listed in the "Output" column. The statements in the "Processing" column must explicitly connect the inputs and the outputs.  [Flow chart] A flow chart is a visualisation of a process, in sequential order, often used to develop an understanding of how a task is to be undertaken in an organization. The flow chart employs [four geometric symbols], representing activities in the process, with arrows connecting them to indicate the sequence. By following a flow chart, one can understand the sequence of processes that must take place to reach/generate a desired outcome/output. 1. Elongated circles that represents the start or end of a process 2. Rectangles which present instructions or actions 3. Diamonds which indicate where a decision must be taken 4. Parallelograms which represent inputs and outputs (for example, people, Example Task resources, reports,...) The creation of a flow chart to represent the processing that must take place in an Excel spreadsheet to support business analytics is similar to the steps followed when creating an IPO table. One must identify, in turn, the outputs, inputs, and the calculations that take place to transform the data into information. However, unlike the IPO table (which records these as entries in a Example table), a flow chart provides a more visual Input and Output representation (using the symbols described above, connected by arrows) of the sequence of processing that takes place to generate the required outputs. [Pseudocode] Pseudocode is a planning tool widely used when computer programming, which expresses the algorithm that is to be programmed in natural language, rather than using a programming language. Each line of Pseudocode **describes a single action to be taken to achieve the final desired output.** As such, the use of Pseudocode also requires that the designer begins by identifying the input, output and necessary calculations (as for the process of creating an IPO table). **The Future of Business Analytics** A case has been made that using analytics to uncover meaningful insights from data is crucial for fact-based decision making. It requires people with a capacity for creative flair to expose the insights that can be hidden in business data sets. [The management of data, including the analytics used to transform it into information, is an evolutionary process, and organisations are at various levels of this evolution.] To advance, business must understand their analytics capacity from both an IT and a business perspective. The following trends have been observed: - **Data Democratisation** -- Self-service analytics tools have changed people's expectations. "People will seek empowerment across the data continuum, especially as more millennials enter the workforce. For business users to stay iterative, they must be able to shape certain data on the fly. That's why the [demand for self-service data preparation tools and even self-service data warehouses] will grow as a natural extension of self-service analytics". - **Agile Analytics** -- There is an increasing demand for "agile analytics". [Companies want to get the right data to the right people, and quickly.] "This is no small challenge, because that data lives in many different places.... With the rise of sophisticated tools and the addition of new data sources, companies will stop trying to gather every byte of data in the same place. Data explorers will connect to each data set where it lives and combine, blend, or join with more agile tools and methods". - **Cloud Data and Cloud Analytics** -- Organisations are increasingly [organising their data storage on the cloud]. "They realise putting data in the cloud is [easy] and [highly scalable]. They also see that cloud analytics allows them to be agile.... Early adopters are already learning from this data, and others are realising they should. And more companies will use cloud analytics to [analyse more data faster]. They'll come to rely on it just like any other critical enterprise system". - **Visual Analytics** -- Data is now dominating business decision making, in the boardroom, in the media, and in social media. "People are visualising their data to explore questions, uncover insights, and share stories with both data experts and non-experts alike. As data usage grows, even more people will turn to data with both professional and personal questions. And employers will look for candidates who can think critically with data. Visual analytics will serve as the common language, empowering people to reach insights quickly, collaborate meaningfully, and build a community around data". **[SUMMARY:]** The vast amount of information processed today requires that everyone, especially those involved in implementing information systems, have a strong understanding of information and knowledge management principles. This understanding helps in making informed decisions and effectively managing business operations. Business analytics is the use of data, information technology, statistical techniques, and quantitative models, to help managers build their understanding of business operations, so supporting them to take improved, informed decisions. Forms of Business Analytics 1. Descriptive Analytics: Analyzing past data to understand what has happened. "What has happened?" 2. ⁠Predictive Analytics: Utilizing data and statistical techniques to forecast future outcomes. "What do we expect to happen at a future time?" 3. Prescriptive Analytics: Determining the best course of action to achieve a desired outcome. "What decision should we take now to maximize the likelihood of achieving a specified outcome in the future?" Designing and Documenting Business Analytics Tasks--- Tools and Approaches: - IPO Table (Input-Process-Output): Outlines the steps required in business analytics tasks. - Flow Chart: Visual representation of the process steps. - Pseudocode: Writing out the logic of tasks in a simplified, human-readable format. Key Trends in Business Analytics 1. Data Democratization: Making data accessible to all members within an organization to enhance decision-making at all levels. 2. ⁠Agile Analytics: Applying agile methodologies to analytics projects for flexibility and rapid development. 3. ⁠Cloud Data and Cloud Analytics: Leveraging cloud technology for storing data and performing analytics, improving scalability and access. 4. ⁠Visual Analytics: Using visual tools and representations to make data more understandable and actionable **Data visualisation** Data visualisation helps people understand data---both small and big. To turn data into information requires recongising and incorporating context to a story; data visualiation can be the key to achieve this. Data visualization tools are designed to be used by everyone in the organization, not just analysts and the capacity to build insight becomes available to all. **Data visualisation** is the visual representation of data, to facilitate the exploration of that data, to generate insights that can increase understanding, and inform decision making. Two observations motivate the application of data visualisation to the data sets typically collected by business: 1. When you are lost in data, a map is useful! By visualising data (providing a context for the data and so converting it to information) you have converted thousands, maybe millions or billions, of numbers into an image that you can explore with your eyes. You create an information map! 2. To appreciate what the data has to tell you, you must understand that the whole is greater than the sum of the parts. Data visualisation allows you to identify patterns and trends. Instead of just summarising data (for example by generating totals, averages and standard deviations), data visualisation allows you to see connections that link all parts of a data set. **What**: Data visualisation is not a single tool but rather [a set of techniques which\ can make use of various tools and services]. It involves displaying data sets in visual, both static and including video, formats. **Why**: The aim is to [reveal patterns by presenting the data in particular ways] ( eg,\ by context or business). It underpins the growing use of [infographics], especially in\ the media, to communicate key information quickly and in an easily comprehensible\ format. **How:** While creating data visualisations traditionally required sophisticated\ technical skills, tools (including Excel) are now appearing which make it possible for\ non-specialists to create these kinds of visualisations. **A Brief History of Data Visualisation** Humans have a deep-rooted ability to process visual information, a skill that has evolved over centuries. The history of data visualization can be traced back to scientific advancements from the 15th century, where intricate visual strategies were developed to communicate data. These strategies have built upon basic cognitive skills and scientific discoveries. **Data visualisation guidelines** 1. You must **understand the data you are trying to visualise**, in particular its volume (i.e. the quantity of data that must be visualised) and content (i.e. the strict definitions of each data item that appears in the data set that is to be visualised). 2. **Determine the message you want to communicate**. You must start with the questions that you are seeking to answer using the visualisation -- in particular questions that are meaningful to the relevant business. 3. **Know your audience**. You must know who the audience for your visualisation will be (accountants, marketers, organisational management, production engineers, sales staff \...). What form of visualisation will be accessible and meaningful to that audience? 4. Then, drawing from the visualisation options that satisfy points 1 to 3, choose the visualisation form that **conveys the information in the clearest and simplest way.** **Static visualisatons** 1. Infographics 2. Narrative 2.0 -- visualises music 3. Last.Forward -- visualises a user's social network 4. Voyant -- web-based text analysis and visualisation tool in the form of word clouds, display trends and word frequency tables **Video visualisations** 1. Multimedia; YouTube videos 2. Aircraft movement **Infographics** Data visualization is often associated with infographics, which are graphic visual representations of data designed to present information quickly and clearly. Infographics enhance understanding by helping the human visual system detect patterns and trends. Infographics have evolved from being a mode of mass communication to being tailored for specific audiences, such as business specialists. This chapter focuses on data visualization for business use rather than general consumption. **Types of quantitative message** 1. **Nominal comparison***:* Comparing categorical subdivisions in no particular order, such as the total salary bill by department. A bar chart may be used for this comparison 2. **Time-series***:* A single variable is captured over a period of time (eg, monthly sales over a 3 month period). A line chart may be used to display the trend, and potentially to extrapolate it. 3. **Ranking***:* Categorised data are ranked in ascending or descending order, such as a ranking of sales performance (the measure) by sales staff (the category, with each sales person a categorical sub-division) during a single period. A column chart may be used to show the comparison across the sales staff. 4. **Part-to-whole***:* Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A pie chart can show the comparison of ratios, such as the sales percentage market share generated in different sales regions. 5. **Deviation**: Catergorical subdivisions compared against a reference, such as a comparsion of achieved sales vs target sales for sales representatives. A column/bar chart can show comparison of the actual versus the reference amount. 6. **Frequency distribution**: Shows the number of observations of a particular variable for given interval. It could show the number of orders placed by order size. A histogram, a type of column chart, may be used for this analysis. 7. **Correlation**: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A scatter plot is typically used for this message. **Examples of data visualisation using Excel spreadsheets** 1. Charts (pie, column, bar, line, X-Y scatter plot, area, bubble, combination, radar, stock and surface charts) 2. Sparklines 3. Conditional formatting 4. Pivot tables and pivot charts   **Data Visualisation -- The Future** - Simon (2014) suggests that successful future organizations will not just capture \'big data\' but will also establish frameworks to understand it. - Data visualization is seen as crucial for extracting insights from big data, leading to the concept of the \"Visual Organization.\" - Advanced organizations are increasingly using a variety of data visualization tools to enhance business decisions. - The thesis highlights that as data visualization tools evolve, employees interact with data more deeply, moving beyond basic tools like Excel. - The rise of Big Data and the growing awareness of its potential has led to a new, data-oriented mindset in the business world. **Data Visualisation Skill Sets** - Data visualization is no longer limited to IT professionals; non-technical professionals must now engage with and understand Big Data. - Emerging tools like Datawrapper, Visual.ly, Tableau Software, D3.js, and R allow for easier and more powerful data analysis and visualization. - The concept of \"hybrid employees\" is introduced, referring to professionals who can turn data into actionable insights through advanced visualization tools. **[SUMMARY]** **Importance and Role of Data Visualization** - **Understanding Data**: Data visualization helps in turning data into information by providing context, enabling people to understand both small and big data. - **Organizational Accessibility**: Modern data visualization tools are designed for use across the organization, allowing everyone, not just analysts, to build insights. - **Purpose**: The goal is to facilitate exploration, generate insights, enhance understanding, and inform decision-making. **Key Concepts of Data Visualization** - **Mapping Data**: Visualizing data creates an \"information map,\" converting large quantities of data into an image that can be explored visually. - **Seeing the Whole Picture**: Visualization helps in identifying patterns and trends, revealing connections in data sets beyond mere summaries. **Techniques and Tools** - **Not a Single Tool**: Data visualization encompasses various techniques, including static and video formats, to reveal patterns and present data effectively. - **Infographics**: Infographics are a key form of data visualization, helping the visual system detect patterns and trends quickly. - **Quantitative Messages**: Different types of charts (bar, line, pie, scatter, etc.) are used to convey various quantitative messages, such as comparisons, trends, rankings, part-to-whole ratios, deviations, and correlations. **Guidelines for Effective Data Visualization** - **Understand the Data**: Know the volume and content of the data you're visualizing. - **Define the Message**: Start with the key questions or insights you want to convey. - **Know Your Audience**: Tailor the visualization to the audience's needs and understanding. - **Choose the Right Form**: Select the visual form that best conveys the information simply and clearly. **Static and Video Visualizations** - **Examples of Static Visualizations**: Infographics, narrative visualizations, social network visualizations, text analysis tools. - **Examples of Video Visualizations**: Multimedia content like YouTube videos, visualizations of aircraft movements. **Excel in Data Visualization** - **Excel Tools**: Includes pie charts, bar/column charts, line charts, scatter plots, sparklines, conditional formatting, pivot tables, and pivot charts. **The Future of Data Visualization** - **Big Data Integration**: Future organizations will establish frameworks to understand big data through visualization, becoming \"Visual Organizations.\" - **Evolution of Tools**: Advanced tools beyond Excel are enabling deeper interaction with data. - **Rise of Data-Oriented Mindset**: There is a growing awareness of big data\'s potential in the business world, leading to new approaches to data analysis. **Data Visualization Skill Sets** - **Beyond IT Professionals**: Non-technical professionals are increasingly required to understand and engage with big data through visualization. - **Hybrid Employees**: Professionals with the ability to turn data into actionable insights using advanced visualization tools are becoming essential. **[Module 4: Business Communication]** - Recognise the [different information needs of management levels] within an organisation - Understand the importance of teamwork to business performance - Comprehend the basic terminology and concepts relating to business communication - Apply the plan, write, review and send writing process to written communications It is essential therefore that an organisation must appreciate the organisational culture, problems, opportunities, constraints, requirements and strategic objectives of an organisation, before specifying a business information system, possibly implemented using computer technology. **Organisational Communications Software Applications** - Products introduced into an organisation affect the strategic planning, core business functions, workflow and people throughout. - Cultural shifts, customisation of the technology, and adaptation of workflow and work practice are all required. - The evidenced capacity of end-users to customise software applications, create workflows and adopt new technologies as they arise has transformed business expectations of workers and service. - The need for custom-written - software has decreased as the volume of pre-packaged applications and the processing power of PCs has increased. - Some applications, such as word processors, have features that make them useful to all knowledge workers at an individual level. - Organisations usually provide access to a cloud-based application that all workers can use, irrespective of the device they possess. - Other applications are designed with features that assist individuals or groups across all subsystems. **Management levels and information needs**  - Each level of management in an organisation has different information needs. - Top-level managers need information that is highly summarised to reveal the overall condition of the business. - Middle-level managers need summarised information: weekly or monthly reports. - Supervisors need detailed, very current, day-to-day information on their units so that they can keep operations running smoothly. **Human factors** The implementation of information systems in organisations necessarily requires people to change the way they complete their work tasks. Various teams formed to support agile projects delivering innovations in a short amount of time and functional areas across the organisation require simple communication tools. Teams: Well-designed teams are said to offer a platform for promoting creativity, motivating extraordinary performance, and enabling fast, flexible response to customer needs. There are many kinds of teams: 1. 2. quality teams 3. project teams 4. self-managing teams 5. semi-autonomous teams 6. learning teams 7. virtual teams 8. global teams **Computer technologies to support teamwork** **Business communications forms:** Business intelligence activities are based on effectively sharing/communicating the outcomes of business analytics 1. Non-verbal - Visual cues between people: visuals, noise, smell, taste and feeling - Most social psychologists believe that nonverbal communication makes up about two-thirds of all communication between two people or between one speaker and a group of listeners. - Kinesics: study of how people communicate through movement and body positions  2. Verbal - Using your voice to communicate with others - Effective verbal communication builds relationships with to clients, customers, the media, etc. - Discussions - Presentations Some conversation pointers: - Business introduction - include your company name plus first and last name. - Introduce yourself to those sitting next to you. If possible, meet everyone at your table and sit next to someone you don\'t know. - Introduce your guest or another person at a function, using first and last names and perhaps an interesting item of information about that person. - Before going to an event, business or social, be prepared to discuss items of current interest including books, films, television shows, or current events. - Get to know others first. Build relationships and trust first. - Beware of being a pushy promoter. - Listen attentively - 80 percent listening and 20 talking. - Use open-ended questions - Who, What, When, Where and Why. 3. Written a. Letters, emails, memos, manuals and reports are forms of written communication. The 3 key principles: purpose, audience centered and concise Advantages of written communication: b. Written communication helps in laying down organisational policies and rules. c. Permanent - useful where record maintenance is required. d. It assists in proper delegation of responsibilities. It is impossible to fix and delegate responsibilities on the grounds of speech alone as understandings can be taken back by the speaker, or the listener may refuse to acknowledge/agree about exactly what was said. e. Written communication is more precise and explicit. f. Effective written communication develops and enhances an organisation's image. Disadvantages of written communication: - Written communication does not save upon the costs. The costs can be huge, in terms of stationery and the manpower employed in writing/typing and delivering letters. - Also, if the receivers of the written message are separated by distance and if they need to clear their doubts, the response is not spontaneous. - Written communication is time-consuming as the feedback is not immediate. The encoding and sending of a message takes time. - Effective written communication requires great skills and competencies in language and vocabulary use. Poor writing skills and quality have a negative impact on an organisation's reputation. **Document Templates** - Templates, are essentially pre-constructed documents in which users can input their own information in lieu of repeatedly designing the document themselves. - Convenient and time-saving. - Microsoft Word is pre-loaded with templates: resumes, business brochures, business reports, cards etc. **Cloud-based Document Creation, Sharing and Storage** Google Drive: a cloud based storage system that allows users to store documents, photos, videos and other files online. Google Docs: is a free web-based application that allows you to create, share and manage documents online. **Business Reports** A means of conveying data, information and knowledge gathered as a result of business analytics/intelligence activities. 3 key principles: purposeful, audience-centered, concise. Business reports proces: 1) Planning 2) Writing 3) Review 4) Print, and send (completing) - Introduction: provide the reader with relevant background information and ensure the purpose of the report is clearly explained - Body This section contains the findings of any research as well as the interpretation of the findings. Use headings to signpost - Conclusions :A concluding statement regarding the facts or findings presented, based on the evidence presented. - Recommendations: Detail appropriate courses of action evaluated against the report's original purpose. They must be based on the evidence presented in the body of the report **[SUMMARY]** - Massive technological changes in the workplace have altered how end-users communicate. - Mobile devices and cloud-based technologies enable communication anytime, anywhere, including in-transit. - Emerging technologies and information management tools are crucial for improving individual and overall work performance. - Adoption of information technologies, particularly through networks and web-mediated information dispersal, has significantly impacted communication. - The speed of technological application in business has shifted behavioral expectations. - Innovation requires end-users to work cooperatively in teams, applying project management processes for planning, directing, and monitoring tasks to meet business goals. - Communication and Information Technology (CIT) supports teams working in geographically isolated situations. - During the planning phase, human factors must be considered to ensure successful change once a system is implemented. **[Ecommerce]** **eBusiness**, or **electronic business**, is the term used to describe any business transaction or activity that uses networks, including the internet. **eCommerce** is defined as a subset of eBusiness. eCommerce includes "all electronically mediated data/information exchanges between an organisation and its external stakeholders" (Chaffey, 2004). This new way of doing business has a number of advantages: - increased profitability due to a broader market base and resulting improvements in sales volumes - improved customer service improved delivery times for products and services. **eCommerce Technologies** - [Intranets] facilitate communication within organisations - The networks can support inventory control, finance, and general best-practice business processes that will improve the enterprise's performance - [Extranets] enable business-to-business transaction processing between suppliers, distributors, resellers, contractors and consultants - [Internet] which can support both the business-to-consumer and business-to business relation- ships that exist for the sale of products, information or services  **Automatic Identification and Data Capture** - - Bar codes - Radio Frequency Identification (RFID) - Biometrics - Magnetic stripes - Optical Character Recognition (OCR) - Smart cards - Voice recognition **eCommerce business models** [Business-to-consumer (B2C)] eCommerce, where customers deal directly with an organisation, without the involvement of any intermediaries. For example, this would include booking airline tickets directly using the internet, or purchasing a book on Amazon.com - The elimination of intermediate organisations between the producer and the consumer is termed disintermediation. - Amongst many possible models, B2C eCommerce includes: - Portals -- Gateways to the Internet offering searching tools for products/services (e.g. MSN.com). - eTailers/Storefronts -- Online version of a 24/7 retail store (e.g. amazon.com). - Content Providers -- Information and entertainment providers like newspapers (e.g. CNN.com). - Transaction Brokers -- Process online sales transactions (e.g. travel agents, stock brokers). - Service Providers -- Companies that sell users a service online rather than a product (e.g. consultancy, expertise).  [Consumer-to-consumer (C2C)] eCommerce, which involves consumers selling directly to each other. eBay is an example of a C2C site, where customers can buy and sell directly to each other using the capabilities of the site C2C eCommerce models include: - Market Creators -- Web-based businesses that use the Internet to create markets that bring buyers and sellers together (e.g. eBay.com). - Community Providers -- Sites where individuals with common experience can meet and compare (e.g. About.com). [Business-to-business (B2B)] eCommerce, where the participants in the sales transactions are both businesses The B2B ECommerce Trends - - Multichannel - Omnichannel - Video Marketing - Switching to open-source - Focus on global markets - Fast implementation & integration - Flexible payment options - AI for content marketing - User experience and mobile - Personalization **Supply Chain** - The flow of materials, information and services from raw material suppliers through factories and warehouses to the end customers - A supply chain includes the organisations and processes that create and deliver these products, information and services to the end customers. - It includes many activities such as purchasing, materials handling, production planning and control, logistics and warehousing, inventory control[, distribution and delivery.] [Simple Linear Supply Chain] - Upstream supply chain - Internal supply chain - Downstream supply chain [Just-in-time (JIT) inventory management] **Other B2B eCommerce business models** - [B2B electronic mall model]: where virtual storefronts or eShops are provided on a single website, or are linked to/from a central website - [B2B virtual community:] where many business owners or employees in a related industry sector discuss issues, participate in online forums, and so build a central repository of knowledge - [Value-chain service provider model:] where organisations provide specialised supply chain services such as electronic payments and logistics - [Internet marketplace model:] which is a site where multiple business buyers and business sellers can trade, by making bids and offering goods through the conduct of online auctions. **eSourcing and eProcuring** - eSourcing - to find suppliers of resources they need using the internet - eProcuring - to use the internet to facilitate the purchasing of those goods **Network performance analysis** The performance of eCommerce activities can be assessed by both technology and business outcomes. From a technology perspective, web applications must address issues such as network, hardware, and software scalability, availability, reliability, responsiveness, cost, and security, given the complexity of connecting globally independent sites. **eCommerce performance analysis** For business performance, the assessment approach will vary based on the business model. **[SUMMARY:]** eCommerce involves all electronically mediated exchanges of data and information between an organization and its external stakeholders, including buying from suppliers and selling to customers. Its advantages include increased profitability, improved customer service, and faster delivery times due to a broader market reach. This chapter categorized eCommerce into three types: Business-to-Consumer (B2C), Consumer-to-Consumer (C2C), and Business-to-Business (B2B). It discussed business models for each type, exploring them as examples of business solution designs. The chapter also covered the technologies enabling these models and the specialist roles required to implement various eCommerce solutions. **[Human-Computer Interaction]** Human-computer interaction is a discipline concerned with the design, evaluation and implementation of interactive computing systems for human use and with the study of the major phenomena surrounding them. A diagram of a company\'s company Description automatically generated with medium confidence Why study usability? - We want to support and enhance human activity, so we should design with the users in mind - Technology is not useful unless it is usable! - Usable systems lead to increased productivity and greater satisfaction. [Measurement for usability, Usability attributes] These attributes can be used to define measures of a system's usability and to measure the success of a system interface. 1. Learnability: How fast can a user learn to use an interface sufficiently well to accomplish basic tasks? - "Easy to learn" refers to a novice user's experience - Measurement of this attribute involves measuring the increased proficiency reached by users completing tasks after a specified period of training 2. Memorability: If a user has used the system at some earlier date, can he or she remember enough to use it effectively next time? - The system (metaphor) should be easy to remember - Casual users can return to the system after some period of time without much relearning! - Measurement can be done on users after a period of no use, or a memory test can be conducted after a test session - Built in methods of reducing the need to remember e.g. email addresses 3. Efficiency of use: : Once an experienced user has learned to use the system, how fast can he or she accomplish tasks? - High level of productivity is possible - Once a user reaches expert status, one can measure how much faster that user can complete a task, compared with the time they took before they were provided with the syste 4. Error-related factors: How often do users make errors while using the system, how serious are these errors, and how easy is it to recover from a user error? - An error is any action which inhibits the accomplishing of a desired goal - One can measure the error rate -- the number of errors that occur while performing a specified task - Catastrophic errors should never occur! 5. Subject satisfaction: How much does the user like using the system - Systems should be pleasant to use - Entertainment value can be more important than the speed with which things get done - This is measured by asking users for their subjective opinions of the system after they have experienced its use **Interface design principles (Nielsen's ten heuristics)**  1. use simple and natural dialogue - Use the user's conceptual model - Match the users' task in as natural a way as possible -- minimise mapping 2. be consistent - Consistency of effects: Same words, commands, cause the system to perform the same actions in similar situations (predictability) - Consistency of language and graphics: - Same visual appearance across system 3. speak the user's language - Use terminology based on the users' language - Use meaningful mnemonics, icons, and abbreviations - Cater for multiple interaction styles (eg File/Save -- Save Icon, Menu, Ctrl +S) 4. minimise users' memory load - Promote recognition over recall - Affordance: An aspect of an object which makes it obvious how the object is to be used. - Icons: Symbolise computer actions - Shapes should be recognisable - Employ standard icons - Useful if meaningful - Labels or ToolTips to clarify meaning - Menus provide a variety of prompts to help the user - Shortcut Keys - Toolbar Icons - Ellipses \... - Sub-menus - Buttons & Checkmarks 5. provide visual and audible feedback - Continuously inform the user about: - What's being done - How the user's input is interpreted - User must be aware of what's going on 6. provide clearly marked exits - Users don't like to feel trapped by the computer! - Strategies - Cancel button - Universal UNDO - Interrupt - Quit - Defaults 7. provide shortcuts - Experienced users should be able to perform frequently used operations quickly - Keyboard and mouse accelerators - Type-ahead - Navigation - jumps - Shortcut methods - (toolbars/F\# keys) - History - systems 8. deal with errors positively - People will make errors! "To err is human" - Errors we make: - Slips -- unconscious behaviour - Mistakes -- conscious - Avoiding errors: - Forcing functions: prevent wrongful action - Gag: prevent continuing - Warn: audible bell or alert box - Responses for errors: - Do nothing - Self-correct - Lets talk about it! - Teach me - Provide meaningful error messages - Prevent errors: Provide reasonable cheks on input data 9. provide help - Tutorial and/or getting started manuals - Short guides - Online "tours", and demos - Reference manuals - Reminders (reference card/keyboard template/tooltips) - Context-sensitive help - Wizards (walk through typical tasks) - Tips 10. Form/screen/control design - Easiest to read dark text on a light background - Use Colour carefully - Attention or status - Flashing fields - Change of colour - Reverse images (most noticeable) - Fonts -- no more than 3 - Information density -- overall should not be more than 25% - Association by position: Objects that appear to be placed together are assumed to be related - Zoning - group like things together (eg. buttons) - colour - position ("frame"ing) **Heuristics** are strategies using readily accessible, though loosely applicable, information to control problem solving. Heuristics can be **mental shortcuts** that ease the cognitive load of making a decision. A rule of thumb or an educated guess. Interface Design Heuristics - usability statements that guide a developer's design efforts Derived by evaluating common design problems across many systems Interaction styles - - Batch - Question and answer - Command language - Form fill-in - Menu - Direct manipulation - Non-command - Natural languag **[User analysis]** Users affected by the system: - Primary users - Secondary users - User communities - Users as buyers - Surrogate users: supervisors or managers Task analysis: When examining goals and related tasks, we are doing a task analysis. All the various parts of the system -- software, hardware, interface and documentation -- help users to perform tasks and therefore need consideration. - Workflow analysis - Job analysis - Task lists and task inventories - Process analysis, task sequences - Task hierarchies - Procedural analysis [Usability evaluation techniques] 1. Cognitive walkthrough In a cognitive walkthrough, one or more evaluators **inspects a user interface by performing a set of tasks and evaluating it for usability problems.** The user interface can often be presented as a paper mock-up, a storyboard or a working prototype, or it could even be a fully developed interface. This technique is best used in the design stage of development, but it can also be applied throughout the life-cycle process. [Requirements before a walkthrough] - Identify users of the system, including specific **background experience** or **technical knowledge** that could influence users as they attempt to deal with a new interface. The users' knowledge of the task and of the interface should both be considered. - Analyse the tasks to be tested, limited to a representative collection of the set of tasks. - Plan the steps involved in performing each of the tasks. - Define the interface by describing the various prompts and labels, and the various actions and information provided. Analyse each action required to accomplish the tasks, as well as the reaction of the interface to performing each of these actions. If the interface has been implemented, all information is available from the implementation. The evaluation can be performed on a paper mock-up, but may require additional description to provide the appropriate feedback as actions are performed. Performing the walkthrough The analysis phase consists of examining each action in the solution path and attempting to tell a credible story as to why the expected users would choose that action. Credible stories are based on **assumptions about the user's background knowledge and goals**, and on an understanding of the problem-­ solving process that enables a user to guess the correct action. During the walkthrough, the evaluator asks the following four questions for each action: - What will the users do to try to achieve the right effect? - Will the user notice that the correct action is available (visibility)? - Will the user associate the correct action with the effect to be achieved? - If the correct action is performed, will the user see that progress is being made towards solution of the task (feedback)? The evaluator will try to construct a story for each step in the task case. At each step, possible usability problems are identified. 2. Heuristic evaluation In a heuristic evaluation, one or more evaluators inspect the **user interface and interaction with a system, and judge its compliance with usability principles**. The heuristics are a set of guides used in the evaluation. This is a cheap evaluation method that can be performed at any point in a life-cycle process, from paper designs to fully implemented systems. The evaluators used in performing this inspection are usually usability specialists. The best results are attained with four to six evaluators. The heuristics used are a guide to help broaden the aspects of a system that are examined. The evaluators should follow some simple inspection guidelines: - Be familiar with the users. - Be familiar with the tasks to be performed. - Identify usability problems. - Don't explain or debate or redesign, just note and move on. - Focus on heuristics, not on personal taste. How to perform the evaluation 1. **Review** the problem domain, primary users, and tasks performed with the system. 2. Evaluators **review the interface** individually and later, given a period of elapsed time, evaluate it again and finally report any problems to the coordinator. 3. The coordinator **prepares a list** of the problems, removing any duplicates. 4. Evaluators come together as a group and discuss the list of problems, **assign a severity rating** to each, and may suggest a potential design change. 5. The coordinator completes a **heuristic evaluation report** and presents it to the designer for action. [\ ] [\ ] [Heuristics to use during inspection ] **Visibility of system status** The system should always keep users informed about what is going on through appropriate feedback within a reasonable time. **Match between the system and the real world** The system should speak the user's language, with words, phrases and concepts familiar to the user, rather than system-oriented terms. Follow real-world conventions, making information appear in a natural and logical order. **User control and freedom** Users often choose system functions by mistake and will need a clearly marked 'emergency exit' to leave the unwanted state without having to go through an extended dialogue. Support undo and redo. **Consistency and standards** Users shouldn't have to wonder whether different words, situations or actions mean the same thing. Follow platform conventions. **Error prevention** Even better than good error messages is a careful design that prevents a problem from occurring in the first place. **Recognition rather than recall** Make objects, actions and options visible. The user shouldn't have to remember information from one part of the dialogue to another. Instructions for use of the system should be visible or easily retrievable whenever appropriate. **Flexibility and efficiency of use** Accelerators -- unseen by the novice user -- may often speed up the interaction for the expert user such that the system can cater to both inexperienced and experienced users. Allow users to tailor frequent actions. **Aesthetic and minimalist design** Dialogues shouldn't contain information that is irrelevant or rarely needed. Every extra unit of information in a dialogue competes with the relevant units of information and diminishes their relative visibility. **Website colour schemes** Colours are very important as they strongly influence the time that people spend on a site and how they react to different colours and calls to action. For example, it is well known that site visitors tend to read more if they are presented with dark coloured text on a light background. Tools are available to assist web designers to recommend colour schemes that will augment readability. The web site needs to be easy to look at and easy to read. Vision impairments, such as colour blindness, need to be considered at the design stages. **Error recovery** Help users to recognise, diagnose and recover from errors. Error messages should be expressed in plain language (no codes), precisely indicate the problem, and constructively suggest a solution. Help and documentation Even though it is better if the system can be used without documentation, it may be necessary to provide help and documentation. Any such information should be easy to search, be focused on the user's task, list concrete steps to be carried out, and not be too large. [Usability Evaluation Techniques and Technologies: Understanding interaction] Use of heat maps to improve website design **Eye tracking to understand end-user interaction** Eye tracking is the process of measuring either the point of gaze or the motion of an eye relative to the head. An eye tracker is a device for measuring eye positions and eye movement, often used to when observing the response of users to a display (e.g. a website) on a computer screen Figure 9.18 illustrates the pattern of end-user access of click points on a screen. [Comparing websites] Tricot ( 2002) uses Utility, Usability and Acceptability to underpin the creation of a checklist to assess Web Site usability. In this checklist: - Utility is concerned with the assessment of relevance or efficacy. It can be evaluated by tasks like recall and problem solving. - Usability refers to ease of use. It can be evaluated with criteria like learnability, efficiency, memorability, less errors, satisfaction - Acceptance addresses the learners' desire to use the external representation. It assesses the end-­ user's motivation, affect, culture and values. **[SUMMARY]**: Human-- computer interaction and usability are two extremely important components in terms of the practical acceptability of a system. Developers of a system, usually the programmers, don't always consider the interface or interaction aspects with which a user is faced. A developer is usually focused heavily on the utility, or functional and technical aspects of a system. This is the main reason for the large number of usability issues in systems today. A simple set of good design principles has been covered in this chapter. These principles, coupled with knowledge about interaction styles, will provide a good background to help broaden understanding of usability problems. This, in turn, will equip you with knowledge to help you evaluate interfaces and interaction designs of systems. Interface and interaction are still considered at the tail end of a life-cycle process, rather than throughout it. Some simple, cheap usability testing could occur throughout the life cycle, not just at the end. However, if time and money permit, a formal usability test, with actual users participating, would be a more thorough and enlightening test of the system. n this chapter we have discussed usability and the various attributes used to measure it. Two cheap usability inspection methods are also discussed: cognitive walkthrough and heuristic evaluation. A combination of these methods may enhance the discovery of usability problems in a system's interface. **[Managing Data Resources]** Access to stored data enables knowledge workers to copy, save, name, create, move, delete, retrieve, update, display, print, play, execute, import, compress and protect files. The Black Box Database Model argues that the [processing in databases should be hidden from business users. ] - Business users are not concerned with how data is stored and manipulated in a database. - The concern of business users is limited to what goes in (data) and what comes out (information).  [Modern Data Storage ] **Metadata** is a term that is used for information that describes other information. Note that none of this metadata appears on the page when the browser views it normally. It is only visible when you choose to view the source code. **Cloud Web Services** Advancements in communications technologies and the internet have greatly increased access to data within organizations, particularly regarding the volume, velocity, and variety of data. Mobile devices and secure cloud-based storage allow employees at all levels to access and retrieve data, with significantly reduced costs of digital storage. However, this adds complexity to managing and storing digital data, especially concerning the purpose of collecting and sharing information. Applications software tools help users create useful data views, enhancing operational functionality and decision-making, particularly within transaction-processing systems. The effectiveness of these tools depends on technology, staff skills, and the task. The integration of IT into business processes has transformed how information is accessed and used for problem-solving. Globalization, new technologies like the web, and desktop applications have impacted workplace tasks, requiring well-designed information systems. The data produced must be accurate, timely, and relevant, supported by organized storage and aligned business processes. As computing power increases and storage costs decrease, the role of technology in enhancing business efficiency continues to grow.In recent times, computing power and availability of information have increased as the cost of data storage gas decreased. **Databases** A database is a technology that stores data and converts it to information. A database is simply a collection of related data or information organised to assist end-users. Some common examples include: - The 'People' application on your mobile phone (each entry contains a particular name that is related to an address, phone number, fax, email and phone number). - The online ticketing system you use to locate and book tickets to a concert by your favourite band. - The University Library system that you access to locate a text or research paper and arrange to view that material. - The University Student Records system that contains details of each student and their enrolment and their results in the various courses that they study. A database is also an 'electronic cabinet' complete with 'folders' and 'drawers' in which data is stored. Database management systems are commonly used to enable end-users to collect, organise and use related data. Databases Data is meaningless without context. **Leading technologies---[Data warehouses](https://www.oracle.com/au/database/what-is-a-data-warehouse/) & Data marts** A data warehouse is a type of data management system that is designed to enable and support business intelligence (BI) activities, especially analytics. Data warehouses are solely intended to perform queries and analysis and often contain large amounts of historical data. The data within a data warehouse is usually derived from a wide range of sources such as application log files and transaction applications. A data warehouse is a large, sophisticated database containing current and historical data from the company. Low costs but difficult to create, maintain and manage. End result: store large amounts of unused data. - data may originate from a number of information-processing systems within the company, or from the web - useful for managers and knowledge workers - data warehouse could store large amounts of unused data - data warehouse can still prove difficult to create, maintain and manage A typical data warehouse often includes the following elements: - A relational database to store and manage data - An extraction, loading, and transformation (ELT) solution for preparing the data for analysis - Statistical analysis, reporting, and data mining capabilities - Client analysis tools for visualizing and presenting data to business users - Other, more sophisticated analytical applications that generate actionable information by applying data science and artificial intelligence (AI) algorithms, or graph and spatial features that enable more kinds of analysis of data at scale A **data mart** are subsets of data warehouses divided on the basis of geography and frequency of access to stored data. Bottom up approach to data management. End user needs: data stored and data warehouses are functional. - a bottom-up approach to database management, ensuring that the data stored and the data warehouse functionality are closely connected to end-user needs. - Data mining and Online analytical processing - techniques and specialised IT tools to make use of the vast amount of detailed data stored in data warehouses. **Redundant data & data integrity** - Hammer wines case study - each time a new tap installation was added, data items for all of the descriptors (fields) in Table 10.1 would need to be recorded. - Customer details such as contact name and company name would be recorded every time the customer booked a new tap. - This would create duplicate data in many files or redundant data. - Storage of the same data in several locations - wasted storage facilities - increased the probability that the data integrity was questionable. **Data hierarchy** - Field: A field is the smallest element of the data hierarchy that is meaningful to humans. - Record: A record is a logically related collection of data items or fields. - Table: A number of related records that contain the same fields forms a table. E.g. Hammer Wines Customers table containing all the customer records. - Database: A collection of integrated and related files is referred to as a database **Relational Database Approach** - An entity is data an organisation wants to store or display. - Each row or record in a table captures characteristics, or attributes, of the entity. - Relational databases reduce data redundancy - link related files using a common or key field. - The field in each table that is used to uniquely identify each record is called the primary key.  **\ ** **Entity relationship diagrams (ERD)** Entity-relationship diagrams (ERDs) commonly drawn to illustrate the organisation of the storage of data in database management systems. The cardinality of relationships: 1. [One to many (1:M):] In a one-to-many (1:M) cardinality, record in Table A (CUSTOMER) can have many matching records in Table B (VENUE), but a record in Table B (VENUE) has only one matching record in Table A (CUSTOMER). A close up of a word Description automatically generated 2. [Many to many (M:M):] A many-to-many relationship (M:M) is when a record in Table A can have many matching records in Table B, and a record in Table B can have many matching records in Table A. - This type of relationship is implemented in a relational database by defining a third table (called a junction table). - A many-to-many relationship is really two one-to-many relationships with a third table. - E.g a student at a university can be enrolled in many subjects and a subject can have many students enrolled.  3. [One to one (1:1):] A one-to-one relationship (1:1) is when each record in Table A can have only one matching record in Table B, and each record in Table B can have only one matching record in Table A. - This type of relationship isn't common - Most information related in this way would be in one table. - E.g. a student can only have one library card and a library card can only be for one student - A one-to-one relationship could divide a table with many fields. - Reasons include isolating part of a table for security reasons, or to store information in a subset of the main table. A diagram of a diagram Description automatically generated **Forms--an easy way to enter or display data** An easy way to enter or display data (into or from a table). The objects on a form are called controls. The objects of a form are called controls. There are 3 types of controls: 1. [Bound control:] A bound control is 'bound' to the underlying table and is used to enter or change data in the table. E.g. the white box called 'Customer' in Figure 10.12 is a bound control. This control will display the company name from the client table. 2. [Unbound controls]: Unbound controls display information that doesn't come from the table, such as graphics or labels. E.g. the title and Hammer graphic in Figure 10.12. Note that a label is often paired with a bound control to tell the user what the field is for (e.g. the labels 'Customer', ' Address', 'Suburb' etc. in Figure 10.12). 3. [Calculated controls:] Calculated controls occur where the data doesn't come directly from the underlying table but from a calculation based on the table. E.g. calculated control can add the various tap supply and installation costs together to display a total cost.  **Queries--- let you ask questions of your database** Relational databases use **Structured Query Language** (SQL) to manipulate data and enable end-users to locate fields and records in a database. A screenshot of a phone Description automatically generated  Dynaset can be used as the basis of a form or report, a more usable format for the end user A screenshot of a computer Description automatically generated **Reports** Reports provide a method of rearranging data to produce information for end-users in a useful format. It presents data from your tables or queries as information in a printed format. Reporting functions of relational database packages have incorporated some of the most useful features of standard reports to assist in the creation of readable documents. Reports are divided into sections, as follows: - The report header appears once at the beginning of the report (title, date, and so on).  - The report footer appears once at the end of the report and contains information on the report as a whole (the total number of pages). - The page header appears at the top of every page (usually field headings). - The page footer appears at the bottom of every page (usually page numbers). - Detail holds the main body of the report. Information appears for each record in the underlying table or query. **Guidelines for designing solutions (design phase of the information system)** 1. **Information gathering** to ensure that all stakeholders' perspectives are taken into consideration, and that all data storage and output requirements are specified. 2. **Input-Processing- Output table:** to describe the business processes required for the new system 3. **Design the tables you will need:** Entity-relationship diagrams are used to translate the IPO table into a data model providing xphysical specifications for the creation of the required tables, fields and relationships. 4. **Design assumptions -- Business rules** - The business rules need to be applied to describe the input data in terms of properties. - Validation rules ensure that the data is as correct and complete as possible. **Database management staff roles** - System analyst -- - Data-entry employee -- - Data Quality-assurance officer -- - Systems administrator -- - Network administrator -- - Database administrator -- This employee is responsible for the organisation's data and maintains the data structure. This is a senior information systems role as these employees have control over the physical data definition, implementing data definition controls and defining and initiating database backup and recovery. - Security architect -- These employees examine, design, implement and oversee the operation of the security infrastructure that protects the organisation's data resources.  **SUMMARY** Traditionally, data was stored and accessed sequentially. The business processes and structures in place to utilise technology manipulating data organised in this manner supported inefficiency in the work environment. In order to complete business transactions, employees were required to repeatedly enter detailed fragments of data that resulted in the storage of redundant data. These business processes allowed the same input data to be stored in multiple locations. This affected the reliability or integrity of business information resources. The subsequent availability of database management systems, and the application of relational database concepts underpinning the design of the data organisation, had an enormous, positive impact on effective information usage in business contexts. Knowledge workers use information created internally or externally to the organisation they work for to support most activities. The explosion in communications technologies, desktop productivity tools, and databases providing information to assist end-users has changed business performance expectations. End-users have access to leading technologies such as data warehouses and data mining techniques and tools that solve problems or realise opportunities. DBMS software is used to manage an organisation's digital data. Relational database applications software on the desktop enables end-users to design simple database solutions to business problems. The design by end-users of relational databases to support their decision-making and productivity requires the use of systems analysis and design tools and end-user applications. Data needs to be retrieved, grouped, categorised, sorted or manipulated to transform it into information. Databases provide the facility to do this. A thorough understanding of the problem or opportunity, and logical models of the design, are needed prior to implementation. The first step in describing the database design is the creation of an IPO chart. This tool illustrates the inputs to the business process being examined and the logic required to produce desirable outputs. The next step is to use an ERD to describe the proposed data model. This is all completed prior to implementing the database design in a database system. The business rules and assumptions required to bound the system being modelled reflect the business operations being investigated. Interfaces and reports to enable communication with end-users of the system are then designed, taking into consideration the graphical user interface (GUI) and good design guidelines. **[Designing effective business solutions]** **Systems and system thinking** Two types of systems - [Open systems] and closed systems depending on the relationship of the system with its environment. Open systems interact freely with their environments, taking in input and returning output. - [Closed systems] don't interact with their environments, so changes in the environment are not an issue of concern. 9 characteristics of a system: 1. **Components**: a part, or aggregation of parts, of a system commonly referred to as a subsystem. 2. **Interrelated components**: the dependency of one subsystem on one or more subsystems. Subsystems are related and usually interact with each other in order to achieve their predetermined objectives within their environment. 3. **Boundary**: the line that marks the inside and outside of a system and that sets off the system from its environment. 4. **Environment**: everything external to a system that interacts with the system. 5. **Interfaces**: point of contact where a system meets its environment or where subsystems meet each other. 6. **Input**: whatever a system takes from its environment in order to fulfil its purpose. 7. **Output**: whatever a system returns to its environment in order to fulfil its purpose. 8. **Constraint**: a limit or condition within which a system can accomplish its objectives. 9. **Stakeholders**: person(s) or organisation(s) that have a direct interest in the system. **User requirements** - Requirements are defined using fact-finding techniques and a problem-solving process, and then aligned to the features of an appropriate product. - A systems analyst may perform the matching of an optional Computer Information Technology solution with the described business problem. - The symptoms of these instances are the transformation of simple tasks into complex processes, and the creation of processes that are disconnected from the actual functions that display misunderstandings relating to the significance of important user needs. - A consequence of this is that user requirements may be left out altogether, functions may be provided that are not used, and the sequencing of tasks can become extremely difficult as end-use