Example of on the training documentation in business management major in human resource

1)Changing Business Environments and Evolving Needs for Decision Support and Analytics :-. Need for Analytics in Modern Business:-Modern businesses use analytics to: Understand what is happeningll Predict what is likely to happenll Decide what actions to takell These activities require organizations to collect and analyze large volumes of data.llEarlier, computers were used mainly for payroll and bookkeeping, but now they are used in:Automated factory design and managementll Supply chain managementllEvaluation of mergers and acquisitions llAlmost all executives now consider information technology vital for their business operations.ll 2. Importance of Decision Making:-Decision making is the most important activity in any organization.llIt determines the success or failure of the organization.llIt has become more difficult due to: Internal changesllExternal business pressuresll Good decisions bring high rewards, while wrong decisions lead to huge losses.ll 3. Types of Organizational Decisions:-According to De Smet et al. (2017), organizational decisions are classified into:Big-bet, high-risk decisions ll Cross-cutting decisions (repetitive but high-risk and involve group work)llAd-hoc decisions (arise occasionally)llDelegated decisions (handled by individuals or small groups)ll Each type requires a different decision-making approach.ll 4. Changing Nature of Decision Making:-Earlier, decision making was considered an art, based on: Intuition ,Experience JudgmentllNow, companies that focus on analytical, methodical, and data-driven decision making perform better than those that rely only on communication skills.ll Modern managers must rely on analytics and systematic approaches instead of intuition alone.ll 5. Role of IT and Analytics in Decision Making:-Data volume is growing rapidly (it doubles every two years).llComputer systems are now used for: Automated factoriesllBusiness analytics llMerger and acquisition evaluationll Cloud-based and mobile systems are widely used.llImportant tools include: Data Warehousing ll Data MiningllOLAPll Dashboards ll Many decisions today are automated using these technologiesll Technologies for Data Analysis and Decision Support:- 6. Group Communication and Collaboration :_-any decisions are made by groups in different locations.llCollaboration tools and smartphones allow easy communication.llVery important in supply chains, where partners must share information quickly to react to changing customer demand.ll 7. Improved Data Management:-Data may be: Text ll Sound ll Graphics ll Videoll Stored across different systems and locations.ll Modern systems can store, search, and transfer data quickly, securely, and cheaply.ll 

8. Data Warehouses and Big Data:-Companies like Walmart maintain huge data warehouses.ll Technologies used: Parallel computing ll Hadoopll Spark ll Cloud platformsll Big Data allows companies to view performance in ways not possible earlierll 9. Analytical Support:-Analytics helps managers: Evaluate many alternativesll Make better forecastsll Perform risk analysisll Run simulations ll Experts can contribute remotely, reducing cost and time.ll 10. Overcoming Human Cognitive Limits:_Humans have limited ability to store and process information.ll Computer systems overcome these limits by:Processing huge datall Reducing errorsll Providing faster insightsll This improves decision quality.ll 11. Knowledge Management:_ Organizations store large amounts of knowledge about: Customers ll Employees ll Operationsll Tools like text analytics help convert this knowledge into useful insights.ll 12. Anywhere, Anytime Support:_With mobile and cloud technologies, managers can:Access data anytimell From any place ll This has increased the speed and expectations of decision making.ll 13. Innovation and Artificial Intelligence:-AI plays a major role in decision making. It supports: Data analysisll Pattern recognitionll Automated decisionsll AI and analytics together create a powerful decision-makingsystemll 2) Decision-Making Processes or simon :-Decision-Making Processes:-Decision making is a systematic process through which managers identify problems, analyze alternatives, choose the best solution, and implement it. According to the textbook, this process consists of four major phasesll 1. Intelligence Phase:-This is the problem-identification stage.:-The organization’s goals and objectives are examined to see whether they are being  achieved.llA problem occurs when there is a gap between what is expected and what is actually happening.llThe decision maker:Determines whether a problem existsll Identifies symptomsll Measures the size of the problemllClearly defines the real problemll Often, what appears to be the problem (e.G., high cost, low sales) may only be a symptom of a deeper issue (e.G., poor inventory management or weak marketing).ll Data Collection Issues:-During this phase, data must be collected, but several problems may occur:Data may be unavailable, costly, inaccurate, or insecurell Some data may be qualitative (soft data)ll There may be too much data (information overload)llFuture data may not follow past trendsll The phase ends with a formal problem statement.ll 2. Design Phase:-In this phase,possible solutions are developed and analyzed.ll A model of the problem is created.ll A model is a simplified representation of reality.ll Assumptions are made to reduce complexity.ll Variables and their relationships are identified.ll

Different alternatives are generated and tested for feasibility.ll The goal is to understand the problem better and develop workable solution options.ll 3. Choice Phase:- This is the decision stage.ll The best alternative is selected.ll Each option is evaluated for: Viabilityll Profitabilityll Ability to meet goals ll Techniques used: Sensitivity analysis – checks how changes affect the decisionll What-if analysis – studies different scenariosll Goal seeking – finds values needed to reach a targetll The decision maker commits to one course of action.ll 4. Implementation Phase:- This phase involves putting the chosen solution into action.ll It introduces change, which must be managed carefully.ll Important factors: User acceptancell Top management supportll Training ll Resistance to change ll Implementation also includes: Monitoring resultsll Collecting feedback data ll Learning from past decisions to improve future onesll

3)Computerized Decision Support Framework :_The computerized decision support framework is a system that uses computers, data, and analytical models to help managers make better decisions in complex and changing business environments. The textbook explains that decision making requires large amounts of data, information, and knowledge, and therefore it must be supported by computerized system llMain Elements of the Framework:_1. Data Management:_Decisions are based on real organizational data.llData is collected from many sources such as sales, inventory, customers, and operations.llThese data help in:Identifying problems ll Measuring performancell Predicting future conditionsll Model Management:_A model is a simplified representation of reality.llInstead of experimenting on the real system, managers test decisions on models.ll Models: Represent relationships between variablesllUse assumptions to simplify realityll Help analyze alternativesll Analytical Support:-Sensitivity analysis – checks how small changes affect the solutionllWhat-if analysis – tests different scenariosllGoal seeking – finds the values needed to reach a targetll  user interface:-Managers interact with the system through:ReportsllDashboardsllScreens and menusll Implementation and Feedback:-The chosen solution is put into action.llData is collected after implementation to:Check if the decision workedllLearn from mistakesllImprove future decisionsll 4)Evolution of Computerized Decision Support to Analytics/Data Science, :-

The evolution of computerized decision support reflects how organizations have moved from simple reporting systems to advanced analytics and artificial intelligence. Figure 1.18 in the textbook shows how the terminology and capabilities have changed from the 1970s to the 2020s.ll 1. 1970s – Routine Reporting and MIS:-In the 1970s, organizations mainly used computers to generate structured, periodic reports such as daily, weekly, or monthly summaries. These reports showed what had happened in the past.llThese systems were called Management Information Systems (MIS).llMIS provided routine reports to managers to support monitoring and control.llll 2. Late 1970s & 1980s – DSS, OR, and Expert Systems:_During this period: Operations Research (OR) models were used to solve optimization and resource allocation problems.llSimulation and heuristic methods were applied when problems were too complex for mathematical models.ll Expert Systems (ES) emerged, which captured expert knowledge in the form of if-then rules to provide advice and recommendations like a human expert.ll 3. 1980s – ERP and Relational Databases :-In the 1980s, organizations integrated their separate systems into Enterprise Resource Planning (ERP) systems.Data was stored using Relational Database Management Systems (RDBMS).llThis ensured:Data consistencyllData integrityllA single version of the truth across the organizationll ERP made data available across all departments, enabling on-demand reporting instead of fixed routine reports.ll 4.1990s – Data Warehousing and Executive Information Systems:-In the 1990s:Executive Information Systems (EIS) were created for senior managers.ll These used dashboards and scorecards to track key performance indicators (KPIs).llTo support this, organizations built Data Warehouses (DW), which stored large volumes of historical data for analysis.ll 5. 2000s – Business Intelligence (BI):-In the 2000s: Data-warehouse-driven DSS became known as Business Intelligence (BI).ll BI provided:Interactive dashboardsll Reporting ll Drill-down analysisll Because data was updated periodically, systems evolved into: Real-time or Right-time data warehousing.ll To discover useful patterns in large datasets, techniques such as: Data miningll Text mining ll Cloud computing and Software-as-a-Service (SaaS) made analytics affordable even for small and medium-sized companies.ll 6. 2010s – Big Data and Advanced Analytics:-With the rise of:Social media ll IoT devices ll RFIDll Clickstream logsll Smart devices ll  ll Fast in speed ll Diverse in format ll New technologies such as: Hadoopll MapReduce ll NoSQL databasesll Parallel processing ll 

5)Framework for Business Intelligence and Analytics:-The BI and Analytics framework shows how organizations convert raw data into useful information and knowledge in order to support better decision making and improved business performance. It explains the complete flow from data collection to decision making.llThe framework consists of four main layers.:-1. Data Sources :-This is the starting point of the framework.llData is collected from:Transaction processing systems (sales, inventory, payroll, etc.)ll Enterprise systems such as ERP, CRM, and SCM ll Web, social media, mobile applications ll External data sources (market data, competitors, government reports)llData may be:StructuredllSemi-structured ll Unstructured ll 2. Data Warehousing and Data Management-This layer prepares data for analysis.ll It includes:ETL (Extract, Transform, Load) processes to clean and integrate datallData Warehouse (DW) – a central repository of historical and integrated datallData Marts – smaller databases for specific departmentsllThis layer ensures that data is:Consistent llAccurate ll Integrated llTime-oriented ll 3. Business Analytics Layer:-This layer converts data intoknowledge and insights.ll It includes:Reporting and queryingllOLAP (Online Analytical Processing)llData mining ll Text and Web mining ll Predictive and prescriptive models llOptimization and simulation ll 4. Presentation and Performance Management:- This is the delivery layer.ll it provides:Dashboards ll Scorecardsll Reports ll Visualizations ll Alertsll Managers use these tools to: Monitor KPIs ll Track performance ll Identify problems ll Take corrective actionsll 6)Define Analytics. List and explain three types of analytics:- Definition of Analytics :_Analytics refers to the use of data, statistical methods, models, and analysis techniques to generate insights and support decision making. It helps organizations understand what is happening, predict future outcomes, and decide what actions should be taken.ll Three Types of Analytics :-1. Descriptive Analytics :Descriptive analytics focuses on understanding what has happened and what is currently happening in an organization.ll It involves:-• Consolidating data from multiple sources • Making data available for analysis • Creating reports and summariesll Tools and technologies include: • Business reports • Dashboards • Scorecards • Data warehousing • Visualization tools ll These tools help organizations identify trends, patterns, and performance and provide answers to: • What happened? • What is happening? ll

2. Predictive Analytics :-Predictive analytics aims to determine what is likely to happen in the future. It uses: • Statistical techniques • Data mining methods It helps answer questions such as: • Will a customer leave (churn)? • What will a customer buy next? • Will a customer respond to a promotion? Techniques used include: • Classification (logistic regression, decision trees, neural networks) • Clustering (to group customers) • Association mining (to find relationships between purchases) This allows organizations to forecast future events and customer behavior. 3. Prescriptive Analytics:_ Prescriptive analytics focuses on deciding what should be done. It uses: • Optimization • Simulation • Decision modeling • Expert systemsll It combines: • What is happening (descriptive) • What will happen (predictive) to recommend the best possible actions to achieve optimal performance. It answers: • What should I do? • Why should I do it? ll

7)Business Applications of Conversational AI — Chatbots:-Conversational AI (Chatbots):-Conversational AI refers to computer systems that can interact with humans using natural language through text or voice. These systems, commonly called chatbots or virtual assistants, use Artificial Intelligence (AI), Natural Language Processing (NLP), and machine learning to understand user questions and provide meaningful responses.llChatbots operate using: Natural Language Processing (NLP) – to understand user inputll Machine Learning – to improve responses over time ll Knowledge bases and databases – to provide correct information llIntegration with business systems (CRM, ERP, websites, apps)ll Business Applications of Chatbots:-1. Customer Service:-Chatbots are widely used to:Answer customer queries ll Track orders llProvide product information ll Handle complaintsll 2. Sales and Marketing:-Chatbots help in:Recommending products llAssisting customers during online shoppingllCollecting customer preferencesllRunning promotional campaignsll3. Banking and Finance:_Banks use chatbots for:Account balance inquiryllTransaction historyllLoan and credit card queriesllFraud alerts ll4. Healthcare:-Chatbots are used to:Book appointmentsllProvide basic medical advicellSend reminders llAnswer patient queries ll5. Human Resource (HR):Employee onboardingllLeave managementllPolicy queriesll Training supportllThey improve internal efficiency.ll6. E-Commerce:-Online stores use chatbots to:Help customers find productsllHandle returns and refundsllGive shipping updatesll 8)Artificial Intelligence: Concepts, Drivers, and Major Technologies :-Artificial Intelligence (AI) refers to the ability of machines and computer systems to perform tasks that normally require human intelligence. These tasks include learning, reasoning, problem solving, decision making, speech recognition, and understanding language. AI systems use data, algorithms, and computing power to simulate human thinking and improve their performance over time.ll 2. Drivers of Artificial Intelligence:The rapid growth of AI is mainly due to the following drivers: a) Big Data:-Huge amounts of data are generated from business systems, social media, sensors, and online activities. AI systems require large datasets to learn patterns and make accurate predictions.ll b) High Computing Power:-Advances in cloud computing, GPUs, and parallel processing allow AI systems to process large volumes of data quickly, making complex AI models possible.ll

c) Improved Algorithms:-The development of machine learning and deep learning algorithms enables computers to automatically learn from data instead of being explicitly programmed.lld) Digital and Internet Technologies:-The widespread use ofinternet, mobile devices, IoT, and social media provides continuous streams of data that support AI applications.ll 3. Major Technologies of Artificial Intelligence:-a) Machine Learning:_Machine learning allows computers tolearn from data and improve their performance without explicit programming. It is used for prediction, classification, and pattern recognition.llb) Deep Learning:-Deep learning uses multi-layer neural networks to process complex data. It is widely used in image recognition, speech recognition, and autonomous systems.llc) Natural Language Processing (NLP):_NLP enables machines to understand, interpret, and respond to human language. It is used in chatbots, voice assistants, and language translation.ll d) Computer Vision:-Computer vision helps machines interpret images and videos, such as face recognition and medical image analysis.lle) Expert Systems:-Expert systems userules and knowledge bases to provide decisions and recommendations similar to human experts.ll