Data Analytics and AI for Business Operations

- Overview of data analytics, artificial intelligence, and their role in modern business operations.
- Differences between descriptive, diagnostic, predictive, prescriptive, and AI-driven analytics.
- How operations teams use data to improve productivity, quality, cost, speed, and customer value.
- Business problems, use cases, operational KPIs, and measurable success criteria.
- Data-driven decision-making culture and the role of managers, analysts, and operational teams.
- AI opportunities in business operations: automation, forecasting, optimization, and decision support.
- Limitations of AI, human judgment, accountability, and professional responsibility.
- Mini case study: identifying analytics and AI opportunities in a real business process.

- Business data sources: ERP, CRM, HR, finance, sales, supply chain, website, IoT, and external data.
- Data types, data formats, structured and unstructured data, and operational data flows.
- Data collection methods, sampling, survey data, transactional data, and system-generated data.
- Data quality dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness.
- Data cleaning, missing values, duplicate records, outliers, and practical data preparation steps.
- Data governance roles, ownership, access control, documentation, and data dictionaries.
- Data privacy, confidentiality, cybersecurity awareness, and responsible handling of business data.
- Workshop: building a simple data quality checklist for a business operations dataset.

- Foundations of business statistics for decision-making and operations improvement.
- Measures of central tendency, variation, distribution, correlation, and trend analysis.
- Exploratory Data Analysis (EDA) for understanding patterns, anomalies, and business insights.
- Hypothesis testing, confidence intervals, A/B testing, and practical interpretation of results.
- Operational performance analysis using KPIs, benchmarks, and variance analysis.
- Using spreadsheets and analytics tools to summarize, filter, segment, and interpret data.
- Turning raw data into meaningful operational questions and evidence-based conclusions.
- Case study: analyzing service delays, customer complaints, or sales performance data.

- Principles of effective data visualization for managers and operational decision-makers.
- Choosing the right visual: tables, charts, scorecards, heatmaps, funnels, and trend dashboards.
- Business Intelligence (BI) concepts: data models, measures, dimensions, filters, and drill-downs.
- Designing dashboards for operations, finance, marketing, HR, supply chain, and customer service.
- Dashboard storytelling: clarity, simplicity, accuracy, audience needs, and visual hierarchy.
- Common visualization mistakes and how to avoid misleading interpretations.
- Introduction to tools such as Excel, Google Sheets, Power BI, Tableau, or similar platforms.
- Practical task: designing an executive operations dashboard with clear KPIs and insights.

- Introduction to databases, tables, records, fields, relationships, and operational information systems.
- SQL basics: SELECT, WHERE, ORDER BY, GROUP BY, joins, aggregation, and filtering business data.
- Data extraction, transformation, and loading (ETL) concepts for operational reporting.
- Data warehouses, data marts, cloud databases, and the importance of a single source of truth.
- Automating recurring reports and reducing manual data work in business operations.
- Data lineage, documentation, version control, and auditability of business reports.
- Data pipeline risks: broken sources, wrong definitions, access issues, and quality failures.
- Workshop: writing basic queries to answer practical business operations questions.

- Introduction to machine learning and its practical use in business operations.
- Supervised and unsupervised learning: classification, regression, clustering, and recommendation.
- Preparing data for modeling: features, labels, training data, testing data, and validation.
- Forecasting demand, sales, inventory needs, staffing levels, and operational workload.
- Customer segmentation, churn prediction, lead scoring, fraud detection, and risk prediction.
- Model evaluation: accuracy, precision, recall, error, bias, overfitting, and business usefulness.
- Communicating model results to non-technical stakeholders through clear business language.
- Case study: building and interpreting a simple predictive model for an operational decision.

- Business process mapping, bottleneck identification, workflow analysis, and improvement opportunities.
- AI-enabled automation: robotic process automation, intelligent workflows, and decision rules.
- Using analytics to reduce waste, waiting time, errors, rework, and unnecessary manual tasks.
- Process mining, operational monitoring, and continuous improvement through data feedback loops.
- Inventory optimization, scheduling optimization, routing, capacity planning, and resource allocation.
- Human-AI collaboration and redesigning roles, responsibilities, and approval points.
- Implementation risks: resistance, poor data, unclear ownership, and automation without strategy.
- Practical task: redesigning a business process using analytics and AI-supported automation.

- Introduction to generative AI, large language models, copilots, and prompt engineering for business work.
- Using generative AI to summarize reports, draft communications, analyze documents, and support decisions.
- Prompt design principles: role, context, task, format, constraints, examples, and verification.
- AI-assisted knowledge management, customer support, training, documentation, and operational reporting.
- Decision support systems, scenario analysis, simulations, and management recommendations.
- Evaluating AI outputs: hallucinations, source checking, human review, and quality assurance.
- Confidentiality, sensitive information, copyright awareness, and safe organizational use of AI tools.
- Workshop: building a responsible AI prompt library for operational tasks.

- Supply chain analytics: demand planning, supplier performance, logistics, procurement, and stock control.
- Finance analytics: budgeting, cash flow, cost control, variance analysis, and financial forecasting.
- Marketing and sales analytics: campaign performance, customer behavior, conversion, and revenue trends.
- HR analytics: workforce planning, productivity, retention, recruitment metrics, and training impact.
- Customer experience analytics: satisfaction, service quality, complaints, loyalty, and response time.
- Risk and compliance analytics: anomaly detection, operational risk indicators, and control monitoring.
- Cross-functional dashboards and integrated decision-making across departments.
- Capstone workshop: selecting a business function and designing an analytics-driven improvement plan.

- Developing an AI and analytics strategy aligned with organizational goals and operational priorities.
- AI governance: roles, policies, documentation, accountability, transparency, and monitoring.
- Ethical AI: fairness, bias, explainability, privacy, human oversight, and responsible innovation.
- Risk management for analytics and AI projects: data, model, cybersecurity, legal, and reputational risks.
- Change management, staff training, stakeholder communication, and adoption of new data practices.
- Measuring impact: productivity, cost reduction, quality improvement, customer value, and ROI.
- Integrating data collection, analysis, dashboards, models, automation, governance, and implementation plans.
- Preparation of the final data analytics and AI business operations project.