Introduction
The programme “Big Data Analytics” develops professional practice in Information Systems. It connects the subject with workplace challenges by examining existing evidence and practices and choosing methods appropriate to the organization and its available resources.
The learning progresses from data analysis and decision support to project planning and implementation control, with practical work on information systems analysis, integration, and service management, information protection and incident response, designing digital transformation and developing services. Participants prepare outputs to support workplace application and review the quality and results of implementation.
Learning objectives
- Turn available data into documented analysis that supports a specific professional decision in an application related to “Big Data Analytics”.
- Analyze information system requirements and design acceptance, integration, and operational criteria.
- Assess information risks and select suitable preventive controls and a response plan.
- Assess digitization opportunities and define implementation requirements that improve a service or process.
- Prepare a project plan connecting scope, time, resources, and risks.
Learning areas
1. Data Analysis and Decision Support
- Frame an analytical question linked to a specific decision, and define the unit of measurement, time period, and data needed to answer it.
- Check data quality for completeness, accuracy, consistency, and duplication, and document cleaning steps and limitations on reliance.
- Select appropriate comparisons, indicators, and charts, and distinguish statistical correlation from causal explanations of results.
- Interpret results in the work context, test assumptions and unusual values, and present alternatives and their associated uncertainty.
- Prepare an analytical report containing the finding, recommendation, data source and date, review owner, and next follow-up step. The application examines a case related to “Big Data Analytics”.
2. Information Systems Analysis, Integration, and Service Management
- Analyze system users, processes, and the decisions it supports, translating needs into clear functional and nonfunctional requirements.
- Model data flows, entities, relationships, and interfaces, identifying each data item's source and the party responsible for its quality and updating.
- Compare build, buy, integration, and hosting alternatives according to constraints, security, total cost, and available support capabilities.
- Design test cases covering performance, permissions, usability, and integration, and connect each test to a testable acceptance criterion.
- Prepare a transition, operations, support, change management, and backup plan, defining indicators for service availability, fault resolution, and improvement review.
3. Information Protection and Incident Response
- Inventory assets, data, accounts, services, and their interdependencies, classifying sensitivity, criticality, and accountable owners.
- Analyze threats, vulnerabilities, and potential misuse, and identify necessary controls according to risks, resources, and applicable law.
- Review identity and access management, updates, backups, and logs, and identify evidence for verifying control effectiveness.
- Build a process for incident detection, classification, containment, escalation, and evidence preservation, specifying authorized internal and external communication.
- Conduct an exercise using authorized training data and an authorized training environment, and evaluate response and recovery times, lessons learned, and a gap remediation plan.
4. Designing Digital Transformation and Developing Services
- Distinguish information digitization, step automation, and business model transformation, and identify the problem targeted by the digital intervention.
- Map the beneficiary journey, touchpoints, and repeated data requests, and identify opportunities for simplification before selecting technology.
- Analyze readiness across procedures, skills, data, and technical infrastructure, and document gaps and dependencies on existing systems.
- Specify solution, integration, access permission, usability, and accessibility requirements, and establish testable acceptance criteria.
- Design a limited-scope pilot with indicators for time, quality, and use, and define conditions for continuing, adjusting, or stopping.
5. Project Planning and Implementation Control
- Define the project rationale, expected results, stakeholders, and success criteria, and document the scope of work and exclusions.
- Break deliverables into work packages and related activities, and estimate durations, resources, and activity dependencies.
- Prepare a schedule and cost baseline, identify delivery and review milestones, and clarify approval and escalation responsibilities.
- Analyze implementation risks, define responses and warning indicators, and control change requests and their effects on time and cost.
- Build a progress report covering variances, decisions, and lessons learned, and define procedures for accepting deliverables and closing the project.
Practical outputs
- A dashboard and decision memo explaining the data, assumptions, and alternatives.
- A requirements document, data flow map, and testing and acceptance plan.
- An information asset and risk register, response plan, and recovery test.
- A digital transformation roadmap covering the beneficiary journey and implementation requirements.
- A project charter, implementation schedule, and change and risk tracking register.
Who should attend
- Practitioners, specialists and supervisors whose work relates to the programme subject.
- Managers and teams working in information systems, according to their practical responsibilities.
- Professionals responsible for applying the outputs of “Big Data Analytics” within their organizations.
Prerequisites
- Working familiarity with the programme field and the ability to discuss a professional case or challenge.
- Confirm the suitability of your experience and any specialist prerequisites with the Union before participation.
Conclusion
The programme “Big Data Analytics” brings together concepts, case review and a practical application. Participants prepare steps that can be adapted to their responsibilities and organizational resources, with an indicator to follow application and review results after returning to work.
Participation arrangements and any special requests are specified in the confirmation issued by the Union.