Big Data for Managers Training Course

Participants can prioritise an analytics portfolio using explicit strategic and delivery criteria.

• Analytics strategy linked to corporate objectives and operating priorities
• Use-case canvases define users, decisions, actions and expected benefits
• Impact-effort matrices rank candidate initiatives for investment decisions
• Dependencies mapped across data, people, technology and process change
• Quick wins distinguished from foundational data capability investments
• Portfolio roadmaps sequence initiatives by value, readiness and risk

Participants can use CRISP-DM to structure an analytics initiative from problem definition through deployment.

• CRISP-DM phases translated into management decisions and stage gates
• Business understanding converts strategic questions into analytical objectives
• Success criteria define adoption, financial, service and risk measures
• Data understanding tests availability, meaning, lineage and suitability
• Model evaluation checks technical performance against business acceptance criteria
• Deployment planning assigns ownership, workflow changes and review cycles

Participants can specify fit-for-purpose data requirements and assess source-system suitability.

• Source inventories distinguish systems of record from secondary datasets
• Data requirements define entities, attributes, granularity, history and refresh frequency
• Entity resolution links customer, supplier, asset and transaction records
• Data lineage traces critical fields from capture through reporting outputs
• Metadata records definitions, owners, classifications and permitted uses
• External data assessed for provenance, licensing, bias and refresh reliability

Participants can define data quality controls and assign accountability for remedial action.

• Accuracy, completeness, consistency, timeliness, validity and uniqueness quality dimensions
• Critical data elements selected from decisions, controls and reporting needs
• Business rules detect missing values, invalid codes and duplicate records
• Data profiling exposes distributions, outliers, nulls and format inconsistencies
• Quality scorecards assign thresholds, owners, exceptions and remediation dates
• Root-cause analysis separates capture errors from integration and transformation defects

Participants can evaluate major data architecture patterns against business and operational requirements.

• Data warehouses support curated reporting, governed metrics and historical analysis
• Data lakes retain raw structured, semi-structured and unstructured source data
• Lakehouse architectures combine open storage with managed analytical tables
• Data mesh principles distribute domain ownership with shared governance standards
• ETL and ELT patterns compared for transformation location and control
• Architecture choices assessed against scale, latency, cost and skills

Participants can assess processing, integration and cloud operating choices for a proposed initiative.

• Cloud service models distinguish infrastructure, platform and software responsibilities
• Apache Spark processing explained through distributed datasets and parallel tasks
• APIs, message queues and file transfers selected by integration requirement
• Data pipelines monitored for freshness, failures, volumes and processing cost
• FinOps practices allocate cloud consumption to products and business units
• Disaster recovery requirements define recovery time and recovery point objectives

Participants can specify decision dashboards that support reliable monitoring and action.

• Key performance indicators defined with formulas, owners and decision thresholds
• Leading and lagging indicators separated for operational intervention
• Dimensional analysis compares performance by time, segment, product and location
• Dashboard requirements distinguish monitoring, diagnosis, exploration and reporting needs
• Visual encodings selected to avoid misleading scales and comparisons
• Power BI dashboard prototypes tested against user decisions and workflows

Participants can interpret common statistical evidence and recognise unreliable conclusions.

• Population, sample and sampling bias examined before interpreting results
• Mean, median, percentiles and variation selected for appropriate distributions
• Correlation distinguished from causation in operational and customer data
• Confidence intervals communicate uncertainty around estimates and comparisons
• A/B tests define control groups, treatment groups and success measures
• Confounding variables identified before approving causal claims or interventions

Participants can evaluate whether a machine learning proposal is appropriate and sufficiently evidenced.

• Classification models predict categories such as churn, fraud or default
• Regression models estimate numerical outcomes including demand and revenue
• Clustering groups customers, cases or assets without predefined labels
• Precision, recall, F1 score and confusion matrices guide classification decisions
• ROC curves compare model discrimination across decision thresholds
• Training, validation and test datasets prevent overstated model performance

Participants can set proportionate controls for fair, explainable and accountable analytical decisions.

• Protected characteristics identified within customer, employee and beneficiary decisions
• Fairness metrics compare error rates across relevant population groups
• Feature selection reviewed for proxy discrimination and unjustified exclusion
• Explainability methods communicate drivers behind individual model outputs
• Human-in-the-loop controls define escalation, override and appeal routes
• Model cards document intended use, limitations, risks and approvals

Participants can establish governance controls for data access, use, retention and accountability.

• DAMA-DMBOK roles distinguish data owners, stewards, custodians and consumers
• Data classification separates public, internal, confidential and restricted information
• Purpose limitation constrains personal data use to approved objectives
• Role-based access control restricts datasets according to job responsibilities
• Retention schedules define disposal triggers, archival periods and legal holds
• Privacy impact assessments identify processing risks before implementation

Participants can define roles, forums and controls for sustained analytics delivery.

• Product operating models organise analytics around persistent business outcomes
• RACI matrices clarify sponsor, product owner, engineer and risk responsibilities
• Data governance councils resolve ownership, quality and access decisions
• Agile backlogs prioritise data products, features, defects and technical debt
• Stage gates test readiness for pilot, release, scale and retirement
• Service-level objectives define availability, freshness and response expectations

Participants can plan adoption and benefits tracking for an analytics-enabled change.

• Benefits maps connect data capabilities to actions and measurable outcomes
• Baseline measures established before interventions alter operational performance
• Adoption metrics track active users, workflow compliance and decision usage
• Process redesign embeds analytical recommendations into frontline work routines
• Change impact assessments identify affected roles, skills and incentives
• Benefits reviews compare realised outcomes against approved business cases

Participants can identify analytics risks and specify monitoring responses after deployment.

• Risk registers capture data, model, cyber, supplier and operational exposures
• Model drift detected through performance, input distribution and outcome monitoring
• Data drift alerts identify changing source patterns and feature distributions
• Threshold breaches trigger investigation, rollback, recalibration or escalation
• Independent validation challenges assumptions, methods, data and documentation
• Audit trails retain approvals, versions, access events and decision evidence

Participants can prepare a defensible investment case and assess supplier proposals.

• Total cost of ownership includes platforms, storage, skills and support
• Build, buy and partner options compared against strategic control requirements
• Vendor due diligence reviews security, portability, service levels and viability
• Proof-of-value scopes define hypotheses, datasets, duration and exit criteria
• Procurement requirements specify data ownership, intellectual property and audit rights
• Investment cases quantify benefits, uncertainty, costs and implementation dependencies

Participants can build, review and present a complete big data initiative pack for an executive decision.

• Select a priority business problem with accountable sponsor and measurable outcome
• Complete a CRISP-DM use-case canvas with assumptions and acceptance criteria
• Assess source data, quality risks, lineage and access requirements
• Propose architecture, operating roles, governance controls and delivery stages
• Build benefits measures, cost assumptions, risks and adoption actions
• Present the initiative pack and defend recommendations through stakeholder review