Data Analysis and Information Management Training Course

Participants can identify relevant sources and interpret common structured data relationships.

• Source inventory covering systems, spreadsheets, files, reports and external datasets
• Entity relationship diagrams showing tables, keys, cardinality and business meaning
• Primary keys, foreign keys and surrogate keys in transactional datasets
• Fact and dimension tables within star-schema reporting models
• Data lineage tracing report values back through transformations and sources
• Source selection criteria covering authority, granularity, freshness and accessibility

Participants can assess dataset fitness using measurable data-quality dimensions and evidence.

• Column profiling for data types, ranges, formats and null values
• Completeness rates calculated by record, field, segment and reporting period
• Validity checks against permitted values, patterns and business constraints
• Consistency tests comparing repeated attributes across systems and reports
• Duplicate detection using exact matching, fuzzy matching and survivorship rules
• Timeliness measures comparing event dates, load dates and publication dates

Participants can prepare analysis-ready data while documenting each transformation and exception.

• Standardising dates, currencies, text values and categorical labels consistently
• Missing-value treatment using deletion, imputation, flags and exception codes
• Outlier identification using interquartile range, z-scores and business thresholds
• Parsing compound fields into atomic attributes for reliable analysis
• Transformation logs recording source fields, rules, outputs and approvals
• Reconciliation checks comparing transformed totals against controlled source totals

Participants can create controlled spreadsheet models for repeatable analytical work.

• Structured tables supporting stable references, filters and calculated columns
• Lookup functions matching codes, reference data and classification values
• Pivot tables summarising measures by segment, period and category
• Named ranges and formula auditing for transparent model logic
• Error checks identifying broken references, duplicate records and invalid formulas
• Version control conventions for files, assumptions, changes and approvals

Participants can retrieve defined records and measures from relational database tables.

• SELECT statements retrieving required fields from named database tables
• WHERE clauses filtering records with logical operators and predicates
• ORDER BY sorting outputs for review, ranking and presentation
• DISTINCT queries identifying unique values and possible classification issues
• CASE expressions creating documented categories and conditional measures
• NULL handling using IS NULL, COALESCE and explicit data rules

Participants can combine tables and calculate grouped measures without double-counting.

• INNER JOIN queries matching related records through defined keys
• LEFT JOIN analysis retaining unmatched records for exception investigation
• GROUP BY aggregations calculating counts, sums, averages and percentages
• HAVING clauses filtering aggregated results against business thresholds
• Join validation using row counts, key checks and reconciliation totals
• Common table expressions structuring multi-step queries for review

Participants can summarise central tendency, variation and distribution patterns appropriately.

• Mean, median and mode selected for appropriate data distributions
• Range, variance and standard deviation measuring operational variation
• Percentiles and quartiles identifying service thresholds and customer segments
• Frequency distributions revealing concentration, gaps and unusual patterns
• Cross-tabulations comparing categories, cohorts and performance groups
• Skewness interpretation distinguishing typical results from extreme-value effects

Participants can identify meaningful changes over time and between defined groups.

• Time-series charts separating trend, seasonality, cycles and anomalies
• Month-on-month and year-on-year calculations using comparable periods
• Index numbers normalising performance against a selected baseline period
• Cohort analysis tracking groups from entry through subsequent outcomes
• Variance analysis separating actual, budget, forecast and prior-period results
• Segmentation criteria grouping customers, cases, locations or service channels

Participants can assess whether observed differences warrant management attention or further investigation.

• Sampling frames defining populations, inclusion criteria and selection methods
• Confidence intervals expressing uncertainty around estimated population values
• Null and alternative hypotheses framed from business questions
• T-tests comparing average outcomes between two defined groups
• Chi-square tests assessing associations between categorical variables
• Correlation analysis distinguishing relationship strength from causal explanation

Participants can define controlled metrics that support consistent reporting and accountability.

• KPI trees linking strategic objectives, drivers, measures and operational actions
• Metric specifications documenting formula, unit, owner and reporting frequency
• Numerator and denominator definitions preventing misleading performance ratios
• Target-setting methods using baselines, benchmarks, tolerance bands and thresholds
• Leading and lagging indicators matched to management intervention points
• Metric change control governing revisions to logic and historical comparability

Participants can design dashboards that reveal priorities, exceptions and decision-relevant patterns.

• Chart selection matching comparison, composition, distribution and relationship questions
• Visual hierarchy directing attention toward decisions, exceptions and required actions
• Pre-attentive attributes using position, colour, size and shape purposefully
• Dashboard wireframes arranging KPIs, trends, filters and supporting detail
• Axis, scale and label choices preventing visual distortion
• Annotation techniques explaining events, thresholds, assumptions and significant changes

Participants can present findings with clear evidence, limitations and recommended actions.

• Pyramid Principle structuring conclusions, supporting evidence and detailed analysis
• Executive summaries stating decision, finding, implication and recommended action
• Analytical narratives distinguishing observation, interpretation, assumption and recommendation
• Uncertainty statements describing confidence, limitations and unresolved questions
• Evidence packs linking charts, calculations, sources and audit trails
• Challenge preparation anticipating management questions, objections and alternative explanations

Participants can establish practical accountability for information assets and critical data elements.

• DAMA-DMBOK governance concepts applied to operational information responsibilities
• Data owner, steward and custodian roles defined through RACI matrices
• Critical data elements prioritised by risk, usage and decision impact
• Business glossaries defining approved terms, measures and classifications
• Data policies covering ownership, quality, access and permitted use
• Governance forums managing issues, standards, approvals and escalation routes

Participants can control definitions and shared codes needed for consistent analysis and reporting.

• Metadata registers documenting fields, definitions, formats, sources and owners
• Business metadata distinguishing terms, rules, measures and report interpretations
• Technical metadata recording table structures, transformations and system lineage
• Master data domains covering customers, suppliers, products, locations and employees
• Reference data controls managing code lists, hierarchies and valid values
• Golden record rules resolving duplicate entities across connected systems

Participants can apply lifecycle controls to information from creation through disposal.

• Information lifecycle stages covering creation, use, storage, archive and disposal
• Retention schedules assigning periods, triggers, legal holds and disposal actions
• Records classification schemes organising information by function and activity
• Disposition controls documenting approval, destruction, transfer and audit evidence
• Version and document controls preserving authoritative information records
• Archiving criteria balancing retrieval needs, storage costs and retention obligations

Participants can identify information risks and specify proportionate access and handling controls.

• Personal data identification across direct identifiers, quasi-identifiers and sensitive fields
• Data minimisation limiting collection, analysis and retention to stated purposes
• Role-based access control assigning permissions through defined job responsibilities
• Least privilege reviews removing unnecessary access to confidential information
• Data classification labels distinguishing public, internal, confidential and restricted information
• Privacy impact assessment inputs documenting purpose, risks, controls and residual exposure

Participants can prioritise, resolve and monitor recurring data-quality issues.

• Data-quality scorecards tracking defects, trends, thresholds and accountable owners
• Root cause analysis using five whys, fishbone diagrams and process mapping
• Issue logs recording defect evidence, impact, owner, action and due date
• Remediation plans separating correction, containment, prevention and monitoring actions
• Control points embedded within data capture, validation and approval processes
• Quality service levels defining acceptable defect rates and response times

Participants can plan repeatable analytical outputs with ownership, controls and review cycles.

• Analytical workflow maps covering inputs, transformations, outputs and review stages
• Refresh schedules aligned with decision cycles, source availability and control checks
• Report operating procedures defining production, review, approval and distribution
• Acceptance criteria testing accuracy, usability, timeliness and traceability
• Change requests assessing metric impact, data impact and stakeholder approval
• Implementation plans assigning milestones, owners, dependencies and escalation points

Participants can build, review and present a complete governed analysis pack for a defined management decision.

• Business case framing with decision question, stakeholders and success criteria
• Source assessment documenting lineage, access constraints and data-quality findings
• SQL extraction and transformation rules producing an analysis-ready dataset
• Metric specification defining calculations, exclusions, targets and accountable owners
• Evidence-based analysis using trends, segments, tests and documented limitations
• Dashboard and briefing pack presenting findings, risks and recommended actions
• Peer review against traceability, accuracy, governance and communication criteria