Data Analytics Fundamentals for Data Literacy and KPI Interpretation Training Course

Course overview

Many managers and business professionals receive dashboards, operational reports and KPI packs without being able to test whether the figures answer the business question, use sound definitions or support a defensible decision. A sales conversion rate can improve because the denominator changed; an on-time delivery KPI can conceal cancelled orders; a monthly average can hide a deteriorating customer segment. This course equips participants to move beyond reading charts at face value and to question, interpret and communicate data with confidence.

Participants learn the practical data analytics workflow: framing a decision question, identifying reliable measures, inspecting data quality, cleaning and structuring data, calculating descriptive statistics, analysing trends and variance, and selecting visualisations that show material findings. They work with Excel, SQL and Power BI to examine datasets and KPI definitions, distinguish leading from lagging indicators, interpret targets and thresholds, and recognise common analytical errors including misleading aggregation, selection bias and correlation-versus-causation claims.

Instruction combines short, focused demonstrations with guided analysis of realistic operational, customer and financial datasets. Participants build a KPI dictionary, write basic SQL queries, create a Power BI report and present a concise evidence-based recommendation. They leave with a completed KPI interpretation pack containing documented metric definitions, data-quality checks, analysis outputs, dashboard visuals and an action-oriented management briefing that can be adapted to their own function.

The course is designed for professionals who regularly consume, prepare or challenge business data rather than specialist data scientists. It is especially valuable for analysts, managers and functional leads who need to turn reports into better operational and commercial decisions.

Course objectives

By the end of this course, participants will be able to:

• Define decision-focused analytical questions and translate them into measurable KPI requirements
• Create a KPI dictionary specifying formulae, data sources, owners, refresh frequency and interpretation rules
• Profile tabular data in Excel to identify missing values, duplicates, outliers and inconsistent categories
• Write basic SQL SELECT, WHERE, GROUP BY and JOIN queries to extract and aggregate business data
• Calculate rates, averages, medians, variance and period-on-period change for KPI interpretation
• Distinguish leading, lagging, diagnostic and vanity metrics when evaluating business performance
• Build a Power BI dashboard with appropriate charts, filters, KPI cards and explanatory annotations
• Present a data-backed management recommendation that states assumptions, limitations and next actions

Benefits of attending

Target competencies

Who should attend

• Business Analysts — who translate stakeholder questions into reports and requirements
• Operations Managers — who monitor service, productivity and quality KPIs
• Finance Analysts — who interpret budget, cost, revenue and variance reports
• Sales and Marketing Managers — who need to assess funnel, campaign and customer performance
• Project Managers — who use delivery metrics to identify schedule, resource and risk issues
• Product Owners — who must use usage and outcome data to prioritise improvements

Requirements and prerequisites

Participants should be comfortable using a computer, working with spreadsheets and interpreting basic business measures such as totals, percentages and monthly targets. Prior exposure to Excel tables, filters and simple formulae is helpful, as the course uses these features during data-quality and KPI exercises. No prior SQL, Power BI, statistics, coding, data modelling or dashboard-design experience is required; these are introduced from first principles in a business context. This is a fundamentals course at an intermediate professional level: complete beginners should expect a structured but practical introduction and should be prepared to work with datasets throughout the week.

Training methodology

The instructor uses worked examples to demonstrate each stage of a business analytics workflow, then participants apply it to progressively richer datasets in Excel, SQL and Power BI. Exercises include auditing a flawed KPI pack, profiling customer and operations data, calculating performance measures, and diagnosing variance through segmentation. Small groups compare interpretations and defend their conclusions against ambiguous evidence. Each day closes with a practical output, and the final session develops an individual application plan that identifies a live workplace KPI, its data source, its decision owner and the first analysis to perform.

Course outline

Day 1: Data literacy and decision questions

• Business decisions, analytical questions and measurable outcomes
• Data types: categorical, numerical, date and identifier fields
• Measures, dimensions, granularity and units of analysis
• KPI anatomy: numerator, denominator, scope and time period
• Leading, lagging, diagnostic and vanity metrics
• KPI targets, thresholds, baselines and tolerances
• Common dashboard interpretation errors and misleading claims

Workshop: Participants audit a sample executive KPI pack and produce a list of clarification questions, calculation risks and decision implications.

Day 2: Data quality and spreadsheet analysis

• Data provenance, ownership and refresh-cycle checks
• Missing values, duplicates and inconsistent category labels
• Outlier detection using sorting, filters and conditional formatting
• Excel Tables, structured references and data validation
• PivotTables for grouped summaries and KPI drill-down
• Excel formulae for rates, variance and period comparisons
• Data-quality issue logs and remediation priorities

Workshop: Participants profile an operational dataset in Excel and create a data-quality log with prioritised fixes and a cleaned analysis table.

Day 3: Querying and analysing business data

• Relational data concepts: tables, keys and relationships
• SQL SELECT, FROM and WHERE for targeted extraction
• SQL GROUP BY, COUNT, SUM and AVG for KPI aggregation
• INNER JOIN and LEFT JOIN for combining business records
• Date filtering and period-based performance comparisons
• Descriptive statistics: mean, median, range and distribution
• Segmentation and drill-down to explain KPI variance

Workshop: Participants write SQL queries to investigate declining customer retention and produce a segmented KPI summary with an initial diagnosis.

Day 4: KPI visualisation and dashboard interpretation

• Selecting chart types for comparison, trend, composition and distribution
• Power BI data import and field data types
• Power BI relationships and star-schema fundamentals
• Measures and calculated columns for KPI reporting
• KPI cards, line charts, bar charts and matrix visuals
• Filters, slicers and drill-through for controlled exploration
• Visual integrity: scales, labels, annotations and accessible colour use

Workshop: Participants build a Power BI performance dashboard that compares actuals, targets and variance across business segments.

Day 5: Interpretation, communication and workplace application

• Correlation, causation and confounding factors
• Sampling bias, selection bias and survivorship bias
• Confidence limits and appropriate language for uncertain findings
• Root-cause hypotheses and evidence-testing plans
• Management briefing structure: finding, implication, action and owner
• KPI dictionaries and metric-governance responsibilities
• Personal analytics application planning for a live business question

Workshop: Participants complete and present a KPI interpretation pack containing a metric dictionary, analysis, dashboard view, recommendation and 30-day workplace action plan.

Tools & standards covered

Microsoft Excel, Microsoft Power BI, SQL, DAMA-DMBOK

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions