Spatial Data Analytics Using Power BI Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Business Intelligence Analysts, Data Analysts and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 16 Nov 2026 Course code SD-BI-196

Before you join: Participants should be comfortable creating reports in Power BI Desktop, importing tabular data and using basic Power Query transformations.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Location changes the meaning of business data. Sales performance, service demand, asset failures, fraud alerts, supply routes and project risks often cluster geographically, yet many dashboards reduce geography to a basic pin map or regional total. Analysts need to combine spatial boundaries, coordinates and operational measures accurately, then present patterns that decision-makers can test and act upon. Poor geographic matching, unsuitable map types and misleading aggregation can produce incorrect conclusions about coverage, demand or risk.

This course develops practical spatial analytics skills in Power BI. Participants prepare coordinate and boundary data, build a usable geographic data model, select appropriate mapping visuals and write DAX measures for location-based analysis. The syllabus covers point, polygon and route data; geocoding; spatial data quality; territory comparisons; distance and catchment calculations; drill-through; tooltips; security; and publication of map-based reports. Cases reflect common requirements in banking, public services, logistics, retail, infrastructure and humanitarian operations.

Teaching combines concise technical briefings, instructor demonstrations, guided case work, group interpretation of mapped business evidence and structured report-building tasks. Participants work through the decisions behind a spatial Power BI report: what the geographic unit should be, how source data should be validated, which visual answers the question, and how results should be governed. They leave with a reusable spatial dashboard design pack containing a data-preparation checklist, geographic data model, DAX measure set, visual-selection guide and publication plan.

The course suits analysts and reporting professionals who already use Power BI and need to make location a reliable analytical dimension rather than a decorative feature. It is particularly relevant where teams plan service coverage, monitor distributed assets, compare territories, identify localised risk or allocate field resources.

What you will be able to do

  • Prepare coordinate, boundary and operational datasets for spatial analysis in Power BI
  • Design a geographic data model using fact tables, dimensions and stable location keys
  • Select map visual types using analytical questions, scale and data geometry
  • Build DAX measures for territory performance, distance bands and location comparisons
  • Analyse spatial patterns using drill-through, tooltips, clustering and comparative mapping
  • Validate geocoding, geographic hierarchies and boundary assignments before publication
  • Configure row-level security and sharing controls for location-sensitive Power BI reports
  • Produce a spatial dashboard design pack for an operational business use case

Benefits of attending

For you

  • Build map-based Power BI reports that answer territory and coverage questions
  • Recognise incorrect geocoding, boundary mismatches and misleading map aggregation
  • Add spatial measures and comparison logic to existing dashboard portfolios
  • Communicate localised patterns clearly to operational and executive stakeholders
  • Create a defensible design approach for location-sensitive reporting requests

For your organisation

  • Improve territory decisions through consistent geographic performance analysis
  • Reduce reporting errors caused by invalid coordinates and inconsistent location names
  • Identify service gaps, asset concentrations and demand hotspots earlier
  • Standardise map visual selection and geographic data preparation across reporting teams
  • Protect sensitive location data through appropriate publication and access controls

Course modules

10 modules over 5 days

Participants can frame a business question as a suitable spatial analysis requirement.

  • Spatial analysis questions framed around location, proximity, coverage and distribution
  • Point, line and polygon geometries matched to business data
  • Geographic scale effects: neighbourhood, district, region and national aggregation
  • Choropleth maps distinguished from symbol, heat and reference maps
  • Correlation, clustering and causation interpreted in geographic reporting
  • Location intelligence cases from branches, assets, deliveries and incidents

Participants can assess whether geographic source data is fit for Power BI analysis.

  • Latitude and longitude fields checked for format, precision and coordinate order
  • Address components standardised before geocoding and geographic matching
  • Administrative boundaries evaluated for source, date, scale and ownership
  • Postal codes treated carefully where boundaries change or overlap
  • Coordinate reference systems identified before combining external spatial datasets
  • Missing locations, duplicates and implausible coordinates profiled systematically
  • Data dictionaries record geographic definitions, source lineage and refresh ownership

Participants can transform raw location data into analysis-ready geographic tables.

  • Power Query profiling identifies nulls, malformed coordinates and inconsistent place names
  • Split address fields into standardised locality, region and postal components
  • Conditional columns classify records by geocoding confidence and data completeness
  • Merge queries match operational records to approved geographic reference tables
  • Custom columns create coordinate pairs, labels and location identifiers
  • Query parameters support reusable source paths and controlled refreshes
  • Data validation rules flag coordinates outside expected service boundaries

Participants can design relationships and hierarchies that support reliable geographic reporting.

  • Star schema separates transactions, locations, territories and calendar dimensions
  • Surrogate location keys prevent ambiguous joins on names and addresses
  • Geographic hierarchies organise site, locality, district, region and country
  • One-to-many relationships tested for duplicate geography and filter ambiguity
  • Role-playing location dimensions handle origin, destination and assigned territory
  • Granularity documented before joining site-level measures to regional boundaries
  • Model descriptions state geographic grain, refresh frequency and data owner

Participants can build map visuals that match the analytical question and data geometry.

  • Azure Maps visual configured for points, bubbles, heat layers and boundaries
  • Filled maps used only with appropriate administrative or custom boundary data
  • Symbol size, colour and labels scaled for accurate visual comparison
  • Map layers combine operational points with territory and reference boundaries
  • Clustering settings reduce overplotting in dense urban location datasets
  • Basemap selection considers readability, audience context and data sensitivity
  • Accessibility checks cover contrast, legends, labels and non-map alternatives

Participants can create DAX measures that compare performance across geographic units.

  • CALCULATE modifies geographic filter context for territory-level performance measures
  • DISTINCTCOUNT measures unique customers, assets or incidents by location
  • DIVIDE calculates rates using population, capacity or customer denominators
  • ALLSELECTED supports selected-area benchmarks and comparable regional rankings
  • RANKX identifies leading and lagging territories under active report filters
  • Distance bands classify locations using prepared distance or travel-time fields
  • Variance measures compare local results against targets, averages and prior periods

Participants can investigate spatial patterns without overstating what mapped data proves.

  • Drill-down follows performance from region to district and individual site
  • Report tooltips display supporting measures, trends and location metadata
  • Small multiples compare territory patterns across products, periods or segments
  • Scatter charts complement maps when testing concentration and performance relationships
  • Reference lines establish service thresholds, targets and expected operating ranges
  • Outlier investigation separates data errors from genuine localised performance issues
  • Pattern statements distinguish observed clustering from causal business explanations

Participants can analyse service coverage and territory performance using prepared spatial measures.

  • Territory assignment rules reconcile customer, site and sales ownership records
  • Catchment definitions compare straight-line distance, drive time and administrative areas
  • Coverage measures quantify demand, capacity and service availability by territory
  • Nearest-site analysis uses prepared distance tables for service allocation decisions
  • Overlap analysis identifies competing branch, depot or field-team catchments
  • Boundary changes assessed before comparing historical territory performance
  • Scenario parameters test proposed territory splits, consolidations and resource allocations

Participants can publish spatial reports with appropriate access, refresh and governance controls.

  • Power BI workspaces assign creator, reviewer, publisher and consumer responsibilities
  • Row-level security restricts territories using approved user-to-region mappings
  • Sensitivity labels applied to precise addresses, incidents and customer locations
  • Scheduled refreshes monitored for source failures and geographic reference changes
  • Certified datasets provide controlled geographic dimensions and approved measures
  • Mobile layouts tested for map usability, tooltip access and filter behaviour
  • Release checklists verify data currency, access settings and map interpretation notes

Participants can specify a practical spatial Power BI reporting solution for their work context.

  • Business decision statements define users, actions, thresholds and reporting cadence
  • Spatial dashboard wireframes link map views to supporting tables and measures
  • Data acquisition plans identify source owners, refresh methods and quality controls
  • Visual selection matrices match decision questions to geographic display methods
  • Acceptance criteria define accuracy, response time, security and usability requirements
  • Stakeholder review plans test interpretations with operational and geographic experts
  • Ninety-day application plans schedule pilot, feedback, release and adoption actions

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Spatial data preparation checklist
  2. Geographic Power BI data model template
  3. Location-based DAX measure library
  4. Map visual selection matrix
  5. Spatial dashboard wireframe
  6. Geographic report publication checklist

Who should attend

  • Business Intelligence Analysts — build location-aware reports from operational and commercial data
  • Data Analysts — prepare geographic datasets and identify actionable spatial patterns
  • Power BI Developers — add reliable mapping, DAX and security to published reports
  • GIS Analysts — translate spatial data into business dashboards for non-specialist users
  • Operations Managers — interpret territory, coverage and field-service performance reports
  • Risk and Planning Analysts — assess geographically concentrated exposure, demand and service gaps

Skills and tools covered

Spatial data preparationGeographic data modellingPower BI mappingSpatial DAX analysisTerritory performance analysisMap report governance

Tools and standards

Power BI Desktop, Power Query, DAX, Azure Maps visual

How the course runs

Training methodology

The course uses short instructor briefings followed by demonstrations in Power BI Desktop and structured case work. Participants prepare sample geographic datasets, compare map visual choices, create DAX measures and interpret mapped evidence in small groups. Scenarios include branch performance, field-service coverage, delivery delays and incident concentration. Group discussions test assumptions about geographic units, aggregation and causation. The final sessions use application planning to translate the methods into a participant's own reporting context, including data, stakeholder and publication decisions.

Requirements and prerequisites

Participants should be comfortable creating reports in Power BI Desktop, importing tabular data and using basic Power Query transformations. They should understand tables, relationships, filters and simple measures such as SUM, COUNT and DIVIDE. Prior experience with coordinates, GIS software, geocoding or spatial databases is not required; these concepts are introduced from a Power BI reporting perspective. Participants do not need programming experience, advanced DAX skills, an ArcGIS subscription or access to organisational geographic data. Familiarity with their own reporting requirements will help them apply the methods quickly.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

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

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

You should already be able to create basic Power BI reports, work with tables and relationships, and use simple measures. GIS expertise, programming and advanced DAX are not required.

Bring a laptop with Power BI Desktop installed if attending classroom delivery, plus a charger and access to a current Microsoft account where required. You may also bring a non-sensitive example of a location-based reporting problem from your work.

It is designed for Power BI analysts, developers, GIS analysts and operational reporting professionals. Managers responsible for territory, asset, service or risk reporting can also benefit if they are comfortable with Power BI fundamentals.

General Power BI courses focus on reporting, modelling and dashboard fundamentals across many data types. This course concentrates on geographic data quality, map design, spatial interpretation, territory analysis and controls for location-sensitive reports; it does not teach full GIS software workflows.

You can use the methods to improve branch and customer coverage reports, field-service dashboards, delivery analysis, incident maps or regional performance reporting. The reusable templates support a structured discussion with data owners, GIS specialists and business stakeholders.

Each day runs from 08:30 to 16:30 and provides about 6.5 contact hours. Time is distributed across ten modules covering preparation, modelling, mapping, DAX, interpretation, governance and application planning, with breaks scheduled by the instructor.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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