Business Intelligence for Decision Support: Metrics, Dashboards and Reporting Governance Course

Programme Summary

Business intelligence for decision support is the subject of this five-day training course for managers who sponsor, use or oversee reporting, ending in a metrics and dashboard specification for one decision area. Organisations hold large volumes of data, yet leaders often receive figures that conflict, arrive late or answer no decision. The programme moves from decision needs and metric design, through data models and dashboard design, to reporting governance, then to a worked specification. Mars Training Centre UK delivers this programme in business intelligence for decision support.

The Business Case

When reports are built before the decision is defined, managers spend meetings reconciling numbers instead of acting on them. Leaders who fix the decision, the metric definition and the data owner first can retire unused reports and rely on figures that every function accepts, which shortens the path from question to action.

• Decision Cycle Time: Shortens as one agreed set of metric definitions removes the reconciliation of competing figures.
• Reporting Effort: Falls as unused reports are retired and self-service reporting replaces repeated requests.
• Data Trust: Rises as ownership, quality checks and change control are applied to reporting data.

Principles and Evidence Base

• Balanced scorecard: Its four perspectives link each metric to strategy, and shape the metric selection in Session 2.
• Kimball dimensional modelling: Fact and dimension tables structure the reporting models examined in Session 3.
• DAMA-DMBOK: Its data quality and data governance knowledge areas ground the controls designed in Session 4.
• Pre-attentive visual perception: How readers notice position, length and colour guides the dashboard layouts reviewed in Session 3.

Intended Participants

• Managers who commission dashboards and reports and must judge whether they support decisions.
• Heads of function who define performance metrics for their own area and report them upward.
• Programme leads who coordinate analysts, data owners and technology suppliers.
• Finance, operations and strategy staff who consolidate figures for management and board packs.

The programme serves banking, healthcare, retail, logistics and public service organisations.

Learning Outcomes

1. Participants will be able to translate a management decision into information requirements and a set of candidate metrics.
2. Participants will be able to construct precise metric definitions with an owner, a source and a calculation rule.
3. Participants will be able to evaluate a reporting model for grain, dimensions and consistency of measures.
4. Participants will be able to critique a dashboard for clarity, audience fit and misleading design.
5. Participants will be able to diagnose data quality and access risks in a reporting chain.
6. Participants will be able to justify a metrics and dashboard specification to a steering group.

Programme Structure

Day 1: Decisions and Information Needs

Which decisions must reporting support, and what does each one need to see?

• Decision Inventory and Owner Map
• Information Requirement Statement
• Descriptive, Diagnostic and Predictive Question Types
• Report Usage and Retirement Review
• Business Intelligence Maturity Self-Assessment

Day 2: Metrics and Performance Measures

Which measures describe performance, and how are they defined without ambiguity?

• Balanced Scorecard Perspective Mapping
• Metric Definition Sheet
• Leading and Lagging Indicator Pairing
• Target, Threshold and Tolerance Setting
• Metric Conflict Resolution Log

Day 3: Reporting Models and Dashboard Design

How are data arranged and displayed so that figures stay consistent and are read correctly?

• Fact and Dimension Table Design
• Grain and Conformed Dimension Check
• Data Warehouse and Data Mart Options
• Visual Encoding Choice Guide
• Dashboard Layout Critique

Day 4: Reporting Governance and Data Trust

Who owns the figures, and what controls keep them reliable and secure?

• Data Owner and Steward Role Matrix
• Data Quality Rule Catalogue
• Self-Service Reporting Guardrails
• Report Change Control Procedure
• Access and Sensitive Data Review

Day 5: Specifying Business Intelligence for One Decision Area

Is the specification ready for a steering group to approve and a team to build?

• Applying the Session 1 Information Requirement to the Chosen Area
• Testing the Session 2 Metric Definitions Against Source Data
• Checking the Session 3 Model and Layout Choices
• Applying the Session 4 Ownership and Quality Controls
• Completing the Metrics and Dashboard Specification

Methodology

The programme is built around data workshops. Participants work in small teams with anonymised reporting extracts from retail, healthcare or banking settings and treat them as a sponsor would: questioning definitions, testing totals and redesigning screens. No programming is required.

• Suggested activity: Reconciling two conflicting versions of the same metric and agreeing one definition.
• Suggested activity: Critiquing a live-style dashboard and redrawing it for a named decision maker.
• Suggested activity: Writing data quality rules for a reporting table and assigning an owner to each.

Capstone Output

Each participant completes a metrics and dashboard specification for one decision area. It holds the decision list, metric definition sheets, a reporting model outline, a dashboard layout and a governance plan with owners and controls. The specification is used to brief an analytics team or supplier and to seek steering group approval.

Frequently Asked Questions

Is business intelligence for decision support suitable for managers without a technical background?

Yes. Business intelligence for decision support is set at managerial level and assumes familiarity with management reports, not database design or programming.

Which models and standards does business intelligence for decision support rest on in this training?

Business intelligence for decision support rests on the balanced scorecard, Kimball dimensional modelling and the DAMA-DMBOK data quality and governance knowledge areas.

How is the specification from business intelligence for decision support used at work?

You use the specification to brief analysts or suppliers, to seek steering group approval and to set agreed metric definitions and controls before reporting work starts.

How does business intelligence for decision support differ from a data science programme?

Business intelligence for decision support centres on defined metrics, consistent reporting models and dashboards for current performance, while a data science programme centres on predictive models and pattern finding.