Interactive dashboards allow for more user interaction than simple dashboards through features like navigation, drill-down capabilities, filtering, and different display formats. They are useful for handling large volumes of data and providing insights not obvious before. Effective dashboards are designed based on use cases, group data logically, avoid overload, and consider reporting and decision-making cycles. A case study demonstrated a national project monitoring dashboard that standardized data feeds and had an intuitive interface for different user groups. Choosing the right platform depends on desired features like mobile access, social integration, and traditional or free-form analysis. Dashboards have applications in real estate, transportation, facilities management, and smart cities.

1. Interactive Dashboards
February 2016
Dr Rupert Booth, FIET FRICS FCMA PMP CEng
Chief Economist

2. Agenda
• Interactive dashboards: what & why
• Business Intelligence basics
• Case study
• Work done
• Lessons learned
• Choosing a platform
• Other applications:
• Olympics
• Asset/Facilities management
• Smart Cities
• Geographic
• Operational and Analysis Dashboards
• Summary for RICS Members

3. Definition of Interactive Dashboards
Simple dashboard is adequate for:
• Limited information
• Readers with similar skills and information needs
Typically delivered in an Excel workbook with up to a
dozen sheets
User-interaction adds:
• Navigation
• Drill-down
• Threshold control for exception reporting
• Filter control (e.g. sliders)
• Choice of display formats for naïve or expert users
Excel offers limited interaction but database solution
offers more potential

4. Potential of Data Visualisation
Use of a database increases potential of visualisation:
• Handles large volumes of data
• Increased productivity
• Gain new insights that were not obvious before
• Common vision – do you see what I see?
• Increased user interaction:

5. Need for Automation
Hand-crated ‘executive scorecards’ impractical with large data
volumes
Essential to design data flow.
• Data source, usually Line-of-Business systems
• Staging area, for receipt and cleansing of data
• Data warehouse for storage
• Data extraction to answer queries
• Data visualizing for user, often by the user, i.e. self-service
Data flow is typically web-enabled, and independent of the
Systems-of-Record

6. Initial Scoping
Demand-side: Who are the stakeholders and what
do they want?
• Organisational goals and objectives
• Personal ‘wins’
Supply –side: What data is available?
• Inventory of systems, applications and data
What technical infrastructure is available?
• Communication and storage options
Gap analysis
Initial
dashboards
Key
performance
indicators

7. First step: Identify & Classify the stakeholders

8. What are the other ingredients for success?

9. Dashboard & System Design
Base around ‘Use Cases’ (Story-boards)
• Who are the user groups
• What type of dashboard: Operational, Strategic, Analytical
• Group data logically
• Make data relevant to users
• Avoid data overload – rely on navigation
• Avoid visual clutter
• Consider reporting cycle and decision-making cycle

10. Case Study: Monitoring the National Porfolio
“Developing a national level Programs/Projects Monitoring Dashboards
on a recent project was a true challenge for Malomatia’s Analytics team,
from standardising the data structure through Service Level Agreement
governed data feeds, to having a User Interface that is intuitive,
interactive and easy to use. Developing a business-oriented dashboard
& story-board, and aligning it with a highly creative User Interface,
allowed us to report very sophisticated project data in fast and user-
friendly ways, catering for the needs of country leaders, agency heads,
project and budget analysts, and project managers“.
Khalil Khalil, Head of Analytics, Malomatia.

14. Program level screen shot

15. Project level screen shot

16. Choosing a Platform: Range of Features (Gartner)
Enable
• Business User Data Mashup & Modelling
• Internal Platform integration
• BI Platform Administration
• Meta-data management
• Cloud Deployment
• Development and Integration
Consume
• Mobile
• Collaboration and Social Integration
• Embedded BI
Produce
• Free-form Interactive Exploration
• Analytics Dashboards and Content
• IT-Developed Reporting and Dashboards
• Traditional styles of analysis

17. Gartner ‘Magic Quadrant’
Described as
Gold Standard
by Gartner
Two products,
QlikSense &
QlikView
Traditional Business
Intelligence tools

18. Beware: Not Big Data
Large volume of data does not equate to ‘big data’
Most dashboards based upon:
• Relational data based management system
• Structured data and Structured Query Language
• Record all past transactions
In contrast, Big Data:
• Typically based on Hadoop
• Not based upon data schemas
• Flexible mapping
• Pass-through data
However this traditional contrast is beginning to blur

19. Olympic Delivery Dashboard

20. Facility Management Dashboards
Typical
Typical graphical
output:
• Inter site
comparisons (left)
• Service cost trends
(below)
Typical choice of metrics:
http://dashboardspy.com/dashboards-for-facility-managers/

21. Smart City Engagement http://data.london.gov.uk/

22. Geographic Applications

23. Operational Dashboard – Real Estate
http://www.raveis.com/it_dashboard.asp

24. Analysis Dashboard – Transport Planning

25. Possibilities for RICS members?
Improve the usability of a construction dashboard
Design a dashboard from scratch
Manage a dashboard implementation
Corporate performance measurement
Web-enabled lifecycle management

26. The End…