1. Slide 1
Business Intelligence 102
Realcomm Webinar
Damien Georges
Managing Director
Hipercept Inc.
dgeorges@hipercept.com

2. Slide 2
• Overview
• Data Integration, Data Warehouse and Data Marts
• Reporting and Analytics
• Building the BI Business Case
• Possibilities for Data Mining and Predictive Analytics in Commercial
Real Estate Portfolios
Agenda

3. Slide 3
Overview
• Exploring the technical detail behind a BI implementation
• Building the business case to support a comprehensive business
intelligence program
• Using data mining and predictive analysis to understand potential
future portfolio trends

4. Slide 4
What Should a BI Solution Provide?
• Data transparency, allowing drill through from summarized information
down to the underlying detail
• A platform for monitoring and enforcing data quality standards
• Resiliency to underlying system change
– As underlying transactional systems change the users of the BI
platform are shielded from that change
• Graphical representation of analytics providing immediate
understanding of business trends
• A platform for orchestrating the movement of information between
systems
• A time sensitive view of information across systems

5. Slide 5
Integrated Enterprise Analytics Environment

6. Slide 6
• Data Integration/Warehousing solutions are comprised of:
– Data Dictionary
– Logical Data Model
– Physical Data Model
– Data Quality
– Data Synchronization
– Data Movement Capabilities
• Make sure this is implemented along with a data governance
mechanism and an ongoing monitoring program that ensures
consistent data quality
Integration, Warehousing and Data Marts

7. Slide 7
Implemented across the enterprise in a diverse vendor landscape

8. Slide 8
Taking a system agnostic approach to a data model
OSCRE Hybrid Approach

9. Slide 9
• Reporting in the complex world of commercial real estate can be characterized
as follows:
– Most companies use several dozen Excel spreadsheets to analyze and
report data
– Data is typically scattered in multiple and disparate sources
– “Plain vanilla” reports such as Balance Sheets and Income Statements
are relatively easy to produce at an aggregate level but more detailed
reporting can take weeks to pull together
• The solution
– Find an implementer and vendor who can be relied on to give you what
you really need based on true business requirements
– Consider standardizing on a single technology stack
– Make sure your internal resources understand what the vendor is doing
Reporting and Analytics

10. Slide 10
Building the BI Business Case:
• Quantify Cost Savings
– Interview business users to understand the time it takes to produce the
current reporting and analytics within your organization
– Apply an internal hourly rate
• Quantify BI implementation and ongoing costs
– Consulting costs, infrastructure costs, internal costs
– Training costs
• Determine ROI/Payback
• Simple, right?
Building the BI Business Case

11. Slide 11
• Simple ROI business cases only work in environments where there is a
general consensus that BI is an essential part of the overall organizational
architecture
– Understanding that a transactional system is not a good basis for a data
warehouse
– A system agnostic data and reporting platform is critical to maintaining
business operations
– A potential for expanding to additional asset classes to get a true picture
of an overall investment portfolio
• The qualitative components behind the BI Business Case are
unfortunately the most compelling for implementing an end-to-end
infrastructure
Building the BI Business Case – Not so fast

12. Slide 12
Business Benefit of BI
• Lowers operating costs as a result of eliminating manual
process
• Reduces the chance of reporting errors
• Improves the speed and efficiency at which a company can
determine specific exposure and risk, improving overall
business agility
• Streamlines operations by automating and standardizing the
aggregation of information from various entities irrespective of
geography, technology or business model
• Establishes an architecture that will support future growth
including additional assets in existing entities, new products and
new platforms

13. Slide 13
• Used forever by insurance companies to build risk and premium
models
• Takes historical information to predict future trends
• Requires a robust data environment (multidimensional) to be able to
support the analysis
• Technical resources must be able to determine the application
algorithm to apply to a data set
• Results must be aligned to significant macro indicators – examples:
– Economic environment (inflation, employment, rate of economic
growth)
– Regulatory environment
Data Mining and Predictive Analytics for Commercial Real Estate

14. Slide 14
Data Mining Process Flow

15. Slide 15
Real Estate BI Solution Partners