Business Intelligence 102 for Real Estate

Business Intelligence 102 Realcomm Webinar Damien Georges Managing Director Hipercept Inc. [email_address]

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

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

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

Integrated Enterprise Analytics Environment

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

Implemented across the enterprise in a diverse vendor landscape

Taking a system agnostic approach to a data model OSCRE Hybrid Approach

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

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

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

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

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

Data Mining Process Flow

Real Estate BI Solution Partners

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Business Intelligence 102 for Real Estate

by dailena on Nov 20, 2009

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Business Intelligence 102 for Real Estate — Presentation Transcript