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Business Intelligence 102 for Real Estate Webinar


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Given at Realcomm, 2009, this presentation covers:

* Technical detail behind a business intelligence implementation

* Building the business case to support a comprehensive business intelligence program

*Using data mining and predictive analysis to understand potential future portfolio trends

Published in: Real Estate, Technology, Business
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Business Intelligence 102 for Real Estate Webinar

  1. 1. Business Intelligence 102 Realcomm Webinar Damien Georges Managing Director Hipercept Inc. Slide 1
  2. 2. Agenda • 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 Slide 2
  3. 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 Slide 3
  4. 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 Slide 4
  5. 5. Integrated Enterprise Analytics Environment Slide 5
  6. 6. Integration, Warehousing and Data Marts • 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 Slide 6
  7. 7. Implemented across the enterprise in a diverse vendor landscape Slide 7
  8. 8. Taking a system agnostic approach to a data model OSCRE Hybrid Approach Slide 8
  9. 9. Reporting and Analytics • 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 Slide 9
  10. 10. Building the BI Business Case 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? Slide 10
  11. 11. Building the BI Business Case – Not so fast • 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 Slide 11
  12. 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 Slide 12
  13. 13. Data Mining and Predictive Analytics for Commercial Real Estate • 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 Slide 13
  14. 14. Data Mining Process Flow Slide 14
  15. 15. Real Estate BI Solution Partners Slide 15