Business Intelligence 102 for Real Estate

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Business Intelligence 102 for Real Estate

  1. 1. Slide 1 Business Intelligence 102 Realcomm Webinar Damien Georges Managing Director Hipercept Inc. dgeorges@hipercept.com
  2. 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. 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. 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. 5. Slide 5 Integrated Enterprise Analytics Environment
  6. 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. 7. Slide 7 Implemented across the enterprise in a diverse vendor landscape
  8. 8. Slide 8 Taking a system agnostic approach to a data model OSCRE Hybrid Approach
  9. 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. 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. 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. 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. 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. 14. Slide 14 Data Mining Process Flow
  15. 15. Slide 15 Real Estate BI Solution Partners

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