Analytics Strategy

The Real Estate Analytics Strategy Document

Here we provide an outline for a real estate analytics strategy document for a company that wants to arm all of its departments with metrics and dashboards.

Vision

This section describes how data and analytics capabilities will be used to drive your business over the next 1 to 3 years

• Drivers / reasons for moving away from status quo
• Current State vs Future State
• Executive sponsor (who is the executive championing the change?)

Business Case

This section explains the costs and benefits for investing in analytics

• Justification for the investment
• Investment required
  • Technology and tools
  • Data integration
  • Implementation
  • Training
  • Technology sustainment
• Benefits realized
  • Better decisions (breadth, depth)
  • Faster decisions
  • Team productivity
  • Intrinsic benefits
• Cost of maintaining status quo

Change Management Plan

This section identifies the departments and roles that will be impacted, what will change for them, and how they will transition to the new analytical processes

• Introducing a new way of making decisions is going to be disruptive and will be met with resistance
• Whose interests are positively and negatively impacted, and the response that should be anticipated from them
• How resistance will be addressed and buy-in obtained
• The plan for training the users and supporting them after going live with the analytics capabilities

Analytics Roadmap

This section lists the capabilities and benefits to be delivered over the next one to three years

• Examples of capabilities include: Delinquency Management, General Ledger Analytics, Collections Dashboard etc.
• Each new capability is treated as a project
• The approximate timeframe for the start / end dates for each project

Technology Architecture

This section describes the technology components that will be built or acquired, and how these components will work together to deliver the required capabilities

• Data visualization tools (Tableau, Power BI, Excel etc.)
• Data sources (PMS, CRM, Excel workbooks, Investment Portal etc.)
• Data extraction and data refresh methods (API, DB Backups, Email Exports etc.)
• Data storage and growth (cloud vs on-premise, Amazon vs Azure, scale and size)
• Information protection and compliance (data privacy policies, security policies, disaster recovery, data quality assurance etc.)
• System administration (system monitoring, problem detection, patches, shutdown/restarts)
• Tech support (end user support levels, help desk, centers of expertise etc.)
• Build vs Buy vs Subscribe (ownership of code, software licenses, cloud service agreements etc.)
• Insource vs Outsource the skills required (dashboard developers, database administrators, project managers, business analysts, software architects, trainers etc.)

Appendix

• A list of documents and links supporting the above information