A Checklist for Implementing Data Analytics in Commercial Real Estate
This blog post is the second of a 2-part introductory series on Data Analytics in Commercial Real Estate. In part 1, we showed that data analytics leverages verified data to reach decisions rather than using intuition or guesswork and is core to driving strategic business decisions.
Implementing or improving a data analytics program in commercial real estate requires upfront planning and several critical steps. Here’s a quick checklist to follow:
☑️ Assumptions and Biases - Much of our decision-making is unconscious, which makes it difficult to share and validate the logic we have used when we reach a decision. Take additional steps that will increase your team’s awareness of potential biases that may creep into the decision-making process.
☑️ Define Your Business Objectives (Outcomes) - To get the most out of data analytics, define your business objectives before implementing a system or conducting any data analysis. Define objectives that can be measured with Key Performance Indicators (KPIs).
☑️ Define the Problem You Must Solve to Achieve Your Objectives - Once your objectives are set, you can start to define the specific problems you need to solve by asking the right questions. This will help the team focus on collecting and analyzing the appropriate data for solving the problem identified.
☑️ Identify Your Data Requirements and Sources - Gathering and managing data is as important as asking the right questions. One approach is to specify the ideal data set and embark on acquiring or creating a source for it. Another approach, which is likely to be more cost effective, is to find out which data sets are already available within existing enterprise and building systems, and to explore their potential.
☑️ Select Your Data Analytics Platform - When considering platforms for analytics, you have many options to choose from. There are general purpose enterprise Business Intelligence (BI) tools that are highly versatile, and there are unique solutions dedicated to solving specific problems. Make an informed decision by understanding the options available and the trade-offs involved before dumping data into a commercial BI software tool and starting to make charts.
☑️ Present Results in a Compelling Way - Performing analysis and gleaning insights are exciting first steps, but sharing this information will require telling the story in a simple yet power way. The presenting format used should best communicate the information that will assist teams in making good, data-driven business decisions.
☑️ Adopt a Continuous Improvement Process - Data analytics is a journey, not a destination. It’s important to develop a continuous improvement process. Periodically review your achievements as well as identify opportunities to expand and focus or re-shape the program to increase its utility to support decisions.
Learn more about data analytics by downloading the guide below.