Conversations about big data are as common as conversations about pets, family and vacations. Yet, how to leverage big data seemingly remains a big mystery.

Solving that mystery was the goal of the "How to Leverage Big Data for Big Results" session at NAA's Apartmentalize in 2019. The panel of industry experts outlined how to form a data strategy to effectively gather, analyze and implement decisions from big data.

It starts with understanding what data sets are available to rental housing owners and operators. There are numerous traditional and non-traditional data sets to analyze, including marketing, benchmarking, historical, financial, market research, survey and Google analytical data.

Before embarking on an analysis of any data sets, rental housing operators have to think about the business first and what they want to achieve.

"The number one lesson is to start with the business in mind," said Darren Wesemann, Executive Vice President and Chief Information Officer at Berkadia. "If the operating business ever feels like this data group is going to roll something off the assembly line and it's going to be this wonderful thing, the problem with that is that never actually happens."

Then, according to Wesemann, you have to aggregate and clean the data so you or your technology can conduct the appropriate analysis. "Most of the work we do with data is pulling it together and cleaning it up," he said.

Aggregation is followed by data engineering, which is deciding how to use the data that has been gathered. Last but not least is the data science stage, in which you use people and technology to render insight and make decisions.

"I spend a lot of time asking questions about what are we trying to achieve so that we can create metrics to disseminate down to the team, and make sure we are hitting our goals and make us more efficient," said Diana Norbury, Senior Vice President of Multifamily Operations for Pillar Properties. "Oftentimes, that process is messy and not perfect, but so long [as] you are doing it regularly and doing it over and over, you look back and see how it did help you and you accomplished a lot. You just have to be consistent."

To ensure the success of a big data program, it's critical to consider digital intensity and transformation management. Digital intensity is the degree to which a business applies the forms of information technology to their business. Transformation management is how well an organization adapts to change. According to an MIT study, firms that are the most mature in these two practices generate 26 percent more profit than their industry competitors.

To become more mature in these two arenas, property managers must invest in the people, processes and technology that support digital intensity and transformation management, according to the panel. Calculating the ROI of those investments is critical.

"Try to measure everything you can," said Gino Ferro, Vice President of Ancillary/Procurement at Bridge Property Management. "Use quantitative and qualitative pieces together and find the right balance for your business."

The investment might involve leaning on vendor relationships if you're a smaller company.

"We heavily rely on our vendor partners to provide certain skillsets," Norbury said. "We do the work we can and then go to our partners and say, 'this is what we're trying to achieve, can your product do that?'"

Relying on vendor relationships is often necessary when incorporating machine learning and predictive analytics into decision-making processes. Machine learning allows rental housing providers to define a model and train it with data that isn't stored. The model can then help predict a future event, such as future vacancy or occupancy rates.

Machine learning models don't store any information but become more intelligent the more they are used. Because these models don't store data, they reduce privacy concerns. An example of machine learning is when Google Maps shares that it will take five minutes to get to a destination. 

Predictive analytics through machines could be a game-changing way to solve the mystery of leveraging big data to reduce costs and ultimately increase revenue and NOI for property management firms.