Viewpoints

RE Data Strategies – don’t let IT own the plan

Posted on May 21, 2019 in Viewpoints by Zoe Hughes

Real estate data strategies should follow one key piece of advice to succeed – it has to be owned by senior business leaders in the firm, not the IT group.

The NAREIM Data Strategy meeting, which took place in Chicago last week, heard it was critical for business leaders to take ownership of data initiatives, to clearly articulate the goals of a data strategy – and to fight for the resources needed to clean financial and operational data and to build the systems around it.

There is an insatiable appetite to use data to shape real estate acquisition decisions, but investment managers face significant challenges even just collecting and organizing their data.

Among the best practices shared at the Data Strategy meeting were the creation of  business analyst roles to act as the bridge between the business and technology, and empower them to work with and improve how acquisitions, asset and portfolio management teams access the data they use day in, day out.

Other best practices highlighted during the meeting included:

Outsourcing. When outsourcing, understand how the data will come back to you. And in what format. Specific before contracts are signed the precise format of the data to be returned and how the data will be delivered. Do as much data validation as possible upfront, the meeting heard, as contract renegotiation is almost impossible.

Accountability for data errors. Ensure someone owns each piece of data and produce regular data quality reports, highlighting data errors and the respective owner.

New data. Utilize S&P data to create tenant exposure views by parent company. Mapping by parent company also enables investment managers to use credit rating data and to standardize naming conventions for tenants.

Leadership ownership. Attendees at the meeting reinforced the need for senior leaders in the business and in the C-suite, understand what the objectives of any data strategy, to understand the questions they want data to answer, and to support the changes required to collate and clean the existing data in internal systems.