Is your corporate real estate data ready to inform the future?

Corporate real estate (CRE) creates massive volumes of data every day, and a purposeful business intelligence (BI) strategy can help you put this information to good use. But is your data ready to serve as the foundation for future CRE decision-making?

The answer depends on the four Cs of your data quality.

1. Is your data complete?

Incomplete data leads to imperfect outcomes and missed opportunities. To make smart, informed decisions, you need a full set of data—not insufficient information that tells only part of the story. Otherwise, your analysis and decisions may be skewed, and generalizations may be inaccurate.

If you’re going to draw business conclusions from your data, then you need the complete picture. For example, how can you determine whether building occupancy levels have increased or decreased if you only have data from the last year? If you don’t have the complete set of expected data due to manual processes, spreadsheets, or lack of implementation across your portfolio, then you’ll waste valuable time and effort trying to assemble everything you need and structure it in a way that makes sense.

2. Is your data current?

Relying on outdated data is like predicting a storm after it’s already raining. Within the world of CRE, obsolete information can result in outages and downtime, lost time and money, neglected maintenance, and unhappy workers. Little value comes from decisions made based on old information.

To determine your team’s current work order completion rate, for instance, it’s vital to know how many work orders have been received and completed in the past few months instead of using data from last year.

To make the most of your data, it’s necessary to be clear about the types of data you work with. Can you easily source, select, and tell the difference between:

  • Current data, which needs to be clearly marked as such?
  • Historical data, which requires you to determine how much data you need—and from how far back?
  • Plan data, which is often the least automated?

3. Is your data consistent?

As it moves across a network, the data you see should be the same data that other decision-makers see, too. If you’re making decisions about energy-efficiency initiatives, then the energy cost information you view should align with the energy cost information the accounting team sees. This way, everyone works with the same information.

To ensure reliability, data must also be captured in a consistent manner. For example, the way you identify and refer to specific workspaces for space-utilization purposes should sync with how workspaces are distinguished when it comes to workplace density.

But consistent data isn’t only about standard definitions; it’s also about clarifying audiences and use cases as well as defining which presentation methods are best to ensure consistency for the audience that needs the information.

4. Is your data correct?

Once you can be confident that your data is complete, current, and consistent, then it’s time to focus on its accuracy. This often involves audits or field-checks. Are those properties still active? Do those seats still exist? To ensure correct data, you need people and processes to make sure your records reflect what’s really happening on the ground.

If your data isn’t correct, then business disruption is likely to follow. Inaccurate data can undermine your business goals and create new problems—from flawed predictions and ill-informed choices to wasted resources. A Gartner survey estimates that bad data costs the average U.S. business $15 million per year.

When decisions are being made based on the narrative your data reveals, then it’s critical that the information is correct. Maybe your data reveals that project cycle times are within range—even though they aren’t. If you use this inaccurate data to make decisions about upcoming projects and timelines, then the mistake can cost you valuable time and money while setting the stage for future project failure. The same goes for inaccurate data around operations, facilities management, space management, and sustainability.

Does your data make the cut?

Not sure whether you’re dealing with complete, current, consistent, and correct data? JLL Technologies can help. We’ll quickly identify BI and data strengths, gaps, opportunities, and implications, so you understand the challenges you face and how to improve data quality.

Ready to talk to a real estate analytics expert? Contact us.