Dataquality in Real Estate

Introduction

With the advent of technology it has become clear that data is an important corporate asset. Performance metrics can help us in observing patterns and trends and drawing insights which, in other case, escape our attention and eye for detail.

Analysis of Key Performance Indicators and Metrics can help in improving the performance of a company. For such analysis to take place, determination of the quality of such data collected is also important. Data quality is basically an assessment whether the data collected is in shape to serve its intended purpose. Some of the key characteristics of good Data quality are accessibility, reliability, and correctness. It should be up-to-date, clean and consistent across all the business channels.

Scaling the performance

With the ever-growing competition, the need for data and to measure the performance of a corporate to have that competitive edge has increased exponentially. Companies need to do a self-performance analysis and gauge at their resources and structures in place. There is an imminent need for businesses to optimize their performance. With the economies of scale, businesses also need to take care of operating efficiencies. To improve profitability, businesses need to identify need sources of revenue as well as need to take care of their cost structures. 

But, Due to the nature of the Real Estate, a corporation can faces many difficulties in collecting data of this quantity and quality.

Access to complete, high-quality data sets as well as robust and repeatable processes are not present in CRE. Major roadblock in collection of the Data is the absence of infrastructure. Irrespective of the innovation in technology, Corporate Real Estate collectively as an industry have been late in adopting it. There are still reliant on the tools such as spreadsheets etc. This may be accounted to the fact that people at Corporate Real Estate might resisting the change as the technology will not only bring clarity to the Data, but, will also make jobs and skillset of many people redundant.

The other issue which corporation may face for maintaining the quality of Data collected in Corporate Real Estate is the reluctance of players in sharing Data. It might also be the case of Data being withheld, thereby, creating Data silos. Data silos are the data sets which are collected and recorded but not shared with other member or technical department of company.

If we take the case of project team, there might be chances that financial department of a project may not share data regarding the cost incurred during projects readily or Sales team may hide details of real time data regarding the inventory.

Due to multi-layered structure of Corporate Real Estate, Data collected to measure the performance of companies or departments on the basis of Key Performance Indicators or Metrics may be compromised.

Cash Flow is a good metrics to judge performance of any project as well as company. The Data of inflow of cash and outflow of cash is collected by both project management team as well as finance department of the company. One other source for this data can also be the contractor or firm working on the project. Due to multiple source of Data, multiple version of data might be created and these Data may have fluctuations because of the interests of the parties involved. So, this can create discrepancy in Data collected for the analysis and can in place of being useful and growth promoter, it can become harmful and we might end up taking wrong decisions.

For Data analysis to take place and to measure relative metrics, we need to have Data for that criterion. It might be the case that there is no such provision for Data records of such metrics or the team is not equipped with the same.

To capture the effectiveness of marketing channels, Data of different stages of leads funnel need to be there. But the drawback can be that marketing department of the corporation may not be keeping Data of leads generated with respect to or they might not be verifying with the sales department the number of sales qualified leads.