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LEASE DATA CLEAN-UP FOR A LARGE, MULTI-NATIONAL FOOD COMPANY

Lease data cleanup for MN food company

Getting ready for FASB 17 and just wanting to confirm the accuracy of the data in the lease management system, a large multi-national food company asked NTrust to perform an audit and clean-up project.

LEASE DATA CLEAN-UP AT A LARGE MULTI-NATIONAL FOOD PROCESSOR

Problem

A large multi-national food processor, distributor, and marketer’s Real Estate department had suspicions that they might have bad data in their lease management system. They suspected this based on some quick audits done on different portfolios and found discrepancies in option dates, missing terms, and problems with base-year calculations.

This lead them to contact NTrust to perform an audit, followed by lease data clean-up project. NTrust assigned a small team to perform the initial audit and identify those areas / portfolios that would need the most in-depth review of their data.

Identifying Problematic Portfolios

The client had multiple divisions that had responsibility for maintaining lease data in the central lease management system. These included:

  • Executive and Sales Office portfolios
  • Processing/Manufacturing
  • Warehousing
  • Distribution
  • NTrust decided to perform a statistical audit of each portfolio to provide guidance on how to best approach a Lease Data Clean-up project.

    The results were staggering. There was an overall error rate of 46%, of which over 80% were In critical areas such as Expenses, Key dates, Options, and Security Deposits. Based on the results of the audit, the client decided that a full Lease Data Clean-up project was warranted.

    Performing a Lease Data Clean-up project across thousands of leases

    The scope of the project was quite large, and involved leases in 23 different languages. NTrust assembled a multi-lingual team that included a project manager, a lease abstraction delivery manager, a Database Administrator (DBA), and 6 different teams of lease abstractors organized by language and geographic region.

    Since project management was going to be a key determinant of success, NTrust worked with the client to form up project teams in their organization to work with each of the abstraction teams. This approach proved to be quite successful and the integration of the teams allowed the project schedule to be reduced by over 5 weeks (a 20% reduction).

    Focusing on Project Communications

    NTrust’s management team has deep experience in running large abstraction, data analysis, and data migration projects and knew that project communications between all the participants is on the keys to success.

    To assist in accomplishing this NTrust modified its client portal, iManage, to the specific needs of this project. In addition, each property type was given its own abstraction template, which mapped directly to the client’s Lease Management system’s data structure.

    This would allow for much easier Extraction, Transformation, and Loading (ETL) of the data once the clean data was approved for loading.

    Gaining Trust by Taking Small Initial Steps

    It was clear that the size of the error issue had made an impact across the organization. NTrust wanted to assure that client resources could have confidence in the data being delivered from the abstraction teams. To accomplish this, each abstraction team performed test abstractions on a range of leases and reviewed them in detail with each of the client teams. During this process all issues with regard to lease terms and term interpretations were identified and these terms were added to NTrust’s proprietary Multi-Lingual Lease Term Data Dictionary.

    The Bottom Line

    NTrust was able to deliver clean lease data across a large enterprise by following best-in-class project management approaches in combination with proprietary tools and datasets that allow rapid delivery of accurate lease data.

    In the end, the client estimated that moving forward with the clean lease data saved them at least $10 million over the upcoming 5 year period.