Data analytics for construction companies

Fuller Landau team • November 18, 2021

Data analytics is the process of extracting, processing, and analyzing large volumes of data to draw observations and conclusions.

Construction companies benefit greatly from the use of data analytics.  Transaction data (i.e., the data collected in your accounting and other systems) is analyzed to identify irregularities, potential problems, and issues to investigate further.

As a construction company, you want to ensure that your company:

  1. Obtains construction materials on a cost effective and timely basis;
  2. Prices your projects profitably;
  3. Pays your suppliers on time;
  4. Schedules your staff in the most effective way; and
  5. Protects your assets.

As a first step in applying data analytics, it is important to assess the quality of your data.  What areas of your systems have potential data quality issues?  For example, if data was recently transferred into a new accounting system, ensure that the transferred data was compared to the existing system.

You can leverage data analytics to allow construction site managers to measure and make informed decisions regarding project concerns such as cost over-runs, schedule issues and internal controls.

The following are some examples of data analytics to strengthen internal controls, and thereby protect business assets:

  1. Is there manipulation of the accounting records to hide unauthorized transactions? To test this, you could extract the general ledger detail by transaction into Excel (or preferably construction analytics software). Include all fields available such as date, account, description, and amount in your data extraction.  Analyze for unusual transactions such as round number amounts, transactions posted on holidays or on or around year end, suspense accounts, intercompany amounts, data overrides, etc.
  2. Are there ghost employees being paid? Unfortunately, ghost (or fictitious) employees could be being paid by your outsourced payroll entity or directly by your company. To test this, compare the outsourced payroll list to your employee master list, including employee name, address, and date started with the company.  Are any employees being paid that are not on your employee master list?  Are any employees being paid after their termination?  Are there any two employees with the same bank account or address?  Are any addresses P.O. boxes?
  3. Are employees or teams of employees working on projects for which your company is not being compensated (i.e., are employees “working under the table”). Depending on the systems you use, you could extract a project schedule file, including the dates, project number, team members, etc. and compare this data to hours recorded and paid by project number.  Is the data by project consistent?  Are there any unauthorized projects (unusual numbers, “suspense” projects)? All anomalies should be investigated.
  4. Are there consistencies in unprofitable or less profitable projects that need to be investigated? Assess project profitability by team, geography, and customer and conduct an analysis of any anomalous cost variations and trends.
  5. Are employees working the hours claimed? If telephone bills are available from expense reports, test use of telephone during hours worked. For example, are calls being made from Florida when the employee is supposed to be working on a project in Stratford?
  6. Are there fictitious suppliers receiving payments from your company? This may be tested by extracting the master supplier file from your system, including supplier ID, vendor name, address, phone number, and start date.  Are there any suppliers with similar names?  For example, Acme Supplier Inc., Acme Supplier Ltd., and Acme Supplier Limited may be data input errors but could mean that there are unauthorized payments being made disguised as amounts paid to legitimate suppliers.  Based on the analysis, you will identify any companies that require further investigation.
  7. Are prices of materials correct? One data analytical test would be to extract the materials master file from your system and compare the data to a supplier’s product and price lists, including price, product number, etc.
  8. Are cash disbursements for legitimate business? Test this by extracting the cash disbursement detail (cheque number, amount, payee) and look for missing cheque numbers and cross reference payees to a vendor master list.

The above is just a brief sample of potentially hundreds of tests that may be applicable to your organization.  For example, data analytics can be used to gain insight into which type of work or projects experience lower profitability and which employees worked on profitable vs. less profitable projects.

A forensic accountant can assist you by employing accounting and investigative skills to help you identify risk areas and methods of best extracting data for testing and analysis.  This will enable you to manage the activities of your company to gain greater operational efficiencies, protect assets, and improve profitability.


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