Collecting baseline Finance data

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In a previous post on the challenges of collecting baseline data for use in benchmarking, I covered five points to overcome in the collection of data.  In this post, I'll capture a few quick points to keep in mind when collecting data.

  1. Collect data based on a predefined taxonomy.  A taxonomy is simply the classification of activities in an organization.  For example, in Accounts Payable, some of the activties that would be defined include invoice receipt, invoice entry into the A/P system, printing the check or creating the EFT, and sending the payment.  It's important that data be collected in the same way it will be compared to the benchmark data.  Otherwise you'll have an Apples to Oranges comparison.  So it really makes sense to understand the data set for benchmarking that you'll be comparing your baseline data with.  Many industry organizations have benchmark data available to their members.  Many consulting organizations also have benchmark data for use.
  2. Define FTEs in terms of activities, not titles.  I touched on this in a previous post, but it can't be emphasized enough that the FTE and cost capture needs to be based on activities, not titles.  If you have an Admin Assistant in a Business Unit that is responsible for opening all the mail and forwarding vendor invoices to the B.U.'s payables group. then that person is a partial FTE for Accounts Payable.  This is a common cause of missing FTE, and the related costs, during a baseline exercise.  Spread over a large organization, these discrepancies can be substantial.
  3. Scrub data and perform additional research.  There are certain guarantees in life:  Death, taxes, and that cost data submitted will be incomplete and flat out wrong.  You'll ask for cost data in one format and you'll get it in another.  Departments will combine different categories of FTE and costs that you wanted broken out.  They'll leave blanks when common sense tells you they must have someone performing that activity.  Your job is to identify all of these possible discrepancies and track them down.  In a perfect world you wouldn't have to do it, but hey, we all know how that works.

By keeping these three points in mind, the quality and comparability of the data you collect will be greatly enhanced.