Greg Peterson of TPI's Financial Analysis Services Group will be "blogging about the bottom line" this week.
Striking the balance between data accuracy and time spent data collecting, prior to entering into a sourcing contract, helps determine risk down the road.
When assessing a potential sourcing opportunity, a significant amount of effort is involved in assembling the required financial data to support RFP development and the evaluation process. Much of this effort involves building a financial base case that represents the current spend for the in-scope services, along with the underlying "units of consumption" or resource volumes that the base case spend accommodates.
Clients often weigh the trade-off between time spent collecting, organizing, and validating data versus its accuracy. Relevant data is often not as readily available as clients may hope, which may be due to a variety of factors:
- Financial systems may not capture costs in the required format or to enough granularity (e.g. functional tower splits or expense categorization)
- Poor asset management tool or systems
- Required volumes simply not tracked or reported
- International data not readily available in the same format
- Costs may span multiple geographic regions or business units
Due to these issues, clients need to invest more time than may be desired to pull together the base case and resource volumes. While it is unreasonable to think the data will ever be perfect, the decision often comes down to whether to invest the time now or later to get the data to a reasonable level of accuracy.
With the "later" approach, you initially build the base case and resource volumes quickly and then let further refinements get sorted out throughout the due diligence and contracting process. While this path may lead to a quick release of an RFP, the client project team could lose credibility with internal stakeholders or service providers if the underlying data constantly changes (in addition to the added time spent processing those changes). There could also be financial risk to the client if the data never reaches an appropriate level of accuracy, as decisions could be made based on a faulty business case.
The "now" approach is to get the data fairly accurate in the beginning, which minimizes revisions down the road. This approach requires more time and discipline upfront, but can save time on the back end of the process. To keep this approach from lengthening the process indefinitely, the client needs to avoid the objective of getting the data "perfect". It is unreasonable to think that complete accuracy can be achieved and there are diminishing returns once the data gets to a tolerable level of accuracy.
Regardless of the approach, it is also good to keep in mind that one of the positive by-products of investing any effort in this area is the scrubbing and organizing of data. Even if the process does not lead to sourcing, the client has better data available for reporting, analysis, and planning.
