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Home » Blog » Spend Analysis » Evolution of Spend Analysis and the Rise of Big Data: Part 4

Evolution of Spend Analysis and the Rise of Big Data: Part 4

Evolution of Spend Analysis and the Rise of Big Data
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Implementation is a tricky stage. If things go wrong it can adversely impact the investment made in the technology. In this blog we will talk about the common constraints that organizations face while implementing spend technology.  (Click here to read part 1,   part 2 and part3  of the series.)

Although spend analysis is found to be very useful by most corporations, there are a number of constraints against implementation of the process of spend analysis (Aberdeen Study).

Some of these constraints are examined here:

  • Spend analysis is a time-consuming process. It involves a lot of man hours to complete the analysis. Usually, several weeks are required to complete the exercise.
  • Information required for carrying out the spend analysis is to be collected from various sources, both from within and from outside the enterprise. The sources within the enterprise are Accounts Payable (AP), General Ledger (GL), Purchasing Department and Enterprise Resource Planning (ERP) system. Outside sources of information include Credit-and-Procurement Card (P-card), Banks, Contract Manufacturers, Logistics Service Providers, etc.
  • Due to limited financial data from the available internal financial system, only partial information is available for decisions to be made. Therefore, decisions lack quality.
  • The data in the ERP system and the company’s financial system is unstructured, with occasional errors or lack of critical data. For example, the name of a vendor, product information and account codes may be incomplete or even wrong.
  • Correction of such data errors is not satisfactory due to lack of expertise with the personnel working in the data section. Most of the data processing is done by the staff of the IT department which lacks familiarity with the commodities or with the services under review.
  • Classification of the spending information is not done correctly. In addition, the “miscellaneous” category creates difficulties in analyzing the data.
  • Mistakes in part numbering or in item descriptions, or of suppliers is usually a hurdle against effective spend analysis. The name of the same item or of the same vendor may be written differently in different records. Under such conditions, the spend analysis cannot be fully relied upon.

Having talked about the constraints in spend analysis implementation, in the next blog we will talk about the best practices.

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