Challenges organizations have to face while implementing Spend Analytics
Spend analytics should be a crucial part of any procurement strategy but implementing spend analytics in the strategy is not easy. It calls for a great deal of effort to make spend analytics a part of the organization. The economic conditions are making companies more responsive to implement programs that lead to decreasing the costs. Useful data is vital for the companies in order to gain benefits by reviewing it. It is essential to look below the data surface have insight into the lowest level of transactions, payments and other information, for the purpose of accomplishing new results. The challenges that companies face in implementing the spend analytics are as follows:
- Lack of understanding:
The spending data is usually incomplete, not updated, or isn’t too hi-fi to take effective decisions based on it. The companies now are realizing that they have underinvested in the ways to get the data which helps in making decisions and invested in other strategies. The companies fail to understand the need for collecting the right information, they rely on their current providers for this step but the providers actually sub-optimize the systems. The companies also need to clean and classify the data while having data enrichment. Though the manual methods of cleansing the data are time-consuming, automated programs also enable this opportunity while lacking accuracy sometimes.
- Lack of resources:
Since the traditional spend analysis systems were developed to provide local opportunities and simple strategic programs, which were not on a global scale or with complexity, for today’s spend analytic a lot of resources are essential. It starts with purchasing and strategic sourcing which needs IT to support either from the internal resources or external programs. Though the need for the IT support has been reduced, the company still needs to allocate resources to the strategic sourcing and spend analysis for the purpose of driving leverage in the organization.
- Required capabilities:
The 80% classification solution leads to incorrect strategy decisions instead of providing the necessary information to fulfill the job. The advanced companies are looking forward to expanding the scope and description of spend visibility to include other types of data like performance, risk or diversity. This data can enter the data fields which are inaccurate, incomplete or misleading. The global companies miss the opportunities for savings by not implementing spend analytics to all of its companies worldwide. The data must be collected and classified having a common data language which can include customer commodity groups or standard industry codes.
- Making finance a friend:
The finance team can prove to be allied in initiating sourcing and spend analytics. They can document progress and savings resulting from spend management while making sure that the savings are contributed to the company’s departmental budget. This ensures that savings are coming back to the organization.
- Manual classification of data:
Data classification is not without challenges and it is often difficult to find an “ideal” method for a given dataset. Datasets for each and every company is stored and structured differently. Eventually, data classification and rules get complicated and manual processes break down. Manual classification of data can be expensive and complex.