Spend analysis is fast becoming a popular tool for business both large and small worldwide. It helps identify areas for cost saving and opportunities for streamlining processes. Both of these aims in turn, drive to increase profitability and increase organizational efficiency. While spend analysis is fast becoming an essential in multiple industrial sectors, it does have its pitfalls that you need to be aware of. After all, there is nothing like conducting spend analysis and then receiving outdated or inaccurate solutions to your organizational woes.
Here are some classic pitfalls of data that negatively impact the spend analysis process:
Here are some classic pitfalls of data that negatively impact the spend analysis process:
Pitfall #1: Too Much Information
You may have heard TMI being thrown around social media and the like, but now it has come in to the world of business as well. Too much information about products, services, suppliers, and supply chain processes can complicate things instead of simplifying them. All spend data needs to be analyzed, so the more data you have, the more you will need to process. There is also an increased chance of identifying wrong trends through data that is overly descriptive – for example “red cotton towel with football print” instead of “item 201”.
Pitfall #2: Non Standardized Data
Here is another important item even large businesses forget easily. With the increasing use of computers and mobile devices by managers, customers, and sales personnel, it is only natural to deal with multiple languages, sources, and formats. Any products or services that are new, or discontinued may end up without material codes. Manual override systems built into most software allow selling or even ordering such items without the complete category or description. Furthermore, managers or employees are free to create their own product categories and classes stepping outside your company’s regular catalogue. This makes the sorting of data tedious and can even make you miss out on vital information.
Pitfall #3: Unable to Standardize Processes Efficiently
Research indicates that just over 20 percent of all enterprises using spend analysis utilize automated software. This means that despite the available technology, most organizations still rely on using antiquated single line entry spreadsheets to process massive amounts of data. This poses two problems; first it takes a lot longer to go through the relevant information. Second, any similar categories or products are spread around the sheet with no means of consolidating the data easily with a single click.
Pitfall #4: Other Cautions
It goes without saying that bad quality data or incomplete data will also negatively impact the spend analysis process. In fact, this could lead to overlooked opportunities and issues that crop up with too many suppliers. This process is not something that is to be taken lightly; 70 percent of all organizations that use spend analysis tools report improvements within the first quarter after implementation. But make sure the data is accurate, it doesn’t hurt to verify and validate as you go along.
Summing All Up
The reliability and validity of spend data must be assured from all angles before the data analysis process can begin. Any spend data that is not up to the mark, or not properly sorted or categorized may result in significant trouble down the road. I hope the pitfalls that I highlighted above make sense to you to avoid them in the first place.
You may have heard TMI being thrown around social media and the like, but now it has come in to the world of business as well. Too much information about products, services, suppliers, and supply chain processes can complicate things instead of simplifying them. All spend data needs to be analyzed, so the more data you have, the more you will need to process. There is also an increased chance of identifying wrong trends through data that is overly descriptive – for example “red cotton towel with football print” instead of “item 201”.
Pitfall #2: Non Standardized Data
Here is another important item even large businesses forget easily. With the increasing use of computers and mobile devices by managers, customers, and sales personnel, it is only natural to deal with multiple languages, sources, and formats. Any products or services that are new, or discontinued may end up without material codes. Manual override systems built into most software allow selling or even ordering such items without the complete category or description. Furthermore, managers or employees are free to create their own product categories and classes stepping outside your company’s regular catalogue. This makes the sorting of data tedious and can even make you miss out on vital information.
Pitfall #3: Unable to Standardize Processes Efficiently
Research indicates that just over 20 percent of all enterprises using spend analysis utilize automated software. This means that despite the available technology, most organizations still rely on using antiquated single line entry spreadsheets to process massive amounts of data. This poses two problems; first it takes a lot longer to go through the relevant information. Second, any similar categories or products are spread around the sheet with no means of consolidating the data easily with a single click.
Pitfall #4: Other Cautions
It goes without saying that bad quality data or incomplete data will also negatively impact the spend analysis process. In fact, this could lead to overlooked opportunities and issues that crop up with too many suppliers. This process is not something that is to be taken lightly; 70 percent of all organizations that use spend analysis tools report improvements within the first quarter after implementation. But make sure the data is accurate, it doesn’t hurt to verify and validate as you go along.
Summing All Up
The reliability and validity of spend data must be assured from all angles before the data analysis process can begin. Any spend data that is not up to the mark, or not properly sorted or categorized may result in significant trouble down the road. I hope the pitfalls that I highlighted above make sense to you to avoid them in the first place.