The market, like everything else in the world is in a constant state of flux. Companies seek the most innovative strategy to diversify their portfolios and extend their reach, all in an effort to maximize profits and enhance efficiency. Using the most ideal form of spend analysis for your company can reveal upcoming trends and new opportunities. Let’s look at specialized divisions of spend analysis, where each comes with its own set of strengths and weaknesses, so insights on them can go a long way in making an informed decision.
Descriptive Spend Analysis
By far the most commonly used technique, descriptive spend analysis is useful in identifying what amount is being spent by which division of the company and in which geographic region. Descriptive spend analysis is largely dependent on the user, as he or she needs to be sharp enough to extract the correct data that will be transformed into credible information.
The limitation of this element is in fact the user, hence the data obtained could be very valuable or a pile of trash. This dependency for humans makes it prone to errors and a little laborious. It must be noted that descriptive analysis leans more towards history and review. The insight from the descriptive analysis is applied over a prolonged period of time, and while the data obtained at the time of implementing may be valid, the duration of the commitment may render the insight somewhat obsolete. In addition, the management may lose interest and may seek new avenues that provide better returns in a shorter time frame.
By far the most commonly used technique, descriptive spend analysis is useful in identifying what amount is being spent by which division of the company and in which geographic region. Descriptive spend analysis is largely dependent on the user, as he or she needs to be sharp enough to extract the correct data that will be transformed into credible information.
The limitation of this element is in fact the user, hence the data obtained could be very valuable or a pile of trash. This dependency for humans makes it prone to errors and a little laborious. It must be noted that descriptive analysis leans more towards history and review. The insight from the descriptive analysis is applied over a prolonged period of time, and while the data obtained at the time of implementing may be valid, the duration of the commitment may render the insight somewhat obsolete. In addition, the management may lose interest and may seek new avenues that provide better returns in a shorter time frame.
Prescriptive Spend Analysis
This technique, while requiring the user to be of sound reasoning to feed the correct data, takes the burden of generating the insight away from the individual. If the prescriptive model is solid and the data fed into is correct, then it is almost certain that the result will be error-free, which makes the results viable. However, the limitation is that the data will not churn out tailored solutions. It is the acumen of the business leaders to comprehend the data in terms of their own knowledge and expertise. The user still plays in an important role in the implementation of the solution, adding to its disadvantage.
Predictive Spend Analysis
This is the most advanced method in business analytics but one of the most complex to execute. Predictive analysis requires the input of all historic data of a company, which is then processed to arrive at a specific solution. The hallmark of this method is the extrapolation of the data into the future, where insights can be derived to recommend preventive actions. The trouble lies in the consistency of prediction on a year-to-year basis, as it is extremely difficult to predict with complete accuracy. The risk factor associated usually deters companies from investing their time and effort in this method. Moreover, the biggest problems that plague this method are related to the raw input of data, accuracy level of the computations and accuracy of the result. These limitations prevent managers from completely trusting this method, especially when it comes to making big decisions. Though this is the most recommended method, it may not be a good fit for all organizations.
This technique, while requiring the user to be of sound reasoning to feed the correct data, takes the burden of generating the insight away from the individual. If the prescriptive model is solid and the data fed into is correct, then it is almost certain that the result will be error-free, which makes the results viable. However, the limitation is that the data will not churn out tailored solutions. It is the acumen of the business leaders to comprehend the data in terms of their own knowledge and expertise. The user still plays in an important role in the implementation of the solution, adding to its disadvantage.
Predictive Spend Analysis
This is the most advanced method in business analytics but one of the most complex to execute. Predictive analysis requires the input of all historic data of a company, which is then processed to arrive at a specific solution. The hallmark of this method is the extrapolation of the data into the future, where insights can be derived to recommend preventive actions. The trouble lies in the consistency of prediction on a year-to-year basis, as it is extremely difficult to predict with complete accuracy. The risk factor associated usually deters companies from investing their time and effort in this method. Moreover, the biggest problems that plague this method are related to the raw input of data, accuracy level of the computations and accuracy of the result. These limitations prevent managers from completely trusting this method, especially when it comes to making big decisions. Though this is the most recommended method, it may not be a good fit for all organizations.
