One of the challenges in procurement is consolidating and harmonizing available data to make more fact-based decisions.
The procurement function faces several challenges in procurement. One of the biggest bottlenecks to work more data-driven is related to data collection and harmonization. In this article we will discuss some possible solutions to these problems, from larger enterprise-wide approaches to the use of customized solutions based on modern technology.
Businesses typically have multiple
sources for procurement data, where relevant data may be spread between different systems and solutions. For example, it isn't uncommon for companies within a group structure to use different ERP solutions. This often becomes a barrier to using the data at all.
Having access to consolidated data is crucial to getting the necessary overview and insight into your spend. What options should you consider to consolidate the data?
1. Invest in a Joint ERP Solution Pros: The data is available in one, common system, and in the same format, across all companies. Cons: Requires large capital investments in both internal and external resources, and will often be a comprehensive project. 2. Build Data Warehouses Pros: Possible to gather information from multiple data sources to one, common database. Cons: Requires investment, expertise and resources to build and maintain the database. 3. Use Plug-and-Play SaaS Solution
Data consolidation is something we facilitate in our spend management solution, which creates great value for our customers.
Pros: Gathers all data in a cloud-based solution, which requires minimal investments in capacity and resources. Easy to integrate directly to the different data sources. Cons: Requires robust digital security for data protection. Fortunately, this is something that all serious SaaS companies prioritize. Data Quality
Some companies do experience data quality challenges, such as small differences in the raw data for supplier names or lack of standardization in their accounting system. What can your business do to improve data quality?
1. Master Data Management Pros: Ensures that the raw data is updated and quality assured at regular intervals. Cons: Requires dedicated resources to maintain and manage the data, and the data will rarely be fully updated. 2. Standardization of Policies Pros: Data registration will be standardized throughout the organization, such as policies for using the appropriate accounts in the accounting system. Cons: Requires continuous review in several areas, as well as dedicated resources to follow-up internally. 3. Use Plug-and-Play SaaS Solution
Our cloud-based solution uses data cleansing and validation algorithms, but also allows users to easily edit the data.
Pros: Automatic and manual quality improvements in a user-friendly interface. Any adjustments will be remembered for new data going forward. Cons: Manual adjustments will require smaller one-time investments.
We often encounter companies that want to gain more insight from their data, but end up not using any of their data at all because they think the data quality is too poor. Our experience indicates that it's much better to start using the information you have, than not to use it at all!
Another established barrier is to harmonize the data, i.e. to use the data for purposes relevant to the procurement function. The most common example is
classifying spend into appropriate spend categories. How can you harmonize your data to get the insight you need? 1. Using Excel Pros: Most people have access to Excel and can use this at a basic level. Cons: Time-consuming to build, manage and maintain, where classifying the data will require complex formulas and advanced skills that provide little transparency over time. 2. Build Classification Algorithms in ERP Solution Pros: Ensures that the information is easily accessible in the ERP system. Cons: Requires assistance from internal and external IT resources, and not very dynamic in terms of adjustments and changes going forward. 3. Use Plug-and-Play SaaS Solution
Our solution for strategic procurement facilitates data classification and standardization, where users, for example, have full flexibility to create and maintain category structures.
Pros: Data can be classified based on one or several data parameters, in many cases using "drag and drop" functionality and AI/ML. Classification rules ensure automatic data harmonization going forward, and easily adjustable if needed. Cons: Typically requires a smaller time investment to create appropriate classification structures and rules.