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Spend analytics are a key way for businesses to recognise their existing spend behaviours, decrease costs, increase efficiency and improve supplier relationships. However, those analytics are only as good as the spend data that they represent. This is why business owners must ensure the correct gathering, cleansing and storage of data to inform accurate analysis.
So, what does this look like and how simple of a task is it? Let’s look first at Data Cleansing.
What is Data Cleansing and what are the benefits?
Data cleansing is the process in which inaccuracies, outdated, irrelevant and/or corrupt information is removed from a data set. In spend management the process includes trawling through relevant data sets prior to analysis and eliminating errors and discrepancies to ensure its accuracy.
Through data cleansing, businesses will be able to keep data updated and undergo a review for errors such as typos and invalid coding. Only after effective data cleansing, should the data sets be used for analysis. Without data cleansing, businesses expose themselves to costly errors and increased risk, especially when analytics, such as spend analytics, are used to make important business decisions.
Effective data gathering and storing processes are both vital in proper spend management, having an impact on the quality of analysis that can be done. After all, in order to analyse spend data, it needs to be easily accessible.
So now we know why the process is so important, how do we overcome the challenges that come with managing data?
Data challenges and how to overcome them
· Volume of data Decades ago the problem for businesses was not having enough data, now they are overloaded. Whilst this makes advanced analysis possible, having large data volumes presents its own problems. An obvious solution for this is increasing investment in tools used to automate data gathering and storage. Alongside this, making use of data storage technologies such as cloud storage will enable a clearer and easier way to store all data in one place as well as having far larger capacities than in-house storage methods.
· Organisational Culture In order to prioritise the most efficient gathering, storage and use of data, the benefits of it must be understood through the organisation. For example, if spend data is being used to drive efficiencies, highlight potential cost savings and inform decision making, the organisation must know of the importance of correct data management. By having simple and user-friendly processes in place, with benefits clearly outlined, businesses are far more likely to build a culture that prioritises good data management.
· Data type and quality With Spend Data alone, businesses can expect gather a variety of different data types for analysis. A fact that can come with issues if not managed correctly. Whilst a lot of data is now received and stored digitally, paper-based invoices and documents are still the means of doing business for many companies.
The gathering and storing of physical data sources is a more time consuming one, requiring manual input. Having a properly defined internal process is one step to ensuring higher quality data and better data management. With methods such as having a data classification system in place, data is much easier to locate and retrieve regardless of the type. With a clearly defined internal process, businesses should feel able to request externally sourced data to be sent via a similar structure.
· Human error Whilst automation is on the rise, human error is still a very high and anticipated risk when it comes to gathering and storing data. As discussed above, many physical methods are still used, as data sources requiring human management come with a risk of error and /or potential for duplication. Having integrated auditing and checking systems in place is a solution for this issue. However, data cleansing will also highlight errors or inaccuracies. Another reason why incorporating it into data management is so important.
To summarise, properly gathered, cleansed and stored data can help make economic savings, increase efficiency, reduce risk, manage stakeholder and supplier relations, and create commercial benefits. Data cleansing and innovations in technology are ways in which this process can be improved and managed going forward.
Key points to take away:
· Be clear on your end goal and incorporate it into in the culture of your organisation.
· Understand the importance of clean data, specifically within spend management
· Look for any possibilities of digitising manual processes
· Prioritise the processes first, making sure they are simple and easy for users to understand
· If feasible, consider the use of Artificial Intelligence or Machine Learning for more sophisticated business practices, forecasting and risk reduction.
· Look at the potential for using platforms and tools to streamline your data management.
If you would like to discuss your existing spend analytics processes for your business or how to implement better spend management, get in touch with us today.
