Unless you’re lucky enough to have unlimited amounts of money, it is normal to keep a close eye on how much you spend and what you spend it on. The same applies to businesses, and this is commonly termed spend analysis.
You would expect a well-run business to be on top of its spend and indeed, many are. However, getting to being on top of spend may be far from easy.
Let’s consider some of the challenges businesses might face when aiming to get on top of their spend.
Rarely is spend data all in one system. On top of that, important spend data may exist on legacy and external systems, which only makes getting the data more difficult. To build an accurate picture of spend, all relevant data will be needed. Locating the relevant data sources is just the first task. Data will also need to be extracted into a usable format, so there may be a requirement for IT to get involved.
Once you’ve got hold of the spend data and are confident that it covers the vast majority of spend, how do you make sense of it? It may be in different languages, currencies and use different names or codes for similar items. Data entered manually can contain typos and inconsistencies. Can you identify data for a specific calendar year, or does some data refer to a company’s financial year? Is the data detailed and consistent enough to be analysed? It’s not straightforward.
The tasks to extract, cleanse, classify and normalise spend data should not be underestimated. Traditional, rules-based approaches to cleansing and classifying spend data can be time-consuming and may introduce inconsistencies if carried out manually. Automated approaches may be quicker but exceptions will undermine completeness, and therefore accuracy.
Market leaders in spend analysis use artificial intelligence and machine learning software to help detect and correct inaccurate or incomplete spend data and format errors. This software uses “fuzzy” matching to compare spend data against its own database to ensure that similar item descriptions, company names and other information are identified and corrected automatically. The categorised spend data is presented in customisable output for immediate business use, not just a generic presentation of spend data.
Once the data is cleansed, normalised and available in a customised spend data classification and reporting structure, there should be clear visibility of total aggregated spend on goods and service across all parts of an organisation. Now the analysis can begin, which should lead to:
But can you do all of this yourself?
To find out more about how spend analysis can provide the intelligence and visibility to create the levers that can dramatically increase profitability read our latest whitepaper.
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