The challenge

We can think of spend analysis as being the process of:

• Collating spend data
• Cleaning and normalising spend data
• Classifying spend data
• Analysing spend data

Performing spend analysis in an organisation helps to reduce procurement costs, improve efficiency, monitor compliance and encourage the strategic direction of expenditure.

It is well known that modern organisations nowadays operate as units, not as wholes. A single organisation is therefore often composed of many subsidiaries, departments, locations, projects, etc. This hierarchy often means that different units within an organisation use separate procurement infrastructure, backed by different IT systems and procedures.

Typical Problems

With today's economic climate, it is increasingly important to scrutinise company expenditure. Taking advantage of economies of scale is one route used to determine where savings can be made within an organisation. For instance, if it can be determined that a supplier is used frequently, then it is likely that implementing a procurement strategy based on contractual agreements with the supplier will lead to significant savings in the long run.

In the typical unit based hierarchy employed within organisations it is often difficult if not impossible to see the global picture due to a lack of integration between locally adopted systems and strategies. A recent survey has found that over the last three years, on average, over 51% of organisations have reported that the volume and complexity of data sources is inhibiting their ability to perform spend analysis.

Having the ability to perform spend analysis is highly beneficial to companies as strategic spending is often found to contribute between 5-10% in expenditure savings.

How we help

To solve this problem K-Now collate spend data using semantic technology, aligning it to a central ontology to evaluate similar items, differing suppliers and alternative costs. This data normalisation automatically classifies expenditure across large organisations purchasing records allowing extensive savings to be made.

To solve this problem, our technology leverages semantic technologies to automatically collate and classify spend data across organisations. The classification is aligned with a central ontology, which allows organisations to interlink data across existing boundaries. Our suite of technologies can be used to provide answers to questions such as:

• What was bought?
• When was it bought?
• Whom was it bought from?
• What was paid for it?

Through the use of our information extraction pipeline, we are able to extract information from legacy documents that is then indexed and stored in disKover, our semantic data store.

Furthermore, eyeKnow can be applied to produce visualisations that give the user an insight into spend data stored in our disKover platform. You could, for instance, produce visualisations that show spend data according to its classification, suppliers, location and child organisations to empower enhanced strategic decision making.