Fancy bar and pie charts, complex scatter graphs, even those 3-D area graphs we all wish we could use, they may look good, but what are they actually telling us?
Let’s start with what Data Analytics is. It refers to “qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorised to identify and analyse behavioural data and patterns.”
What benefits can be achieved through data analytics?
Obtaining meaningful insights from the dataset: organisations have a wealth of data hidden in their procurement systems. Understanding what is important to the business (success metrics) and extracting and presenting this data aligned to those metrics is the first key step in using your data better.
Improving efficiency: analytics which show the business where there is room for improvement in areas such as the time taken to approve purchase requisitions and process invoices, monitoring the number of retrospective purchase orders, and identifying invoice exceptions can have a massive effect on business efficiency. Once these aspects have been identified, sessions should be held to recognise the best ways to improve. This can range from key stakeholders taking on the task or holding workshops with the regular “offenders” in order to develop their working habits (are they doing it deliberately or are your systems making it hard for them to do it right?)
Cost reduction: advancements in AI now mean that huge volumes of data can be categorised and analysed in fractions of the time it used to take. Even without AI it is surprising how many organisations don’t have a clear view of spend with suppliers. For larger organisations this can mean missing huge cost saving opportunities – how can the supplier base be rationalised, where is contract leakage, which suppliers across regions could be consolidated – there are many questions that a simple, solid set of data can shine a light on.
Basing critical decisions from the findings: supplying the decision makers with complete data presented in insightful ways, and linked to success metrics means decision making can be faster and more reliable.
Where do people fail with procurement data analytics?
Cost of data analytical tools: as always, cost is a key factor and analytical tools require investment. However, in our experience many of the organisations that decide to avoid costs of technical solutions continue with manual processing (or none at all) which often results in a false economy due to missed opportunities and the time intensive process of analysis
Only highlights the problem and not the solution: it’s great being able to present the data in graphical visualisations and while it may highlight the issues within the business, it still leaves the decision makers without any solutions on how to solve the problem. The key is to make sure data is linked to success metrics and that actions are taken as a result (rather than simply distributing reports to a group of people)
Output may not be utilised efficiently: there is no guarantee that the user will interpret the data accurately and this could lead to incorrect decisions being made or poor advice being provided. The right expertise is required to ensure appropriate action is taken.
Upon reflection, when the time and effort is invested in data analytics it should be a key tool to a business. Many organisations are exploring the option, if not already using “big data analytics” and “artificial intelligence” in order to gain a competitive advantage. Remember, data analytics can cover a broad spectrum; whether through an analytical tool, manual data interpretation and performance reporting, or even through spend management optimisation services such as XoomTune.
XoomTune provides advice and expertise to ensure benefits are identified and achieved from an organisation’s Spend Management solution. It enables an organisation to ensure their solution is continually improved and delivers on the original business case. Read more on XoomTune here.
So, what is the importance of data analytics? Instead, one could argue we should be asking what is not important about data analytics.