Supply Chain Visibility, Ethical Standards and Cost – The Importance of Data Taxonomy
Following on from our recent post looking at the external and internal factors that need to be identified so that supply chains can offer enhanced visibility to support legislative compliance around Ethical Standards; in this blog we’ll take a look at a massively overlooked enabling element – Data Taxonomy. What is Data Taxonomy – well, put simply it’s nothing more than an agreed naming convention. Let’s take a simple example – in a food supply chain a farmer might have a database that covers his produce. He will name the produce in his database, but his naming convention may be different to the conventions used by a ready meal producer supplying a retailer. Ultimately, this will cause significant issues when the data are brought together for analytics purposes or to benefit from Remote Process Automation (RPA). Now add the complexity of multiple suppliers and producers and the confusion grows exponentially, especially where the taxonomy relates directly to cost.
In our view data taxonomy doesn’t have to be difficult, but in needs to be owned by a suitably empowered operational team in the business – it’s not a sport for the IT department. To remain healthy, competitive and control cost, organisations must apply a structured and sustainable process of data management, specifically around direct and indirect overhead transactions.
Let’s look at some more complex scenarios; In 2022 there is still a large number of organisations who have a Spend Analytics platform that provides a structured and meaningful view of spend across multiple internal functions and multiple categories. So why is it that data taxonomy and control are common issues in todays enterprises?
Most organisations experience 4 key challenges:
1. A low-path effort towards consistent integration & management of spend data over time.
2. Taxonomies describing spend data are inconsistent and often not meaningful
3. Classification of spend transactions is often highly manual which leads to long implementation timelines and delays
4. Poor taxonomy leads to data integrity issues and the results derived from spend analytics are often not trustworthy or poorly interpreted
Organisations often fail to create effective taxonomies of key business assets due to large volumes of transaction data which are extremely difficult to reconcile manually. Usually inconsistent due to ad-hoc standards driven by incremental growth in different business areas, the majority of enterprises often leave individual operational teams to be responsible for defining subsets of taxonomy. This often results in a fragmented, inconsistent, and untrustworthy data flow that does not correlate with the information needs of the business. Inevitably, this leads to decisions being made on incomplete or inaccurate information and an inability to understand key metrics around spend and cost control.
An accurate, comprehensive spend taxonomy provides the opportunity for business to obtain and drive vital strategic spend insights. This results in greater cost control and provides the ability to implement strategic procurement and supply chain best practice and, critically gives Boards confidence in supply chain visibility. Product taxonomies must reflect the desired outcome of the total business and reflect actual product data and avoid category taxonomy being open to a ‘catch all’ data dump scenario.
The issues in defining data taxonomy and effective ongoing data management start with the manual approach to data entry and the difficulty in managing data integrity on an ongoing basis. The emergence and capability development of Robotic Process Automation (RPA) can now be applied to data management and ensure enhanced accuracy, reduced process timings and structured data that is correctly classified. However, business needs to walk before it runs in this respect and any transformation of taxonomy must have strong governance and clear process. We recommend that taxonomy be directed at the highest level but executed at the lowest level. In other words, the Board provides guidance and standards which are executable by all elements of the supply chain. Clarity is critical, as is simplicity. With clarity and simplicity comes the opportunity to automate and lift the burden of data entry from staff who inevitably have other roles.
RPA simplifies and automates routine business procedures in a cost-effective, non-evasive manner. Clearly data entry rules can be defined to remove the manual data entry requirement which can often lead to inaccuracy and delays. An automated solution can continuously check for data anomalies and thus improve data accuracy and consistency without human involvement.
Utilising advanced data analytics is pointless if issues regarding incorrect taxonomy and data integrity exist. The data insight will be meaningless and will result in inaccurate and misclassified data which therefore provides no value to an organisation. Applying RPA and then creating spend analytic reporting wither through an external provider or power BI application can provide meaningful reporting and ongoing management of data far in excess of manual methods.
However, even the initial stages of consistent taxonomy adoption can be difficult without a clear strategy. Busy senior executives need to be supported and critically, the key data interfaces, contributors and silos need to be reviewed for consistency and applicability. Most organisations don’t have the intellectual bandwidth to achieve this using internal resources while still concentrating on primary business output.
Here at The Vision Chain we have decades of experience in the application of technology and process change to some of the most demanding environments in business and government. Our proven Concept, Assessment, Demonstration, Manufacture, Implementation and Delivery (CADMID) methodology breaks down even the most complex problems and issues into achievable low risk steps to transform your business.