Supplier data challenges are ubiquitous across most businesses in some form. Perhaps we’re biased toward this as it’s one of the main reasons people talk to us about implementing SourceDogg, but many studies support our experience.
Whether it’s poor data quality or availability being cited as a top challenge by Deloitte, or Harvard Business Review stating that poor data quality issues cost $3.1 trillion in the USA – the problem is widespread and large. It’s not only challenging and costly, but it’s also inefficient, with more studies revealing up to 50% of the time is wasted looking for data then assessing its validity and accuracy.
It’s a major hindrance to business growth, so understanding what makes “bad data” and therefore how to fix it is a good idea for any business looking to enhance productivity and their bottom line.
Survival of the Fittest
So what makes for “good” data? According to the OECD, the definition and dimensions of data quality are defined as “fitness for use” in terms of user needs.
We must consider supply chain data in a more multidimensional way however as this fitness for a need is often misunderstood or conflated with accuracy.
Even if the data is accurate, there are other dimensions that are still important considerations. These could be accessibility or if the data is too slow to obtain rendering it useless. Understanding what makes “good” supplier data relies on needs, priorities, perspectives and context across the organisation.
The Big Six
So how can you assess your supplier master data? Here is our six-point checklist to get you started.
Accuracy (Correctness)
Although accuracy isn’t everything as described above, it’s still incredibly important that the data that you have correctly estimates or describes the quantities or characteristics they are designed to measure.
If the numbers or words you have in a particular field are not accurate, then this feeds the “garbage in, garbage out” a phrase that is repeated often in data management circles. It upends the usefulness of any analysis and evaluation. If supplier information is fundamentally flawed or wrong, then it simply isn’t fit for any user need.
Solving Accuracy Issues
By having a singular supplier master data source in the cloud, inaccuracies can be easier to audit and spot. Anomalies are more visible across teams and users, departments and divisions.
It’s also easier to ask suppliers to review their data as frequently as required. Onboarding shouldn’t be a fit and forget process – re-boarding should always be scheduled.
Accessibility
How many times have you tried to navigate through the impenetrable file naming and folder structure on a colleague’s “shared” drive? It may be shared, but it’s also incomprehensible! Or key data can be locked away in individual inboxes… of someone on annual leave!
Accessibility fundamentally cripples your employee’s ability to do their jobs effectively. The data may be perfect on all other dimensions, but if a stakeholder can’t get to it, in the method and format they need to then it’s pointless.
Solving Accessibility Issues
A single source of master data in the cloud, with simple logins to access this is a great place to start. This may sound obvious (and biased), but it also provides a huge amount of reassurance for audits.
Using a dedicated system means changes and access can be logged clearly and effectively, rather than trying to unpick revision histories in cloud versions of Excel or Google Sheets.
Different divisions and departments are all working from the same information too – not outdated or incorrect versions of files. Governance is made infinitely easier with strong master data management practice.
Completeness
Whether it’s missing records or incomplete fields and factors within your supply chain data, completeness is a massive problem that affects confident decision making.
If you don’t have the same attributes captured for all suppliers in a category, how can you compare them? If full profiles are missing, how can you work to develop the supplier competencies? It could be something as simple as the lack of accreditation or certification uploaded to a system, these seemingly small completeness challenges can pivotally affect your supply chain.
Solving Completeness Issues
Running audits on your data regularly as part of a governance cycle can help ameliorate these issues. If you can’t see it, you can’t fix it. Being proactive and identifying gaps in your data will help lessen problems when the data is being used for mission-critical decisions by senior management.
By using an online platform to do this, you can also quickly export the data to identify gaps, then reach out to the suppliers where the gaps have been found.
Timeliness
Timeliness in supplier data management is closely linked to accessibility. Is the data available when you, your colleague or your incredibly demanding CEO requires it?
If data is not available, or it takes too long to find – it’s as good as useless.
Solving Timeliness Issues
It’s a difficult task to foresee when you’ll need certain datasets, but we believe having a supplier master data platform in the cloud can help you be more agile to requests. If you know where it is and are confident on some or all of the other quality factors (i.e. it’s accurate) then being timely is much simpler.
Even when tenders require intricate social value policy statements and calculations, clients demand audits or a senior manager needs a report yesterday – it’s easier to be timely if you know the data is accurate, complete and you know where to find it.
Relevance (Usefulness)
Relevance is referencing data on your suppliers that contribute to business growth and not irrelevant information like how your steel supplier takes his coffee!
Relevance is difficult to define from a single perspective as it is the most context-sensitive factor on the list. What’s relevant to one employee, department, project or decision-making process may be vastly different to another. So although we jest, the person making the coffee only needs that relevant data!
Solving Relevance Issues
In cases like the above example, understanding one step ahead i.e. what the data is used for is of paramount importance.
Mapping out what is required for each process or person is a valuable exercise, then requesting this information from your suppliers and making the data accurate, complete and accessible are the next steps.
Consistency
Consistency may sound obvious, but we’d like to also add conformity to the mix.
Consistency refers to the data being consistent across all entries of that representation of a characteristic. The supplier’s capacity or turnover fluctuates, but the data you’re using should not. Everyone should be on the same page (synchronised) and the page should be the same (not multiple sources/versions.)
Conformity is more of a formatting issue – but without consistent formatting, data can be rendered useless. A stray space or letter in a field that should be required to be numeric can create chaos.
Solving Consistency Issues
Consistency is achieved by using a singular source of supplier master data. Synchronisation is therefore not a problem, and all edits and revisions can be tracked for auditing.
Conformity can also be achieved by setting specific formats within fields in the supplier master database – and only allowing this, or flagging where data doesn’t meet the required criteria.
In conclusion, by listing what good data management looks like, it’s easy to see where the gaps are in your organisation. By using these six data quality factors as a framework to assess your current data storage, platforms and practices we believe you can make massive leaps forward quickly.
We know it’s not simple, but our clients have all had similar challenges. It’s one of the reasons SourceDogg was created and we’d love to help you implement a Supplier Master Data on the SourceDogg platform.
