Silos are something you’ll find in every organisation, and the larger the organisation is, the more silos there will be. This is inevitable because different departments have their own processes and ways of doing things as they work towards their business goals.
However, while the creation of silos in a business is just something that has to be accepted to maintain efficiency and control, it poses challenges when it comes to implementing business-wide practices.
Changing the mindset for data creation
When it comes to bridging gaps between silos, the key to doing so effectively is to make it clear to each department that good data governance is much more than just an IT issue. Instead, introducing new data governance measures should be treated as a business-wide transformation project.
An important aspect of successfully implementing business transformation is to help people understand how data is going to be used down the line. Whenever someone creates data, they are creating it to meet their needs at that moment. This means that basic data might not be a problem for them there and then.
However, it will be when it is used by others in the future for completely different purposes. Therefore, to reduce the risk of this happening again, as part of the transformation project you need to make it clear to people at the point of creation how that supplier data will be used in future, while also introducing processes that make it easier to create better data records.
How do problems with data quality arise?
You may be wondering how problems with data actually arise. When speaking about the creation of supplier data in abstract terms it can be hard to visualise the process. However, the image below shows clearly how it can happen:
Before you start thinking about introducing governance processes that prevent what’s shown above from happening, you need to understand some important truths.
- Only two moments matter in the lifespan of a piece of data: the moment it is created, and the moment it is used.
- The data’s quality is fixed at the point of creation.
- However, the quality of the data isn’t judged until the moment it is used.
- The person who creates the data might not be aware of how it will be used by other people because they created it to meet their needs.
- If, later on, the person who uses the data deems it to be poor quality, they will typically try to correct the errors themselves.
When you bear these points in mind, it becomes clear that the problem you must find a solution to is connecting the person who creates the data with the person who will use it in the future.
Speaking the language of the business
Given that this will be a business-wide transformation project, rather than taking place in just one department, it stands to reason that you’ll need buy-in from the organisation’s leaders to enact change. Therefore, you need to communicate effectively with the wider organisation in terms they will understand. This means talking to them in broader, more understandable terms, rather than highly-technical language or jargon.
The real business processes they will be interested in, and therefore the ones you need to address, are:
- Supplier onboarding
- Reporting / analysis
- Supplier management
- Supplier phase out
However, while you will need to speak to them in broader business terms, this doesn’t mean talking to them in abstract terms. In other words, you need to link data processes to business processes that they’re familiar with, to show them the value of Master Data Governance.
Enabling, not policing, different departments
While the main aim of Master Data Governance is obviously to make it easier for those within silos to access the data they need centrally and ultimately make people’s lives easier, you need to remember that there will always be some demand for localised flexibility.
You don’t want people within departments to feel like control is being taken away from them and that they are being ‘policed’. Instead, they are merely being enabled. You also want to communicate the fact that any central data repository isn’t there to replace existing systems – again, it merely enables greater efficiency.
The key functions your Supplier Master Data Solution must perform
When implementing your Master Data Governance transformation, here are the key requirements you need to bear in mind:
- Data model: your solution’s data model must be flexible so users and suppliers can work within it easily.
- Data consolidation: given the number of systems your organisation is likely to be using, your solution must be able to support consolidation from those systems and accommodate every attribute for 100% of your suppliers.
- Data flow: clearly, as we are discussing supplier information management, it’s a given that the data flow needs to start from your centralised solution. This prevents duplicates and ensures good data quality.
- Data governance workflow: if data is flowing from one centralised repository then there’s a greater need for data governance so any potential changes go through automated and manual checks.
- Data staging: in a similar vein to governance is staging. This means changes made by suppliers or users aren’t applied immediately, but will only be visible once they’ve been approved. Again, this helps to prevent garbage data and duplicates from being recorded.
In the end, it’s worth remembering that silos and systems don’t need to stand in the way of true transformational change.
Therefore, when you are putting Supplier Master Data Governance in place, the main thing you should be aiming to achieve is practicality rather than purity.