Frequently, we find that our customers have a clear challenge: bad quality in their master data. However, most of the time the affected company is only aware of the consequences and not of the real source. An example:
Company X from foods distribution sector paid last year 2 million USD in penalties to its logistic operator (150 USD per penalty) due to errors in the shipping address in the delivery note provided to the operator and unnecessary transport of the trucks.
The source of problem was evident and in this case they quantified how much these master data error were costing the organization, but is it always like this?
I was recently in a shop in an airport, a multinational best-of-breed company shop. I asked for a specific handkerchief in a specific color for a gift. They had the item, but I couldn’t buy it because the reference for that size and color did not exist in their system. Let’s analyze this situation:
- The product team of the company worked hard designing that new product.
- All of the supply chain machinery all around the globe from the design center in Spain, to the manufacturing plant somewhere in China and finally the transportation from factory to the shop, every single piece has worked like a Swiss watch in order to produce and distribute to the shop with the minimum lead time and the minimum cost.
- The sales and marketing department got my attention as a customer and they convinced me to go to one of their shops to buy it.
- The sales cycle is interrupted, losing a sales (and possibly a future customer) all because the item wasn’t added correctly to the material master data in the sales point terminal
How can all the process be correct, all the supply chain worked as a perfect machine in time and costs, but the sale is ruined due to such a tiny detail?
Situations like this one where master data is not recognized as a crucial part of our business process are happening around us every single minute:
- A production line stops because the components supplied are bad, do we recognize this as a master data error?
- A new employee cannot be productive on day one due to lack of existence in the HR system: not having a computer assigned, no authorizations, etc. Do we understand that updated HR master data is our problem?
- A production plan has to be modified in the very last minute due to lack of materials because the replenishment was not on time. Do we detect that these delays are a consequence of delays in the material master data update?
All of the situations above affect our business process and productivity, and have a serious financial impact. The question we should ask ourselves is:
- If master data has such financial impact in business, why is it handled as a secondary task and we assign it to a non-business department like IS?
Master data is an asset, and is involved in our business processes in the same way a bottling machine or a truck is involved. ALL of our business processes depend on it, it is the heart of our processes, but:
- Is it handled like that?
- Do we ensure its quality?
- Do we maintain that it is clean and updated?
- Or do we just worry about it when it is already too late and the problem is not fixable in a timely manner?
At Winshuttle, we communicate this message to our customers. Master data (materials, customers, vendors, etc.) are the base of our transactional data (sales order, deliveries, GL postings, etc.) and these are the base of our business reporting.
Every day millions of employees around the world make decisions based in their reporting, a lot of them using reports that are based on wrong or poor quality transactional data due to errors in master data. These decisions mean millions in costs and lost opportunities.
When we talk about quality of our master data, we are talking about the quality of our business information.
With the current economic environment, inefficiencies due to bad quality of master data are something we cannot tolerate. Hopefully with solutions like Winshuttle for Master Data, companies can fix these problems and change the negative tendency of these processes, improving their master data along their operative chain, which translates into savings and improvements..
In next posts, I will drill down in available solutions with rapid implementation time and high values that can cover these issues.
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