Unfortunately, in spite of the Oracle Application ERP, data warehouses, BI software, and other goodies that that the company had invested in during the last few years, he couldn't get a reliable picture of the company’s spend. I asked him to send us his product data (in a text file, Excel, or something similar), so we could analyze and evaluate the data quality.
I had a pretty good idea of what to expect, since I’ve seen it many times before. But we needed to put the evidence on the table, so to speak.
Let's take valves as a typical example. We found that they were classified under more than 20 different categories. Here are just a few examples:
- Industrial Safety – Breathing Equipment – Valve/Diaphragm
- Control – Control Equipment – Valve
- Lifting – Winch spares - Engine/Clutch/Relay Spares
- Liquid/Gas – Brass/Copper/Bronze Parts – Safety Valve
- Liquid/Gas – Stainless Steel – Pneumatic Valve
- Control – Control/Tubing Equipment – Electrical Valve
- Control – Control/Tubing Equipment – Pneumatic Valve
- Vacuum – Vacuum Installations – Right Angle Valve
In this scenario, ascertaining annual spend on valves, the valve inventory level, valve inventory turnover, and the number of valve suppliers is almost impossible. But, if all valves (irrespective of their usage) were classified under Valve, getting the required information could take a single click.
Most companies have no suitable taxonomy and, as a result, all their product data quality efforts are built on shaky foundations. If exactly the same product is classified under several different categories, decision making regarding spending and supply chain efficiency becomes guesswork.
I’ll talk more about taxonomy in future posts.
2 comments:
Hi Yossi
This is a huge problem that isn't going to be solved overnight.
I see this all the time in data migration projects in particular and it bites companies hard.
I recently worked with a company who had about 500 types of plant equipment in one system and about 5,700 types in another.
The company wanted to merge these datasets together to create one master, they assumed that because the equipment was essentially the same there would be no real issues but it caused massive impacts because the data could not be loaded without merge.
If the business had aligned its equipment with a taxonomy along the lines of what you discuss this would never have occurred but people never saw the value as they were disparate data sets in different business units.
I think more effective information management, business data area stewardship and ultimately MDM will help but until then we're going to see a lot more of these issues as companies put pressure on their data to perform "sexy" new capabilities it was simply never designed for.
Great post Yossi.
Dylan
Great post very interesting
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