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Enabling ROI through Data Refinement |
$610 billion is wasted each year due to poor data quality. How much of that is from your bottom line?
Dirty data is a lot like dirty fuel. No matter how good the car, it will malfunction and ultimately break down.
Dirty product data is unclear, inconsistent, and incomplete making it impossible to identify products by their technical and functional characteristics, or to locate the same or similar products.
The dirty fuel clogging your information pipes that you can't see…but it's there
This typical real-life example shows three different product descriptions taken from one catalog which describe the same product, but only an expert is able to identify it as such.
1.
DIN 912 10x1x30-2.9 mat304
2.
ALLEN SCR M10x30 stainless steel
3.
M10x1mmx30mm SHCS-SS
The cost of dirty product data
The simple example below only illustrates the higher procurement costs generated by dirty product data.
DIN 912 10x1x30-2.9 mat304 |
ALLEN SCR M10x30 stainless steel |
M10x1mmx30mm SHCS-SS |
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Holland
$ 1.20
x 7,000 units
$8,400
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United States
$ 1.00
x 12,000 units
$12,000
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Korea
$ 0.80
x 10,000 units
$ 8,000
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If the purchasing departments had been aware that these three separate product descriptions were the same product, they would have known that the product was already in stock (under different names) and would have placed a smaller order.
Creating value through data refinement
Refining data involves taking raw, unstructured, incomplete, and inconsistent product data located in different databases within the organization and transforming it into rich, high-quality master data that can be used across the organization - and while also maintaining ongoing data quality.
For example, this single product description was created out of the three different descriptions above.
| Category |
Type |
Screw Size |
Thread Pitch |
Head Style |
Fastener Length |
Drive System |
Material |
Screw |
Machine Screw |
M10 |
1mm |
Cylindrical |
30mm |
Female Hex |
Stainless Steel |
Get the right information at the right time
In the above procurement example, a high-quality product data scenario enables efficient procurement (the overall procurement order would be much smaller and cheaper).
Before Data Refining
$1.20 x 7,000 units
$1.00 x 12,000 units
$0.80 x 10,000 units
Quantity: 29,000 units
Cost: $28,400
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After Data Refining
$ 0.80 x 15,000 units
Quantity: 15,000 units
Cost: $12,000
Saving: $16,400
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What are the Business Benefits?
Reduction in procurement costs
Reduction in stock, logistic, and production costs
Reduction in delivery time
Improved customer service
Streamlined procurement, operations, and engineering
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Refined product data accelerates profits
The availability of accurate data will result in better Spending Data Management (SDM) decisions, while management initiatives such as supply chain optimization, MDM and PIM implementations; lean manufacturing; and more, will achieve vastly improved results.
InQuera offers a comprehensive solution encompassing the entire data quality lifecycle.
Contact an InQuera representative to find out how DataRefiner can improve your product data quality.
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