Cost of Poor Data

Quantifiable cost means the cost which can be quantified

The first is High operating costs:

if data is bad, then a company has to spend more time searching for a better one. This costs money and takes time, resulting higher operating costs.

For example, if data are missing or incorrect, such as customers address is wrong, and an agreement was made with a client based on it. Not only will the transaction takes longer, but the customer will not be happy.

Secondly, Rule of Ten

Thomas' rule of ten says It costs ten times as much to complete a unit of work when the input data are defective as it dose when the input data are perfect. Here Defective means late, incorrect or missing and so on.

the third one is to obtain perfect data is strongly recommended.

assume that the error rate looks low like 1%, since a transaction may involve many data, even the error rate is 1 %, it could cost much more than we image. so a company is supposed to obtain perfect data, don't be fooled by the low error rate.

The other part is Unquantifiable cost

First, its customer dissatisfaction

like the example i mentioned before, once a transaction takes longer, a customer will not be happy and may refuse to do business with the company again. this cost is too huge to ignore.

One study shows that 20% of people surveyed claim they will not do business again with a company that loses their personal data.

In other words, customer is a very important source of income.

The next is that without a happy customer, a company cannot assemble a complete view of its customers and this means the company can not cross-sell other products to the customer, because he or she is not happy already and not going to buy others.

and the company can not discover new products niches or come up the effective strategies based on its profitable customers, because the company lost its customers already.

Thirdly, more time and money spent on fixing and/ or rebuilding systems

as companies are investing more and more replacing current systems with Enterprise Resource Planning, Customer Relationship Management and data warehouses, poor data is the number-one reason for these new systems to fail. If the new systems fail, companies have to spend more time and money fixing or even rebuilding new systems.

Next, Tactic and managing

As people lost faith in the data, or even worse, flaws of a plan using defective data are pointed out

Under

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