It is an issue that often leads people to look like question marks or points to his colleague and says: "It is you, right?" The only problem is that it is not uncommon for even your colleague pointing at you. In the case of bad data, a large part of the problem is that there is no established information policy with a clearly identified owner.
For most companies, it is quite obvious that you have a command set credit policy and a clear policy on how and under what premise the vendors to be evaluated. It is all too often that I am in dialogue with companies that do not have any information policy and in particular a clear ownership of critical business information.
Now several of you think that you have heard it all before before and that's not a big problem really. The only way we can get the focus on this is if it can be measured in money, directly or indirectly.

Let us discuss about this. These days, listed companies are hard-pressed when the stock market expects them to deliver at least as high profits with a significantly reduced turnover. In these situations companies focuses on solving this by reducing the capital and stock size and to save money on the purchases. And this should go quickly when the stock market live after their quarterly cycles. What kind of effects can poor quality of data and fuzzy data structure give?

• Time to money (savings)
• Missed potentials

Time to Money:
If a company lacks a clear information policy on how, what and when information is entered and updated, this results in confusion. The effect of this is that it takes a very long time to collect data and once it is compiled so you realize quickly that there are large gaps in the information and that it is directly wrong and yet they are uncomparable. This results in the best case of an internal quality projects initiated by tying up a lot of internal resources and takes a very long time. This rarely takes less than 10 months and often much longer and the outcome 70% correctness.

The economic impact is of course that it takes more time to both identify savings potentials and to realize real savings resulting in an outcome that does not makes stock analysts to smile and the stock dives. The company looses money.

Missed potentials:
The lack of information policy will result in that all who registers a new supplier does it in different ways. This makes it very difficult to identify savings potential in vendor group spend.
I have a recent example of a global company in heavy industry who in its BI system would identify their total spending at ABB and thus searched for "ABB" in the system. As a result, they missed about 30% of their spending when many companies were improperly registered. Their spending was due to the 55 million against the reality of 77. Multiplies this on a another 10 vendor groups which is not unrealistic to a company of this size, 220 million in the negotiating power of this group, which certainly could result in a couple of percent savings.

Data quality is one key business value of a company's profitability. Accurate data is not only a business requirement, it is common sense.
So if it hurts anywhere in the tree with inaccurate risk assessments and management reports, it's a good idea to "go to the root" with this as it often is where the cause of the problems lie.

So who owns all the information you are daily making critical business decisions based on and does this have your managements attention?

Does your company have a clear Information Policy?