The question ”what is a customer?” is used all around as an example when someone explains why data governance policies and consensus about business rules between involved business units is important prior to going operational with any data quality improvement initiative.

I have no doubt that the consultancy companies will be delighted to support a “what is a customer?” project at every single client around the world and thus making a considerable turnover.

Unfortunately this activity will consume time and focus from an unbelievable number of knowledge workers on a global scale – making the entire withdrawal of human intelligence sum up to the like of the BNP of a medium size nation. That’s a pity these times.

So couldn’t it be a ball if we at dataqualitypro could help the world economy with, if not by sharing the golden answer to “what is a customer?”, then at least place the question with less riddle-factor.

Here is my basic thoughts.

Business rules in general may be specific to a single company. But many business rules may be shared within the same of lines of business and even some business rules have a common foundation to every organisation. The question “what is a customer?” is in my opinion in the high end of this categorisation. So it should be possible to avoid every single organisation from starting from scratch.

A common explanation of the word “customer” is: “Someone who purchases products or services from you”. I think the word “someone” is key here. Because it’s not the role of a “customer” that forms the real problem, but the precision of the term “someone” that causes challenges when we try to link other and more specific roles to that “someone”. These other roles could be contract partner, payer, receiver, user, owner – you name it.

When you try to sum up the requirements for the precision for all these possible roles that “someone” could act in, you always end with the same result: A picture of the real world.

The real world is formed by individuals that may be singles, live together in a household or stay at a campus, nursing home etc. The homes belongs to a physical address. On that address you may have a small business also. Other addresses are occupied by larger business’s. It’s the employees there who actually acts in the customer roles as decision maker, contract signer, payment approver, user and so on. A company may have a mother company somewhere else and daughters here and there.

Ideally the answer to “what is a customer?” could be formed as:

“Customer is a role assigned to a legal entity in my complete and precise picture of the real world. The role is established when the first purchase is accepted with that entity. The role is terminated when…… For related roles see: Prospect, payer, user, supplier….”.

The main challenge is of course if the organisation is able to establish and maintain a complete and precise picture of the real world. So now we are definitely talking MDM and what realistic options you have around data models given your current applications, MDM styles and external data providers.

Also lots of specific challenges is present when you try to consume customer data linked to your complete and precise picture of the real world utilizing the available relations in households, company family trees etc. But having your data quality solved at the real world level is always a plus.

If you are exhausted with the thought of maintaining a complete and precise picture of the real world you might instead ask:

“On what level(s) can I optimally register my business partners and make confident relations?”.

Here you make an approximative construction similar to when we square the circle – sometimes 22/7 is OK and anyway the real fix is proved impossible.

Having your data quality solved at that level is also always a plus.

I made a mistake. BNP should be GDP.

I am not sure if management in general are afraid of taking decisions – often they are just not prepared to decide on undocumented matters.

Data governance and data quality assessment and improvement don’t have to be a linear process but rather an ongoing iterative process with data quality assessment and improvement successes in the starting phases.

As an example you can improve on name and address data so they are more aligned with the real world and thereby harvesting some very operational benefits in the first place and at the same time have a more documented view on possible tactical benefits as single view on customer (cross sale, churn, life time value etc.) and strategic benefits related to business intelligence on a solid basis.

At this stage you probably need these definitions on roles as customer, prospect, contract partner, user etc. – but now it also more clear to stakeholders what it is all about and where the real challenges are.