The success of many business processes is linked directly to the quality of customer data. This is not only an obvious fact, but a recurring conclusion of many field studies: Incorrect, incomplete and inaccurate data will have a direct impact on your business succes rate. The symptomatology of this increase is established in inefficient marketing and sales processes, customer dissatisfaction, difficult cross- and upsell, unreliable analyses and many other disturbances in the day-to-day business of almost every organization dealing with customer, supplier and/or partner data.

In essence, it all comes down to knowing your data, in order to be able to trust your data. If you trust your data, you are definitely doing something right. So, how do you establish that trust? For this, you first have to answer a short, yet rather complex question: What is what in my database(s)? In other words: You have to identify and interpret the data you are working with .

A robust customer data identification solution intelligently interprets the details of both natural and legal persons. That process has to take account of the significance of words in a specific context, usage of company names, abbreviations, synonyms, acronyms, spelling mistakes, notation methods, standards and phonetic similarity of words. All in all, this is not a simple task; it more or less mimics the capabilities that humans show when interpreting data …

It is, however, the first step in a solid data quality strategy. This strategy should entail some sort of methodic, recursive approach. This makes sense, since data cleansing is basically a process of recurring steps. Initial cleansing should, for example, be combined with methods to prevent future pollution. In other words: Do not only fight the symptoms of bad quality, but eliminate the root causes and make sure your clean data will stay clean. Underneath you will find an illustration of such an approach:

Effective customer service, targeted cross- and upsell, cost decrease and creation of cutomer lifetime value are but a few goals that will be achieved by defining and deploying the right data quality strategy. So start to know your customer and learn to trust your data…..

The struggle of some countries has to do with historical reasons and with privacy aspects. Unique identifiation is not always used in favour of the community. And some of the used identification systems contain privacy-sensitive information, among others date of birth, gender and/or place of birth, where older systems might even contain religious or other privacy-senitive information.

A wide range of countries use the combination of date of birth, gender identification and the political region where you are born. In such a mechanism it is most common that part of the identification number is a 2-digit or 3-digit serial number to identify the unique male or female born on a specific date (or born on a specific month). Some countries provide odd serial numbers for male, and even for female. Bulgaria is the only one that wants “odd” females. Some countries like to divide on range (0-499 male, 500-999 female). And some countries like Norway make nice combinations to include the century of birth or period of birth in the serial number.

This ‘number’ generation brings the effect that pretty soon you will encounter the maximum number of citizens that the system can handle on a specific day. Some systems run out of numbers if there are more than 500 males or females born on a day. The Denmark system encountered that situation in 2007, where due to immigration the population exceeded the system for January 1st 1965! The Denmark system (CPR-nummer) has a 3-digit serial number where one of the digits is also the control digit (diminishing the possible numbers than from 500 to less than 50).

Remarkable to see what some countries are doing to solve the ‘century’ issue, people with the same ID but born in the 19th, 20th or 21st century, they add 20 or 40 to the month. Same is true for foreigner identification, e.g. Sweden that is adding 60 to the day of birth. Or again Sweden that is adding 20 to the month to distinguish persons from organisations.

If you want to see the details on these systems you might watch http://prezi.com/csnv3cynv4ai/ or http://en.wikipedia.org/wiki/National_identification_number. Be prepared, definitely there have been PhDs around to invent these systems.

The more a company knows about its customer’s wishes, needs and habits and the more that company is able to tailor its proposition accordingly, the greater the value it will eventually provide for its customers. We all know that there are countless examples where defective, fragmented, or just plain poor customer data cause unnecessary costs, decrease in revenue, employee dissatisfaction or frustation, damage of the corporate image and many other unsdesirable or painful consequences.

Customer data quality and integration problems impact every area of the value chain of organisations. Far too often companies have a multiple view of their customers. Customer Data Integration (or MDM for Customer Data) is the key to providing companies with a single view of their customer. According to Gartner, Customer Data Integration (CDI) is a combination of technology, services and processes to deliver an accurate, timely and complete view of the customer across multiple channels, lines of business, departments and divisions drawing customer data from multiple sources and systems.

I think that the real “miracle” of CDI lies in the automated, intelligent matching of customer records. Mind you, I’m not questioning the importance of the various CDI-processes (for example, I think that the post of my colleague Ramon de Noronha on the creation of “golden” records is majorly important), I’m just saying that -whenever the integration of customer data is an issue- intelligent, automated matching is the key prerequisite for success.

The quality of your customer data integration solution is only as powerful as the quality of your matching engine. If this statement intrigues you, I strongly advise you to read the white paper “High Precision Matching at the heart of Customer Data Integration“. Enjoy!

This watchlist was created in 2004 from several other lists and at that time it consisted of about 68.000 entries. I use the word entries, because in the years after it became fuzzy if one record is the same as one individual. By the end of 2008 the list had grown to over 1,1 million entries. In 2008 after the American Civil Liberties Union (ACLU) mentioned that the list had passed the 1 million, the government came with an explanation. Although we have recorded over 1 million entries in the database, the net result is that these records correspond to about 400.000 individuals. Terrorist often use different and thus multiple identities, use several (falsified) passports etc. But adding entries with only the first initials and last name, while an entry of the full first names and last name already exists will result in unwanted side-effects.

We all know, as being interested in data quality and identity resolution, that J. Robinson will result into much more matches (hits) than James Robinson. Indeed the number of found matches will sky-rocket and have to be evaluated manually. Might this be the reason, that we see more and more security personnel on airports?

In the latest audit report of the U.S. Department of Justice about this watchlist one other problem was analyzed. While extensive procedures were made for nominating and adding suspects to the watchlist, there is no procedure for removing people from the list. Based on a sample of almost 70.000 entries and investigation of the individuals an astounding number of 35% omissions was found. People who had died were still on the list, people who were no longer investigated upon, cases which had been closed etc. So this watchlist is growing and growing. Resulting in screening personnel who ensnare many innocent travelers as suspected terrorists. And wasting their time and divert their energies from looking for true terrorists. It seems to me that FBI and TSC can benefit from better Data Governance, what do you think?

• bank account numbers
• social security numbers
• chamber of commerce numbers