This week, the latest results have been presented in The Netherlands on Transparency in the Banking area. And although some institutions score really good, others really need to take it at least one mile further to get a good or even fair score.

We agree with the recommendations of the report that compliance regulations can help/force in being more transparent, e.g., the SEC in the USA is enforcing more detailed information than their Dutch peer, the AFM. And also for Basel II the financial institutions need to know who they are dealing with in the end. The phrase - in the end – makes it even more difficult for the CSR, because not only the ultimate legal entity is now needed, but additional details per region and per sector are required.

In our daily practice in implementing Customer Data Integration (CDI or MDM for Customer Data) projects, we face these challenges at our customers. They are absolutely willing to provide the right figures, however it’s far from a trivial task. There are many underlying systems that were never created to aggregate this kind of information in an easy way sufficient for reporting the CSR. There is a huge demand on bridging the gap between these systems in an non-intrusive way. To combine individual records in and across systems in so-called Golden Records, so on these can be used both for compliance and transparency on your social responsibility.

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!

The added value of an integrated customer view depends strongly on the quality of that integrated customer view. Every organization that is seriously planning to create a single customer view should ask itself the following question: “What determines the quality of my customer view and so the accompanying level of added value?”

Prior to answering this question we need to take one step back. Why does not every organization have a single view of the customer? The cause lies in the fact that many organizations have their customer data spread across multiple systems all facilitating separate business processes. Additionally customer data is often highly polluted, fragmented and incomplete.

So it appears that the data itself plays a crucial role in the lack of an integrated customer view. Or more accurately, the better the data – the better the customer view. And the better the matching of customer records across separate systems the better the integrated customer view.

So Data Quality and Matching (Identity Resolution) determine in large parts the quality of the integrated customer view and the added value that it delivers. Take a look at this demo showing a step-by-step approach how to build a single customer view and get a better idea of the role of Data Quality and Matching within this process.