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…..