First Steps Toward Customer Data Integration
No. 3: Build measures of data quality. You need these measures to take remedial action when necessary. Data quality cannot be emphasized enough, so at this point we need to discuss it in a little more detail to get an idea of how data quality can and should be measured. There are four components of data quality:
* Data completeness—The percentage of all possible data sources and coverage across all data fields a company has accumulated in its decision-support and operational processes.
* Data accuracy—The overall exactitude of the data content and how well it contains identifying contact information and known data (internal or external) associated with each customer record. The question to ask, once your company has all the sources and fields identified and populated, is how accurate is that data content? In most companies the answer varies considerably with each source of data.
* Grouping accuracy—The accuracy with which a company can consolidate data from disparate sources. Once a company has all of the data sources identified and has performed the necessary cleanup to ensure they are accurate, how well can the company identify and group multiple occurrences of the same customer to provide a comprehensive, accurate customer portrait?
* Data access—The speed with which a company can integrate its data and provide that data in a usable form. If you're trying to beat a competitor to the market with a new product or service, how quickly can you put your data into play? Or, how quickly can you identify a current customer and treat him or her accordingly?
No. 4: Define your own "single view" of the customer. Whether your industry is financial services, retail, automotive, communications, travel or any other, this process starts with accurately recognizing customers through the use of names and addresses; demographic data; customer interactions; previous marketing efforts; risk scores; profitability (determining whether the customer is bringing profit to the business); and external data, which brings in more marketing information and credit risk knowledge.