Managing the quality of customer data has its challenges: It is typically collected from a wide range of sources and channels and very often those responsible for entering or capturing data have no incentive to do so accurately. Even if they do much of it, including contact data can go out-of-date rapidly. Despite this most customer data has one major advantage over many other types of data which is agreed and accepted standards and reference data. While these standards do vary from country to country, they are at least universally understood and have an enormous impact on the approach and the effort required for managing data quality.
Because of these global standards and references, there is general agreement on what a complete, valid and correctly formatted address should look like – likewise person or business name, telephone number, date of birth, email address etc. So this means that if I am sharing my customer data with my business partners at least we have a common view of what high quality data should look like and the checks we need to make to assess the quality levels.
Another huge benefit is that third party service providers and technology vendors also understand the requirements and standards by which to measure and improve customer data and they know that these requirements are largely the same for all vendors. As a result large numbers of service bureaux and technology vendors are able to offer well developed, generic, out-of-the-box products and services to tackle customer data quality issues. These can deliver a lot of value with minimal or no customization and the effort to acquire and implement these solutions is small.
Compare this to other types of master data – for example product data, financial data or asset data – data which is common to all companies (or companies within a specific industry) but where few standards have been agreed and accepted globally. As a result the benefits of a common understanding don’t exist – if I need to share product, material or asset data with a business partner, we will probably have to sit down first and agree new standards for what a high quality data set should look like – how we define complete, valid and correctly formatted data. And worse still the quality definitions and requirements of each of my business partners may differ – forcing me to have multiple descriptions of quality standards and rules for managing my data.
At the same time, because of the sheer complexity of the problem, technology vendors and service providers are less likely to have developed generic products and solutions to assist companies to manage the quality of data that goes beyond generic customer data. The options are for a solution provider are: (1) sit down with each customer, help define their requirements and standards and then build a customized solution or (2) provide them with a tool-kit that enables them to efficiently build and deploy their own solution. As a result the effort and cost to an organization of implementing a solution to manage non-customer data is much higher.
Wouldn’t it be great if there were agreed data quality standards and references for all data types? Vendors could then develop generic, out of the box products and life would be easier and better for everyone….
There are some areas where this is happening, but we still have a long way to go. How can the data quality industry help to facilitate this?
