Depending on the nature of your business and the relationship that you have with your customers you may have several different types of customer master data that you choose to manage and maintain. There are other types of customer data that you may need to manage too, data types that you don’t necessarily always think of as master data but which may benefit from being stored and retrieved centrally as required.
Quantitative and Qualitative
Typically, you can think of customer data as falling into one of two camps, either qualitative or quantitative. So what is the difference?
If you think of this in the context of survey data, for example, quantitative data is values that are representative summations or counts. Each data element is exclusively numerical. The values are quantifiable and can be used in statistical and mathematical analyses and calculations. They’re certain quantities, amounts or ranges. Typically they are also accompanied by measurement units such as kilos, years, metres etc. In the case of say, the height of a person. It makes sense to set boundary limits (validity ranges) to such data. It may also be useful to apply arithmetic operations or calculations to that data, like converting to an alternative unit of measure.
As for qualitative data, in master data for customers, in particular, this may be data values like sizing standards – S for small, M for medium and L for Large and so on. Others might be colours, preferences, types assortments etc. As long as numbers are not assigned, even though they may equate to numbers, this is likely considered qualitative. Similarly, the country, state or province in which a person claims to be a resident is qualitative because the indicator is an attribute characteristic.
Inferred data is conversely derived. It may, for example, be the result of combining data from one or more sources and then effectively joining the dots between data points to draw some sort of conclusion.
Again, you might question, why you would care. The main reason might lie in the fact that your business needs to know the basis on which you hold and maintain customer data and then also consider how you arrive at decisions based on that data.
Declared Data willingly shared by the user through form-fills, cookie opt-ins and submissions through social media accounts often carries the highest value to different aspects of your business as this data is 100% based on the customer or prospect’s activity. Ideally, you will also have gathered a consent indicator from the person who provided this data, which can help inform you on how this data can be used. Optimally, error-free barring deceit on the part of the submitter, you may use this data to determine appropriate access to certain products and services that you offer.
Inferred is often considered amongst the most contentious of data because as the name suggests, you’re joining dots. Data like this is engineered or developed. It has been created without express input from the person that it relates to, it may be systematically generated based on transactions, or activity. This data is neither better, nor worse than declared data, it is simply different and is most contentious, often because it is calculated algorithmically and is based on assumptions, perhaps well-informed, but nonetheless, not declared by the person that they relate to.
You might have a customer who declares that their preferred beverage is coffee but you often see tea or chai in their orders. Does that tell you that they lied? Well no, they have declared their preference to be coffee but they may often order other beverages for others in their party and that’s why you’re seeing an anomaly. You have derived a preference perhaps, from all their known transactions but that doesn’t make your inferred data correct.
So, you have these classes of data, but now let’s consider other aspects of the data that you may have.
Basic data and the rest
This is a very subjective discussion. The main reason is that what one company may consider basic customer data may be considered much more than another company may need. The decision as to what you choose to create and maintain and the reasons you may define the data in different ways may vary wildly from the needs and expectations of a competitor or another industry or even a use case within your organization.
Pretectum’s Customer Master Data management system (CMDM) doesn’t prescribe what you should or should have as that basic data definition, it is entirely up to you. While we may offer some standard models (schemas) and your systems may have specific minimum requirements, those can be supported but the end decision is up to you.
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