I often sit in meetings where I am clearly not the most intelligent person in the room. In fact to think that I am ever the smartest person in the room is to deny the special knowledge that others might have that I am not even aware of. At any rate, as is often the case, some degree of ambiguity and potential confusion is almost inevitable when you have a bunch of people together who are not all on a level field in terms of knowledge and understanding.
The usual indicators that things are not necessarily all well, is when people resort to using acronyms or letter-speak as I call it. You know you need to do a reset when someone brazenly (or bravely) asks for a pause and a clarification on the meaning of the acronym or even abbreviation of a term.
Wage war on acronyms
I would encourage us all to actually call-out acronymization and abbreviation as perhaps one of the greatest sins in meetings of any sort, but most particularly in data management and we should all stop it in its tracks by asking for clarification on an almost naggingly constant basis.
One week pre-COVID19 pandemic was particularly interesting because I had the opportunity to spend some quality time with data practitioners from across the globe and from several spheres of influence.
What I was glad of at the time, was the relative lack of letter-speak but was irked by the injection of some buzzwords into the two-day proceedings. Of course, buzzwords are nothing unusual and many are just appropriations from other spheres of business management, IT and data, but there are some particular ones that seem to be hanging around and have a particular jargon nuance that is hard to line up.
The buzzword bingo card usually gets a special mention at conferences because that is where the jargon becomes particularly popular and where marketeers glom on to particular words and repurpose them in the most bizarre contexts. Common words like scalable, holistic, real-time, insight, imperative and so on, seem to lose their real meaning when overused, overemphasized and incorrectly framed.
As Cathy Nolan over on Dataversity points out, buzzwords are in some cases similar to metaphors that may have regional usage that doesn’t properly translate for a global audience.
So too, with buzzwords, there should be some pain and effort taken to reduce the use of buzzwords and apply more effort to plain speaking especially when it comes to data management.
My most disliked buzzword-ey phrase is the concept of the Single Customer View, something which was re-honed to Single Entity View or something similar. The issue I have with this term is that it implies some sort of panoptical view of the Customer or Entity when in fact it can only mean that in certain kinds of scenarios.
As an example; my ability to pull customer master records from say ERP, CRM and my marketing system and consolidate them into one store of data, only give me the master data view of the customer.
I need to bring in the transactional data to understand how I work or transact with that customer. I need to see what I sell them, what they pay, when they pay and how they pay. I need to see what I ship, where I ship it to and how I ship for that customer and then a raft of other transactional data that may be stored in many other kinds of environments, such as what they tweet about my products or my company.
So when all I do is collate master records and perhaps blend them to create a single record for pushing back down to those source systems, what am I in fact doing? Is this mastering data? Is it the creation of a nominal golden record?
My preferences would be to describe this process of Customer Master Data Consolidation, simply because the issue here is dealing with the many versions of the truth that are present in the many systems that often contain customer records. Some will contain just contact information, some will contain financial and marketing information and some will contain preferences or special terms and conditions.
There is no ubiquitous way of defining a customer record and even if there were, it would vary from industry to industry and quite possibly from country to country.
I can’t really say that this combining of records is even a management process, because unless I create a new reference database of these consolidated or blended records, I am in fact not mastering and managing anything, I am simply deriving a new record based on some business rules that may define what I think a new better customer record should look like.
I may even have a problem pushing any consolidated or revised attributes back down to the source systems because I may not own the relationship with the data or even have easy or proper mechanisms for updating them. Worse still, attributes that I think are discardable in my schema of data may be critical in their intact form, for the original systems because they may relate to special automations, reporting or decision based logic on which basis customers are contacted, serviced or attended to. It is all a little fragile, precarious and potentially problematic.
So in the end my conclusion is that this topic becomes pretty hairy for businesses, pretty quickly and while I don’t have necessarily the best answer for this, I think it is interesting to consider and something that we should seriously evaluate the way we think about. There can be no doubt that with modern technologies we can improve the quality of customer data but we do need to consider that, the idea of more pervasive demands from the business and a greater hunger for data access on something as fundamental as the customer record is pushing the popular concept of data democratization.
I can only look to what the market is doing and what vendors are saying and customers thinking but I think we need a potential reset of this term too, and soon.
