In legal lingo data portability means: “Where the data subject has provided the personal data and the processing is based on consent or on a contract, the data subject shall have the right to transmit those personal data and any other information provided by the data subject and retained by an automated processing system, into another one, in an electronic format which is commonly used, without hindrance from the controller from whom the personal data are withdrawn.”
In other words, if you are processing personal data provided by a (prospective) customer or other kind of end user of your products and services, you must be able to hand these data over to your competitor.
I am sure, this is a new way of handling party master data to almost every business. However, sharing master data with your competitor is not new when it comes to product master data as examined in the post Toilet Seats and Data Quality.
When you buy stuff one of the characteristics you may emphasis on is where the stuff is made: The country of origin.
Buying domestic goods has always been both a political issue and something that in people’s mind may be an extra quality sign. When I lived in The UK I noticed that meat was promoted as British (maybe except from Danish bacon). Now when back in Denmark all meat seems to be best when made in Denmark (maybe except from an Argentinian beef). However, regulations have already affected the made in marking for meat, so you have to state several countries of origins in the product lifecycle.
Country of origin is a product data element that you need to handle for regulatory reasons not at least when moving goods across borders. Here it is connected with commodity codes telling what kind of product it is in the custom way of classifying products as examined in the post Five Product Classification Standards.
When working with product data management for products that moves cross border you are increasingly asked to be more specific about the country of origin. For example, if you have a product consisting of several parts, you must specify the country of origin for each part.
Business rules has always been an important subject when it comes to data quality and Master Data Management (MDM). However, it seems that business rules are considered even more important over the recent years and in the future.
One of the survey results was about how the last 3 years behaviour of managing business rules has developed:
Two third of people answering the question indicated a growing inclusion of business rules (including yours truly in my current main role). So that’s a good growth. However nearly half of respondents did not answer that question, so a bit of caution may be relevant.
As Roberto mentions in his summary post there is a chicken and egg thing with process and data. I also find there is a chicken and egg theme with business rules and MDM. Letting business rules dictate the MDM behaviour is obvious. But MDM can sometimes initiate new business rules as examined in the post To-Be Business Rules and MDM.
An important part of implementing Master Data Management (MDM) is to capture the business rules that exists within the implementing organization and build those rules into the solution. In addition, and maybe even more important, is the quest of crafting new business rules that helps making master data being of more value to the implementing organization.
Examples of such new business rules that may come along with MDM implementations are:
In order to open a business account you must supply a valid Legal Entity Identifier (like Company Registration Number, VAT number or whatever applies to the business and geography in question)
A delivery address must be verified against an address directory (valid for the geography in question)
In order to bring a product into business there is a minimum requirement for completeness of product information.
Creating new business rules to be part of the to-be master data regime highlights the interdependency of people, process and technology. New technology can often be the driver for taking on board such new business rules. Building on the above examples such possibilities may be:
The ability to support real time pick and check of external identifiers
The ability to support real time auto completion and check of postal addresses
The ability to support complex completeness checks of a range of data elements
Much of the talking and writing about data governance these days is about how to start a data governance programme. This includes the roadmap, funding, getting stakeholders interested and that kind of stuff. Focussing on how to get a data governance programme off the ground is natural, as this is the struggle right now in many organizations.
But hopefully when, and not if, the data governance programme has left the ground and is a reality, what does the daily life look like then? I think this drawing can be a good illustration:
As a fan of agile approaches within most disciplines including data governance, it is worth remarking that the daily life should not be seen as an end result of a long implementation. It should certainly be seen as the above concept being upgraded over time in more and more mature versions probably starting with a very basic version addressing main pain points within your organization.
When starting a data governance programme there is typically a lot of existing business rules to be documented in a consistent way. That is one thing. Another thing is to establish the process that deals with data aspects of changing business rules and taking on new business rules as touched in the post Two Kinds of Business Rules within Data Governance.
The ongoing service and the issue resolution part is very much relying on some kind of organizational structure. This could include one of my favourites being collaboration fora between data stewards, maybe a data governance office and usually a data governance council of some name. And perhaps having a Chief Data Officer (CDO) as mentioned in post The Good, the Bad, and the Ugly Data Governance Role.
A very common starting point for producing tangible outcomes in a data governance programme is setting up a business glossary. The alternatives, or next/previous steps, for a business glossary were discussed in the post Metadata Musings by a Nerd.
First, in my eyes a business glossary (or whatever you call such a thing) is indeed a useful deliverable in its own right. In order to support a data governance programme you will need to add things besides definition of terms. One important element is documenting business rules as reported by Nikki Rogers at The University of Bristol here.
A business glossary should ultimately morph into full-blown metadata management or meet data dictionary and/or metadata repository initiatives that also may grow in your organization. What full-blown metadata management means was touched recently by Brian Brewer in a blog post called Gartner Says More Metadata. This post cites a blog post by Darin Stewart of Gartner (the analyst firm). The Gartner post is called Big Content Needs More Metadata.
How did you develop your business glossary?
Did you start with a business glossary and then morphed into the data dictionary and metadata management discipline and lingo?
Did the business glossary grow from the metadata management work?
Is the business glossary just sitting there doing what it does?
When laying out data policies and data standards within a data governance program one the most important input is the business rules that exist within your organization.
I have often found that it is useful to divide business rules into two different types:
External business rules, which are rules based on laws, regulations within industries and other rules imposed from outside your organization.
Internal business rules, which are rules made up within your organization in order to make you do business more competitive than colleagues in your industry do.
External imposed business rules are most often different from country to country (or group of countries like the EU). Internal business rules may be that too but tend to be rules that apply worldwide within an organization.
The scope of external business rules tend to be fairly fixed and so does the deadline for implementing the derived data policy and standard. With internal business rules you may minimize and maximize the scope and be flexible about the timetable for bringing them into force and formalizing the data governance around the rules. It is often a matter of prioritizing against other short term business objectives.
The distinctions between these two kinds of business rules may not be so important in the first implementation of a data governance program but comes very much into play in the ongoing management of data policies and data standards.