Multi-Domain MDM and Data Quality Dimensions

Henrik Gabs Liliendahl5 Comments

The most frequently mentioned domains within Master Data Management (MDM) are customer, product and location. Data quality is a core discipline when working with MDM. In data quality we talk about different dimensions as uniqueness, relevance, completeness, timeliness, precision, conformity and consistency.

While these data quality dimensions apply to all domains of MDM, some different dimensions apply a bit more to one of the domains or the intersections of the domains.

Below is a figure with an attempt to illustrate where the dimensions belong the most:

Multi-Domain MDM and Data Quality Dimensions

Uniqueness is the most addressed data quality dimension when it comes to customer master data. Customer master data are often marred by duplicates, meaning two or more database rows describing the same real world entity. There are several remedies around to cure that pain. These remedies are explored in the post The Good, Better and Best Way of Avoiding Duplicates.

With product master data, uniqueness is a less frequent issue. However, completeness is often a big pain. One reason is that completeness means different requirements for different categories of products as explained in the post Hierarchical Completeness within Product Information Management.

When working with location master data consistency can be a challenge. Addressing, so to speak, the different postal address formats around the world is certainly not a walkover. Even google maps does not have all the right answers as told in the post Sometimes Big Brother is Confused.

In the intersection between the location domain and the customer domain the data quality dimension called precision can be hard to manage as reported in the post A Universal Challenge. What is relevant to know about your customers and what is relevant to tell about your products are essential questions in the intersection of the customer and product master data domains.

Conformity of product data is related to locations. Take unit measurement. In the United States the length of a small thing will be in inches. In most of the rest of the world it will be in centimetres. In the UK you can never know.

Timeliness is the everlasting data quality dimension all over.

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Chinese Whispers and Data Quality

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There is a game called Chinese Whispers or Broken Telephone or some other names. In that game, one person whispers a message to another person. The message is passed through a line of people until the last player announces the message to the entire group. At that point the message is often quite different or very shortened. The reasons for that is human unreliability including how we put our own perceptions and filters into a message.

When working with data quality you often see the same phenomenon when data is passed through a chain. One area I have observed in recent years is within Product Information Management (PIM). Here the chain is not just the data chain within a given company but the whole data chain in ecosystems of manufacturers, distributors, retailers and end users.

While Product Information Management (PIM) solutions and Product Master Data Management (Product MDM) solutions – if there is a difference – address the issues within a given company, we haven’t seen adequate solutions for solving the problem in the exchange zones between trading partners.

Broken data supply chain

From what I have seen the solutions that upstream providers of product data work with and the solutions that downstream receivers of product data work with will not go well together.

Consequently, I am right now working with a solution to end Chinese whispers in product data supply chains. Check out the Product Data Lake.

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Is big data all about analytics?

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My answer to the question in the title of this blog post is NO. In my eyes big data is not just data warehouse 3.0. It is also data quality 3.0.

The concept of the data lake is growing in popularity in the big data world and so are the counts of warnings about your data lake becoming a data swamp, a data marsh or a data cesspool. Doing analytic work on a nice data lake sounds great. Doing it in a huge swamp, a large marsh or a giant cesspool does not sound so nice.

Figure 1In nature a lake stays fresh by having good upstream supply of water and a downstream system as well. In kind of the same way your data lake should not be a closed system or a dump within your organization.

Sharing data with the outside must be a part of your big data approach. This goes for including traditional flavours of big data as social data and sensor data as well what we may call big reference data being pools of global data and bilateral data as explained on this blog on the page called Data Quality 3.0.

The BrightTalk community on Big Data and Data Management has a formidable collection of webinars and videos on big data and data management topics. I am looking forward to contribute there on the 25th June 2015 with a webinar about Big Reference Data.

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Toilet Seats and Data Quality

Henrik Gabs Liliendahl1 Comment

When working with data quality in the product master data management domain you are very dependent on your business partners. Product master data are shared along with the physical products in the ecosystem of manufacturers, distributors, retailers and end users.

Toilet seatIn a current role, I have worked a lot with sourcing product data from suppliers. One of our recurring examples is about one of our product categories being toilet seats. In that context, we have three different kind of suppliers:

  • Those who use the term “toilet seat” in their product descriptions. That is marvelous, then we can use that part of the product description directly as it is. Wonderful data quality.
  • Those who only use the term “seat” in their product description. Well, it is not really bad data quality for a dedicated manufacturer of bathroom stuff, because what could a seat else be in that context. However, for consistency reasons we have to correct “seat” into “toilet seat”.
  • Those who use the term “WC seat”. Actually, “WC seat” could be more accurate than “toilet seat”, because we are talking about seats for a room with water opposite to older solutions. Nevertheless, for consistency reasons we have to correct “WC seat” into “toilet seat”.

Manufacturers, distributors and retailers have to work together in order to create win-win situations by sharing product data with an optimal data quality. This is however not straight forward, as you always will be part of an ecosystem where your competitors operate too and often you are not prepared to share the same seat as your competitor.

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Growing Weight on Business Rules in MDM

Henrik Gabs Liliendahl1 Comment

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.

Fellow MDM professional Roberto Lichtenstein recently published a LinkedIn pulse post called “MDM and business rules” survey outcome.

One of the survey results was about how the last 3 years behaviour of managing business rules has developed:

MDM and business rules

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.

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Master Data Agility and Business Agility

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The term “data agility” was aired recently in a Forbes.com article by H.O. Maycotte. The article is called Ready, Set, Go – How Fast Is Your Data?

The article revolves around getting your data more fit. Notably, it is not about getting data fit for a known purpose of use, which is the thinking that has been around in the data and information quality realm for years. It is about having the data that makes you able to quickly adjust business strategies to meet changing customer needs.

AgileSome of this data will be master data. Master data is arguably the most difficult kind of data to work with in order to achieve data agility. This challenge was examined in the post Business Agility, Continuous Improvement and MDM.

A week ago I had the pleasure of hosting a workshop on the linkage between Business Process Management (BPM) and Master Data Management (MDM) at the Marcus Evans MDM conference in Barcelona, Spain. One of the solutions we referred to many times was to establish a common reporting approach across BPM and MDM grounded on the sentiment that you can’t manage what you can’t measure.

Setting improved agility as a goal for a master data programme is an additional approach. I am working on such a programme right now. Our executive sponsor actually wanted selling more stuff to be the goal. My promise is that the improved master data agility will lead to improved business agility that will lead to being able to sell more stuff in the future.

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Integration Matters

Henrik Gabs Liliendahl2 Comments

A recent report from KDR Recruitment takes a snapshot of the current state of the world of data in order to uncover some of the most pressing issues facing the Information Management industry and get a sense of what changes may be on the horizon.

One of the clearest findings was around what drives the selection of information software. The report states: “New software must integrate easily into existing infrastructure and systems. This is far and away the most important consideration for users, who also want that same flexibility to extend to customisation options and reporting functionalities.”

The graphic looks like this:

Integration

The ease of integration is in my experience indeed a very important feature when selecting (and selling) a data management tool. Optimally it should not be so because you can end up with not solving the business issue in a nice integrated way. But without integration a new data management tool will live in yet another silo probably only solving some part of the business issue.

The report from KDR Recruitment also covers where you use data to improve performance, the barriers to implementing an informational management strategy and other data management topics.You can read the full report called Not waving but drowning – The State of Data 2015 here.

PS: Kudos to KDR Recruitment for actually engaging in the sector where they work and doing so on social media. Very much in contrast to recruiters who just spam LinkedIn groups with their job openings.

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The Data Quality Market Just Passed 1 Billion USD

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The Data Quality Landscape – Q1 2015 from Information Difference is out. A bit ironically, the report states that the data quality market for the calendar year 2014 was worth a fraction over $1 billion. As the $ sign  could mean a lot of different currencies like CAD, AUD or FJD this statement is very ambiguous, but I guess Andy Hayler means USD.

dollarWhile there still is a market for standalone data quality tools an increasing part of data quality tooling is actually made with tools being a Master Data Management (MDM) tool, a Data Governance tool, an Extract Load and Transform (ETL) tool, a Customer Relationship Management (CRM) tool or an other kind of tool or software suite.

This topic was recently touched on this blog in the post called Informatica without Data Quality? Herein the reasons behind why the new owners of Informatica did not mention data quality as a future goodie in the Informatica toolbox was examined.

In a follow up mail an Informatica officer explained: “As you know Data Quality has become an integral part of multidomain MDM and of the MDM fueled Product Catalog App. We still serve pure DQ (Data Quality) use cases, but we see a lot growth in DQ as part of MDM initiatives”.

You can read the full DQ Landscape 2015 here.

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Global MDM versus Local BPM

Henrik Gabs Liliendahl5 Comments

The linkage between Master Data Management (MDM) and Business Process Management (BPM) was intensively discussed at a workshop on a MDM conference organized by Marcus Evans in Barcelona, Spain today. More than 30 master data professionals from a range of large mainly European originated companies attended the workshop.

There was a broad agreement about that the intersection between MDM and BPM is growing – and should be doing so.

Google EarthOne of the challenges identified is that MDM tends to be global within the enterprise while BPM tends to be local.

The global versus local theme has frequently been mentioned as a challenge over the decade MDM has existed as a discipline. The core MDM global versus local challenges spans over common definitions, common value tables and common data models across different geographies. Having a mix of common business rules and business rules that have to be local adds to the difficulties. When applying the full impact of business process management with the variety of formal and informal organizational structures, decision rules and working culture there are certainly both wins and obstacles in linking MDM and BPM.

I think the commonly used phrase about thinking globally and acting locally makes sense in the intersection between MDM and BPM. Thinking big and starting small helps too.

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Royal Data Quality

Henrik Gabs Liliendahl2 Comments

The intersection of royalty and data quality was touched on this blog in the post Royal Exceptions 5 years ago, when the Queen of Denmark turned 70 years old. Now when her Majesty just rounded the 75-year mark, it is time to revisit the subject.

Royal Coat of Arms of DenmarkAs always when a Royal event is around the debate on the reason of being for a Royal House stirs up. Putting the historical and emotional arguments aside, let us have a look on the economic arguments.

In Denmark there are two main arguments in favor of having a Royal House:

  • Having a president instead of a Royal House will cost the same anyway
  • The costs of the Royal House is less than the wins from brand value when exporting goods and services

Cost of having a president versus a Royal House

The idea of an expensive presidency is probably founded in looking at the amount of money countries like the United States and France puts into the safety and glory of their presidency.

On the other hand, countries may make their own choice on the level of costs for a presidency. If you look at countries like Ireland and Finland, countries of similar size of population as Denmark, their costs for the presidency is only a fraction of the costs of the Danish Royal House.

Brand Value of the Royal House

Even high-ranking executives in large Danish companies often make the argument of a high brand value attached to the Royal House. However, I doubt they have checked with their own business intelligence department.

In fact, there have not been made a single public available study on the matter, and I doubt any business college researcher will risk the career on doing so.

There was a comic situation some years ago when it was taunted that there was a correlation between Denmark getting a crown princess from Australia and a sharp rise in the Danish export to Australia. The Mary-effect it was called. Sadly, for royalists at least, a sense check revealed that Norway and Sweden had the same development without importing a crown princess from Australia.

Conclusion

I hope the above examples are Royal Exceptions and most other decisions around are taken based on carefully considered facts.

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