MDM

Foreign Affairs

Henrik Gabs Liliendahl2 Comments

There is a famous poster called The New Yorker. This poster perfectly illustrates the centricity we often have about the town, region or country we live in.

The same phenomenon is often seen in data management.

I mentioned United States centricity as a minor criticism in my recent book review about the excellent book “Master Data Management and Data Governance”.  

An example from the book is this statement:

“It is important to differentiate between U.S. domestic addresses and international addresses. This distinction is important for U.S.-centric MDM solutions because U.S. domestic addresses are normally better defined and therefore can be processed in a more automatic fashion, while international addresses require more manual intervention.”

The same fact could be expressed by saying:

“It is important to differentiate between Danish domestic addresses and international addresses. This distinction is important for Danish-centric MDM solutions because Danish domestic addresses are normally better defined and therefore can be processed in a more automatic fashion, while international addresses require more manual intervention.”

Only, the better formatted address in the first case is the messy address in the last case, and the better formatted address in the last case is the messy address in the first case.

If your MDM scope is country-centric it is sensible to concentrate on automation related to that country.

If your MDM scope is international there are two options:

  • The easy way: The one size fits all option. This is a moderate investment, but also, it only yields moderate results in terms of automation and data quality.
  • The hard way: You have to implement specialized automation and investigate best external reference data for each country. I made a Danish-centric post on that last year here.

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Book Review: Berson and Dubov on MDM

Henrik Gabs Liliendahl3 Comments

A few days ago Julian Schwarzenbach over at the Data and Process Advantage Blog published a review of the book “Master Data Management and Data Governance” by Alex Berson and Larry Dubov. Link to Julian’s review here.

And hey, that’s the book I have been reading too during the last months. So why not make my review too.    

I agree very much with Julian’s positive review of the book. It is a very comprehensive book – and thick and heavy I have learned from bringing it with me on travel which is where I usually read offline stuff. But master data management and related data governance is a big and heavy discipline with a lot of details that has to be dealt with.

Probably I have annoyed fellow travellers in trains and airplanes while reading the book with exclamations as: Yes, precisely, that’s what I always have said, good point and so on. Because I agree very much with many of the issues described and the solutions discussed in the book.

For the mandatory bit of criticism that must be included in every book review I will bring on my pet bashing about United States and English language centricity. Well, it’s actually not that bad, as the book at many places does indicate that other angles and pains exist than those being prominent in the United States and with the English language.

Oh, and I bear with that  my surname in the references are spelled “Sorensen” instead of “Sørensen” and that a related date are formatted like “11/22/2009” which will be the 11th day in the 22nd month of the year 2009 to me.     

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What is Identity Resolution?

Henrik Gabs Liliendahl6 Comments

We are continuously struggling with defining what it is we are doing like defining: What is data quality? What is Master Data? Lately I’ve been involved in discussions around: What is Identity Resolution? A current discussion on this topic is rolling in the Data Matching LinkedIn group.

This discussion has roots in one of my blog posts called Entity Revolution vs Entity Evolution. Jeffrey Huth of IBM Initiate followed up with the post Entity Resolution & MDM: Interchangeable? In January Phillip Howard of Bloor made a post called There’s identity resolution and then there’s identity resolution (followed up by a correction post the other day called My bad).

It is a “same same but different” discussion. Traditional data matching (or record linkage) as seen in a data quality tool and master data management solution is the bright view: Being about finding duplicates and making a “single business partner view” (or “single party view” or “single customer view”). Identity resolution is the dark view: Preventing fraud and catching criminals, terrorists and other villains.

The Gartner Hype Cycle describes the dark view as ”Entity Resolution and Analysis”. This discipline is approaching the expectation peak and will, according to Gartner, be absorbed by other disciplines as no one can tell the difference I guess.

Certainly there are poles. In an article from 2006 called Identity Resolution and Data Integration David Loshin said: There is a big difference between trying to determine if the same person is being mailed two catalogs instead of one and determining if the individual boarding the plane is on the terrorist list.

But there is also a grey zone.

From a business perspective for example the prevention of misuse of a restricted campaign offer is a bit of both sides. Here you want to avoid that an existing customer is using an offer only meant for new customers. How does that apply to members of the same household or the same company family tree? Or you want to avoid someone using an introduction offer twice by typing her name and address a bit different.

From a technical perspective I have an example from working with a newspaper in a big fraud scam described in the post Big Time ROI in Identity Resolution. Here I had no trouble using a traditional deduplication tool in discovering non-obvious relationships. Also the relationships discovered in traditional data matching ends up quite nicely in hierarchy management as part of master data management as described in the post Fuzzy Hierarchy Management.

And then there is the use of the words identity (resolution) versus entity (resolution).

My feeling is that we could use identity resolution for describing all kind of matching and linking with party master data and entity resolution could be used for describing all kind of matching and linking with all master data entity types as seen in multi-domain master data management. But that’s just my words.

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Multi-Commerce Data Quality

Henrik Gabs Liliendahl2 Comments

A month ago I wrote about Multi-Channel Data Quality. Multi-Commerce and the related data quality is pretty much another term covering the same challenges which is that despite we today talk a lot about eCommerce, being doing business online, we still have a lot of business going on offline. So we have challenges with online data quality, offline data quality and not at least a single view of online/offline data quality.

According to the Gartner Hype Cycle there is such a thing as Multicommerce Master Data Management. This discipline has just passed the expectation peak but will, according to Gartner, be absorbed by Multidomain Master Data Management on the descent before climbing up again towards enlightenment and productivity.

As data quality and master data management are best friends I find it very likely that Multi-Commerce Data Quality will be all about Multi-Domain Master Data Management, including:

  • Having a single business partner view (that includes single customer view) encompassing all online and offline activities
  • Having a unified way of maintaining and exposing product data online and offline
  • Having the means for doing content management (that includes unstructured data) embracing online presentation as well as offline distribution.    

I also see Multi-Domain Master Data Management as not only doing master data management for several data domains at the same time (with the same software brand), but also exploring the intersections between the different domains.

If you for example look at a customer/product matrix you may add a third dimension being a channel where we examine the relations between a customer type, a product type/attribute and a given channel, thus having a 3D picture of doing business in a multi-commerce environment.

If you are interested in Multi-Domain Master Data Management including how Multi-Commerce Master Data Management and related data quality are developing right now, then please join the LinkedIn group for Multi-Domain MDM by clicking on the puzzle.

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Fuzzy Hierarchy Management

Henrik Gabs Liliendahl6 Comments

When evaluating results from automated data matching your goal is typically to find false positives and false negatives being entities that are matched, but shouldn’t be (false positives) and entities that are not matched, but should have been (false negatives).

However the fuzziness often used in the data matching process also apply to the evaluation of the results as many dubious results isn’t a question about if the matched database rows are reflecting the same real world entity but more a question about if the matched (or not matched) database rows are reflecting different members of a real world hierarchy.

Example 1:

John Smith on 1 Main Street in Anytown
Mary & John Smith on 1 Main Str in Anytown

Example 2:

Anytown Municipality, Technical Dept
Municipality of Anytown

Example 3:

Acme Corporation, Anytown
Acme Corporation, Anywhere

All three examples above may be considered a false positive if matched and a false negative if not matched.

You may say that it depends on the purpose of use, which is true.

But if we are talking master data management we may probably encompass multiple requirements where we simultaneously need the match and don’t want the match, which is why we need to be able to resolve and store the results from fuzzy data matching into hierarchies.

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Single Business Partner View

Henrik Gabs Liliendahl4 Comments

If you search in google for “single customer view” you’ll get over 20,000 hits. If you search for “single business partner view” you’ll get zero – until I just posted this blog post.

Some time ago I wrote about getting a 360° Business Partner View elaborating on extending the 360° Customer View or Single Customer View (SVC) to embrace all sorts of party master data managed within the organization.

In fact there is at least the same amount of similar techniques used between

  • managing supplier master data and business-to business (B2B) customer master data

as there is between

  • managing business-to-business (B2B) customer master data and business-to-consumer (B2C) customer master data.

If you look at Customer Relation Management (CRM) systems almost every package is aimed at managing B2B data as the data model and the functionality supports real world B2B structures and how the sales force and other employees interacts with B2B customers and prospects.

Interacting with B2C customers and prospects is much more diverse and often supported by operational systems specialized for the industry in question like solutions for financial services, healthcare and so on.

A business partner is a party acting in the role as customer, prospect, supplier, reseller, distributor, agent and other forms of partnership. Sometimes the same party is acting in several roles at the same time thus potentially being both on the Sell–side and Buy-side of Master Data Quality management.

As sell side and buy side has intersections within party master data, in some industries we may also go deeper into identity resolution and find intersections between B2B entities and B2C entities. I’ve described these matters in the post So, how about SOHO homes. The business case is that some products in some industries are aimed at the households of business owners and the small businesses at the same time. This is for example true for industries as banking, insurance, telco, real estate and  law.

All in all achieving a single view of business partners is a task going beyond traditional customer data integration (CDI) and stretching into areas traditionally belonging to Product Information Management (PIM). This is a business case for multi-domain master data management.

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What’s a Six Pack?

Henrik Gabs LiliendahlLeave a comment

I have earlier written about my Right the First Time enrolment at the local fitness club and how I geekingly are using the dashboard on the workout equipment to follow my Fitness Data.  

But it is probably (or actually certainly) too early to talk about the term “six pack” related to these efforts.

So let’s talk about a “six pack” related to master data management.

We may for example have a look at “a six pack of Carlsberg lager”.

Sometimes you may ask how many different products you are handling in a master data hub. In answering that question we here may come up with a lot of different numbers all being a Perfect Wrong Answer.

The real world isn’t flat. When dealing with product master data we certainly need to see the world in hierarchies as:

  • Carlsberg lager as such is a product with some attributes and some relations to the customers liking this product or not.
  • The product may be brewed in the original country of origin (Denmark) or at lot of other facilities around the world, thus making it a different product per supplier with respect to some attributes.
  • As a customer you buy the product in a certain packaging like a six pack of cans in a given size with a given label.

The bottom level presented here is what in data management terms is identified as a Stock Keeping Unit (SKU).

Oh, and consuming the last “six pack” is probably (or actually certainly) not good for achieving the first mentioned “six pack”.

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Customer Product Matrix Management

Henrik Gabs Liliendahl2 Comments

A customer/product matrix is a way of describing the relationships between customer types and product types/attributes.  

Example:

Note: Please find some data quality related product descriptions in the post Data Quality and World Food.

Filling out the matrix may be based on prejudices, gut feelings, assumptions, surveys, focus groups or data.

If we go for data we may do this by collecting available historical data related to sales and inquiries made by persons belonging to each customer type regarding products belonging to each product type.  

In doing that correctly we need two kinds of master data management and data quality assurance in place:

  • Customer Data Integration (CDI) for assigning the accurate customer type in the real world related to the uniquely identified person in transactions coming from all sources – here based on location master data.
  • Product Information Management (PIM) for categorizing the relevant fit for purpose product type.

This reminds me about multi-domain master data management. Customer master data (or shall we say party master data), product master data and location master data used to figure out how to do business. I like it – both the master data management part and the mentioned product types.  

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Customer Relationship Mess (CRM)

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I have several times witnessed how a sales department for a lot of good reasons has forced the implementation of a CRM (Customer Relationship Management) software package disconnected from the ERP (Enterprise Resource Planning) system and other applications where customer master data have been handled until then.

The good reasons have been that the current applications didn’t fit the business processes in a dynamic sales department and perhaps that the current monolithic enterprise solution was too inflexible for the business needs in sales.

While this move may have been a great success in sales force automation the downside is often that the single customer view has been limited to a single customer view seen from the windows in the sales department offices.

In order to have a 360 degree view of customer you have to cover all the view points in the enterprise embracing all departments being in contact with the customer and thereby accessing and maintaining customer master data.

Those who feel the pain when a company doesn’t maintain such a view is the customer and those who enjoys when a company have that view is the customer.

Lately I had two experiences as a customer. A bad experience facing a lousy approximately 110 degree customer view from a phone company and a well executed 360 degree view from an insurance company. Both cases haven’t been around one of my favorite subjects being identity resolution. Both companies have my citizen ID.

It is just so that some companies cares more about single department business needs than true customer relationship management. IT’s a mess.

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Where is the Business?

Henrik Gabs Liliendahl6 Comments

In technology enabled disciplines we often like to divide an organization into two distinct parts being IT (Information Technology) and “the business”.

I am aware that we do that to emphasize that our solutions has to be business centric opposite to technology centric. We mustn’t fall into the trap of discussing technology too early and certainly not selecting certain technology brands as the first step of our solutions.

A problem however is where to find “the business” in an organization. The top management surely represents all of the business (including the IT part of the business). But in order to find the so called subject matter experts we are looking down the levels in the organization where people don’t belong to “the business” but to sales, marketing, customer service, purchase, production, human resources, finance and so on.

Some technology enabled disciplines belong to a certain department. But disciplines as (enterprise wide) data quality and master data management are supposed to support most departments. The business. So where do we find the business? And who are we by the way?

Call them?

Assuming it doesn’t matter who we are: Let’s go find “the business”. I guess it doesn’t help calling the reception and ask them to put us through to “the business”. Actually the manned reception probably doesn’t exist today. And it will be surprising to get a machine asking:

  • Do you want to speak with IT? Press 1.
  • Do you want to speak with “the business”? Press 2.

If we are in my home country Denmark we also have a linguistic issue. If I ask google to translate “the business” from English to Danish I get the word “forretningen”. If I ask google to translate “forretningen” from Danish back to English I get the word “shop”. So calling “forretningen” will probably get me to the shop floor. Not a bad place, a true gemba, but maybe not the only one.

Everyone belongs to “the business”

In data quality and master data management there is a question used all over to exemplify a common challenge within these disciplines.

The question is: What is a customer?

The challenge is that people from different departments will have different definitions. Marketing defines a customer one way, sales tend to do it a bit different, finance sees it yet in another way and production has their view point. And the stereotype IT guy defines a customer as a row in the customer table.

So now we are asking for Alexander the Great from “the business” to come cutting the Gordian Knot.

That is probably not going to happen.

More likely someone from any business unit will be able to negotiate a proper conceptual solution covering all requirements from the different business units. And from what I see around it may often be someone who’s human resource master data record is related to the IT part of the business. Or was. The main point is having a holistic view of the business where everyone belongs.    

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