The Future of Master Data Management

Back in 2011 Gartner, the analyst firm, predicted that these three things would shape the Master Data Management (MDM) market:

  • Multi-Domain MDM
  • MDM in the Cloud
  • MDM and Social Networks

The third point was in 2012, after the raise of big data, rephrased to MDM and Big Data as reported in the post called The Big MDM Trend.

In my experience all these three themes are still valid with slowly but steadily uptake.

open-doorBut, have any new trends showed up in the past years?

In a 2015 post called “Master Data Management Merger Tardis and The Future of MDM” Ramon Chen of Reltio puts forward some new possibilities to be discussed, among those Machine Learning & Cognitive computing. I agree with Ramon on this theme, though these have been topics around in general for decades without really breaking through. But we need more of this in MDM for sure.

My own favourite MDM trend is a shift from focussing on internally captured master data to collaboration with external business partners as explained in the post MDM 3.0 Musings.

In that quest, I am looking forward to my next speaking session, which will be in Helsinki, Finland on the 8th December. There is an interview on that with yours truly available on the Talentum Master Data Management 2015 site.

It is Magic Quadrant Week

Earlier this week this blog featured the Magic Quadrant for Customer MDM and the Magic Quadrant for Product MDM. Today it is time to have a look at the just published Magic Quadrant for Data Quality Tools.

Last year I wondered if we finally will see that data quality tools will focus on other pain points than duplicates in party data and postal address precision as discussed in the post The Multi-Domain Data Quality Tool Magic Quadrant 2014 is out.

Well, apparently there still isn’t a market for that as the Gartner report states: “Party data (that is, data about existing customers, prospective customers, citizens or patients) remains the top priority for most organizations: Almost nine in 10 (89%) of the reference customers surveyed for this Magic Quadrant consider it a priority, up from 86% in the previous year’s survey.”

Multi-Domain MDM and Data Quality DimensionsFrom own experience in working predominantly with product master data during the last couple of years there are issues and big pain points with product data. They are just different from the main pain points with party master data as examined in the post Multi-Domain MDM and Data Quality Dimensions.

I sincerely believes that there are opportunities in providing services to solve the specific data quality challenges for product master data, that, according to Gartner, “is one of the most important information assets an organization has; second-only, perhaps, to customer master data”. In all humbleness, my own venture is called the Product Data Lake.

Anyway, as ever, Informatica is our friend when it comes to free copies of a data management quadrant. Get a free copy of the 2015 Magic Quadrant for Data Quality Tools here.

The Perhaps Second Most Important MDM Quadrant 2015 is Out

This year the Gartner Magic Quadrant for Master Data Management of Product Data Solutions is published very shortly after the Gartner Magic Quadrant for Master Data Management of Customer Data Solutions. Now 1 day in between. I hope this is a sign of that the two MDM quadrants eventually will melt into a (Multi-Domain) MDM Quadrant as touched yesterday in my post about the Customer MDM Quadrant.

MDM Brands

This is not the quadrant, just some vendor names

The product MDM quadrant states: “Product master data is one of the most important information assets an organization has; second-only, perhaps, to customer master data”. In my humble opinion, I think you can refine that statement. It depends on the number of customers (or other party roles) versus the number of products you deal with. Highest number names the most important domain to start with in your organization.

As usual Informatica seems to be the fastest MDM vendor measured on providing a free copy of the Gartner quadrants. Find the 2015 Product MDM Quadrant here from Informatica.

Two Ways of Exploiting Big Data with MDM

MDM Wordle

This is not the quadrant, just some vendor names

The Gartner 2015 Magic Quadrant for Master Data Management of Customer Data Solutions is out. One way of getting the report without being a Gartner customer is through this link on the Informatica site.

Successful providers of Mater Data Management (MDM) solutions will sooner or later need to offer ways of connecting MDM with big data.

In the Customer MDM quadrant Gartner, without mentioning if this relates to customer MDM only or multi-Domain MDM in general, mentions two ways of connecting MDM with big data:

  • Capabilities to perform MDM functions directly against copies of big data sources such as social network data copied into a Hadoop environment. Gartner have found that there have been very few successful attempts (from a business value perspective) to implement this use case, mostly as a result of an inability to perform governance on the big datasets in question.
  • Capabilities to link traditionally structured master data against those sources. Gartner have found that this use case is also sparse, but more common and more readily able to prove value. This use case is also gaining some traction with other types of unstructured data, such as content, audio and video.

My take is that these ways applies to the other MDM domains (supplier, product, location, asset …) as well – just as I think Gartner sooner or later will need to make only one MDM quadrant as pondered in the post called The second part of the Multi-Domain MDM Magic Quadrant is out.

Also I think the ability to perform governance on big datasets is key. In fact, in my eyes master data will tend to be more externally generated and maintained, just like big data usually is. This will change our ways of doing information governance as discussed in my previous post on this blog. This post was by the way inspired by the Gartner product MDM person. The post is called MDM and SCM: Inside and outside the corporate walls.

MDM and SCM: Inside and outside the corporate walls

QuadrantIn my journey through the Master Data Management (MDM) landscape, I am currently working from a Supply Chain Management (SCM) perspective. SCM is very exciting as it connects the buy-side and the sell-side of a company. In that connection we will be able to understand some basic features of multi-domain MDM as touched in a recent post about the MDM ancestors called Customer Data Integration (CDI) and Product Information Management (PIM). The post is called CDI, PIM, MDM and Beyond.

MDM and SCM 1.0: Inside the corporate walls

Traditional Supply Chain Management deals with what goes on from when a product is received from a supplier, or vendor if you like, to it ends up at the customer.

In the distribution and retail world, the product physically usually stays the same, but from a data management perspective we struggle with having buying views and selling views on the data.

In the manufacturing world, we sees the products we are going to sell transforming from raw materials over semi-finished products to finished goods. One challenge here is when companies grow through acquisitions, then a given real world product might be seen as a raw material in one plant but a finished good in another plant.

Regardless of the position of our company in the ecosystem, we also have to deal with the buy side of products as machinery, spare parts, supplies and other goods, which stays in the company.

MDM and SCM 2.0: Outside the corporate walls

SCM 2.0 is often used to describe handling the extended supply chain that is a reality for many businesses today due to business process outsourcing and other ways of collaboration within ecosystems of manufacturers, distributors, retailers, end users and service providers.

From a master data management perspective the ways of handling supplier/vendor master data and customer master data here melts into handling business-partner master data or simply party master data.

For product master data there are huge opportunities in sharing most of these master data within the ecosystems. Usually you will do that in the cloud.

In such environments, we have to rethink our approach to data / information governance. This challenge was, with set out in cloud computing, examined by Andrew White of Gartner (the analyst firm) in a blog post called “Thoughts on The Gathering Storm: Information Governance in the Cloud”.

Spectre vs James Bond and the Unique Product Identifier

bond_24_spectreThe latest James Bond movie is out. It is called Spectre. Spectre is the name of a criminal organization.

In the movie “Bond, James Bond” alias 007 and in this case Mickey Mouse sneaks into a Spectre meeting. At that meeting the Spectre folks reports how they maliciously earns money. One way is selling falsified medicine.

Of course Bond hits Spectre hard during the movie. And if Bond didn’t hit all the villains, data management will do so related to falsified medicine.

The method is using a unique product identifier.

Usually in master data management, we describe a product to the level of unique characteristics also called a Stock Keeping Unit (SKU). In the pharmaceutical world that will typically be a brand name, a concentration of active substances, a dosage type and pack size and possibly a destination country.

From the electronics and machinery sectors, we know the approach of assigning each physical instance of the product a serial number. The same approach is becoming mandatory for medicine in more and more countries. The pharmaceutical manufacturers will assign a unique number to every package (and sometimes also shipping boxes) and report those to the health care authorities around the world. At the point of delivery, it is checked that the identifier equals an original product instance.

The identifier is formed by a product identifier being a Global Trade Identification Number (GTIN) or a National Drug Code (NDC) plus a randomly assigned serial number, making it hard to guess the serial number part.

The World of Reference Data

Google EarthReference Data Management (RDM) is an evolving discipline within data management. When organizations mature in the reference data management realm we often see a shift from relying on internally defined reference data to relying on externally defined reference data. This is based on the good old saying of not to reinvent the wheel and also that externally defined reference data usually are better in fulfilling multiple purposes of use, where internally defined reference data tend to only cater for the most important purpose of use within your organization.

Then, what standard to use tend to be a matter of where in the world you are. Let’s look at three examples from the location domain, the party domain and the product domain.

Location reference data

If you read articles in English about reference data and ensuring accuracy and other data quality dimensions for location data you often meet remarks as “be sure to check validity against US Postal Services” or “make sure to check against the Royal Mail PAF File”. This is all great if all your addresses are in the United States or the United Kingdom. If all your addresses are in another country, there will in many cases be similar services for the given country. If your address are spread around the world, you have to look further.

There are some Data-as-a-Service offerings for international addresses out there. When it comes to have your own copy of location reference data the Universal Postal Union has an offering called the Universal POST*CODE® DataBase. You may also look into open data solutions as GeoNames.

Party reference data

Within party master data management for Business-to-Business (B2B) activities you want to classify your customers, prospects, suppliers and other business partners according to what they do, For that there are some frequently used coding systems in areas where I have been:

  • Standard Industrial Classification (SIC) codes, the four-digit numerical codes assigned by the U.S. government to business establishments.
  • The North American Industry Classification System (NAICS).
  • NACE (Nomenclature of Economic Activities), the European statistical classification of economic activities.

As important economic activities change over time, these systems change to reflect the real world. As an example, my Danish company registration has changed NACE code three times since 1998 while I have been doing the same thing.

This doesn’t make conversion services between these systems more easy.

Product reference data

There are also a good choice of standardized and standardised classification systems for product data out there. To name a few:

  • TheUnited Nations Standard Products and Services Code® (UNSPSC®), managed by GS1 US™ for the UN Development Programme (UNDP).
  • eCl@ss, who presents themselves as: “THE cross-industry product data standard for classification and clear description of products and services that has established itself as the only ISO/IEC compliant industry standard nationally and internationally”. eCl@ss has its main support in Germany (the home of the Mercedes E-Class).

In addition to cross-industry standards there are heaps of industry specific international, regional and national standards for product classification.

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