MDM as Managed Service

This month I am going to London to attend the Master Data Management Summit Europe 2017.

As a teaser before the conference Aaron Zornes made a post called MDM Market 2017-18: Facts vs. Beliefs (with apologies to current political affairs fans!).

In his article, Aaron Zornes looks at the slow intake of multi-domain MDM, proactive data governance, graph technology and Microsoft stuff ending with stating that MDM as MANAGED SERVICE = HOT:

“Just as business users increasingly gave up on IT to deliver modest CRM in a timely, cost effective fashion (remember all the Siebel CRM debacles), so too are marketing and sales teams especially looking to improve the quality of their customer data… and pay for it as a “service” rather than as a complex, long-time-to-value capital expenditure that IT manages”.

Master Data ShareI second that, having been working with the iDQ™ service years ago, and will add, that the same will be true for product data as well and then eventually also multi-domain MDM.

How that is going to look like is explained here on Master Data Share.

Is blockchain technology useful within MDM?

This question was raised on this blog back in January this year in the post Tough Questions About MDM.

Since then the use of the term blockchain has been used more and more in general and related to Master Data Management (MDM). As you know, we love new fancy terms in our else boring industry.

blockchainHowever, there are good reasons to consider using the blockchain approach when it comes to master data. A blockchain approach can be coined as centralized consensus, which can be seen as opposite to centralized registry. After the MDM discipline has been around for more than a decade, most practitioners agree that the single source of truth is not practically achievable within a given organization of a certain size. Moreover, in the age of business ecosystems, it will be even harder to achieve that between trading partners.

This way of thinking is at the backbone of the MDM venture called Product Data Lake I’m working with right now. Yes, we love buzzwords. As if cloud computing, social network thinking, big data architecture and preparing for Internet of Things wasn’t enough, we can add blockchain approach as a predicate too.

In Product Data Lake this approach is used to establish consensus about the information and digital assets related to a given product and each instance of that product (physical asset or thing) where it makes sense. If you are interested in how that develops, why not follow Product Data Lake on LinkedIn.

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Data Management Platforms for Business Ecosystems

The importance of looking at your enterprise as a part of business ecosystems was recently stressed by Gartner, the analyst firm, as reported in an article with the very long title stating: Gartner Says CIOs Need to Take a Leadership Role in Creating a Business Ecosystem to Drive a Digital Platform Strategy.

In my eyes, this trend will have a huge impact on how data management platforms should be delivered in the future. Until now much of the methodology and technology for data management platforms have been limited to how these things are handled within the corporate walls. We will need a new breed of data management platforms build for business ecosystems.

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Such platforms will have the characteristics of other new approaches to handling data. They will resemble social networks where you request and accept connections. They will embrace data as big data and data lakes, where every purpose of data consumption are not cut in stone before collecting data. These platforms will predominately be based in the cloud.

Right now I am working with putting such a data management service up in the cloud. The aim is to support product data sharing for business ecosystems. I will welcome you, and your trading partners, as subscriber to the service. If you help trading partners with Product Information Management (PIM) there is a place for you as ambassador. Anyway, please start with following Product Data Lake on LinkedIn.

Adding Things to Product Data Lake

Product Data Lake went live last month. Nevertheless, we are already planning the next big things in this cloud service for sharing product data. One of them is exactly things. Let me explain.

Product data is usually data about a product model, for example a certain brand and model of a pair of jeans, a certain brand and model of a drilling machine or a certain brand and model of a refrigerator. Handling product data on the model level within business ecosystems is hard enough and the initial reason of being for Product Data Lake.

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However, we are increasingly required to handle data about each instance of a product model. Some use cases I have come across are:

  • Serialization, which is numbering and tracking of each physical product. We know that from having a serial number on our laptops and another example is how medicine packs now will be required to be serialized to prevent fraud as described in the post Spectre vs James Bond and the Unique Product Identifier.
  • Asset management. Asset is kind of the fourth domain in Master Data Management (MDM) besides party, product and location as touched in the post Where is the Asset. Also Gartner, the analyst firm, usually in theory (and also soon in practice in their magic quadrants) classifies product and asset together as thing opposite to party. Anyway, in asset management you handle each physical instance of the product model.
  • Internet of Things (IoT) is, according to Wikipedia, the internetworking of physical devices, vehicles (also referred to as “connected devices” and “smart devices”), buildings and other items—embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data.

Fulfilling the promise of IoT, and the connected term Industry 4.0, certainly requires common understood master data from the product model over serialization and asset management as reported in the post Data Quality 3.0 as a stepping-stone on the path to Industry 4.0.

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Connecting Product Information

In our current work with the Product Data Lake cloud service, we are introducing a new way to connect product information that are stored at two different trading partners.

When doing that we deal with three kinds of product attributes:

  • Product identification attributes
  • Product classification attributes
  • Product features

Product identification attributes

The most common used notion for a product identification attribute today is GTIN (Global Trade Item Number). This numbering system has developed from the UPC (Universal Product Code) being most popular in North America and the EAN (International Article Number formerly European Article Number).

Besides this generally used system, there are heaps of industry and geographical specific product identification systems.

In principle, every product in a given product data store, should have a unique value in a product identification attribute.

When identifying products in practice attributes as a model number at a given manufacturer and a product description are used too.

Product classification attributes

A product classification attribute says something about what kind of product we are talking about. Thus, a range of products in a given product data store will have the same value in a product classification attribute.

As with product identification, there is no common used standard. Some popular cross-industry classification standards are UNSPSC (United Nations Products and Service Code®) and eCl@ss, but many other standards exists too as told in the post The World of Reference Data.

Besides the variety of standards a further complexity is that these standards a published in versions over time and even if two trading partners use the same standard they may not use the same version and they may have used various versions depending on when the product was on-boarded.

Product features

A product feature says something about a specific characteristic of a given product. Examples are general characteristics as height, weight and colour and specific characteristics within a given product classification as voltage for a power tool.

Again, there are competing standards for how to define, name and identify a given feature.

pdl-tagsThe Product Data Lake tagging approach

In the Product Data Lake we use a tagging system to typify product attributes. This tagging system helps with:

  • Linking products stored at two trading partners
  • Linking attributes used at two trading partners

A product identification attribute can be tagged starting with = followed by the system and optionally the variant off the system used. Examples will be ‘=GTIN’ for a Global Trading Item Number and ‘=GTIN-EAN13’ for a 13 character EAN number. An industry geographical tag could be ‘=DKVVS’ for a Danish plumbing catalogue number (VVS nummer). ‘=MODEL’ is the tag of a model number and ‘=DESCRIPTION’ is the tag of the product description.

A product classification tag starts with a #. ‘#UNSPSC’ is for a United Nations Products and Service Code where ‘#UNSPSC-19’ indicates a given main version.

A product feature is tagged with the feature id, an @ and the feature (sometimes called property) standard. ‘EF123456@ETIM’ will be a specific feature in ETIM (an international standard for technical products). ‘ABC123@ECLASS’ is a reference to a property in eCl@ss.

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Social Selling: Does it Work?

Social Master Data Management (Social MDM) has been on my radar for quite a long time. Social MDM is the natural consequence of Social CRM and social selling.

Social MDMNow social selling has become very close to me in the endeavour of putting a B2B (Business-to-Business) cloud service called Product Data Lake on the market.

In our quest to do that we rely on social selling for the following reasons:

  • If we do not think too much about, that time is money, social selling is an inexpensive substitution for a traditional salesforce, not at least when we are targeting a global market.
  • We have a subscription model with a very low entry level, which really does not justify many onsite meetings outside downtown Copenhagen – but we do online meetings based on social engagement though 🙂
  • The Product Data Lake resembles a social network itself by relying on trading partnerships for exchange of product information.

I will be keen to know about your experiences and opinions about social selling. Does it work? Does it pay off to sell socially? Does it feel good to buy socially?

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Starting up at the age of 56

It is never too late to start up, I have heard. So despite I usually brag about having +35 years of experience in the intersection of business and IT and a huge been done list in Data Quality and Master Data Management (MDM) which can get me nice consultancy engagements, a certain need on the market has been puzzling in my head for some time.

Before that, when someone asked me what to do in the MDM space I told them to create something around sharing master data between organisations. Most MDM solutions are sold to a given organization to cover the internal processes there. There are not many solutions out there that covers what is going on between organizations.

But why not do that myself? – with the help of some younger people.

FirstLogoSaveYou may have noticed, that I during the last year have been writing about something called the Product Data Lake. This has until recently mostly just been a business concept that could be presented on power point slides. So called slideware. But now it is becoming real software being deployed in the cloud.

Right now a gifted team in Vietnam, where I also am this week, is building the solution. We aim to have it ready for the first trial subscribers in August 2016. We will also be exhibiting the solution in London in late September, where we will be at the Start-up Alley in the combined Customer Contact, eCommerce and Technology for Marketing exhibition.

At home in Denmark, some young people are working on our solution too as well as the related launching activities and social media upbeat. This includes a LinkedIn company page. For continuous stories about our start-up, please follow the Product Data Lake page on LinkedIn here.

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