Takeaways from MDM Summit Europe 2016

Henrik Gabs LiliendahlLeave a comment

Yesterday I popped in at the combined Master Data Management Summit Europe 2016 and Data Governance Conference Europe 2016.

This event takes place Monday to Thursday, but unfortunately I only had time and money for the Tuesday this year. Therefore, my report will only be takeaways from Tuesday’s events. On a side note the difficulties in doing something pan-European must have troubled the organisers of this London event as avoiding the UK May bank holidays has ended in starting on a Monday where most of the rest of Europe had a day off due to being Pentecost Monday.

MDM

Tuesday morning’s highlight for me was Henry Peyret of Forrester shocking the audience in his Data Governance keynote by busting the myth about the good old excuse for doing nothing, being the imperative of top-level management support, is not true.

Back in 2013 I wondered if graph databases will become common in MDM. Certainly graph databases has become the talk of the town and it was good to learn from Andreas Weber how the Germany based figurine manufacturer Schleich has made a home grown PIM / Product MDM solution based on graph database technology.

Ivo-Paul Tummers of Jibes presented the MDM (and beyond) roadmap for the Dutch food company Sligro. I liked the alley of embracing multi-channel, then omnichannel with self-service at the end of the road and how connect will overtake collect during this journey. This is exactly the reason of being for the Product Data Lake venture I am working on right now.

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Take an Ultra Short Survey on Product Data Exchange

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How do you exchange product data with your trading partners today? At the Product Data Lake we would like to know some more about that. We do expect that many still send eMails with spreadsheets and digital assets. But please tell us how it is with you. Take the survey by clicking here.

Survey

Also please comment on this blog post on your plans or if you work with Product Information Management (PIM) as a service provider and have experiences to share.

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Adding Business Ecosystems to Omnichannel

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Omichannel has become a buzzword in marketing and beyond. The jury is still out on what omnichannel really is, but most will agree that it is a refinement and/or extension of earlier known buzzwords as multichannel and cross channel. You may learn more in this article.

In omnichannel you will try really, really hard to have a single customer view across all channels, and you will try really, really, really hard to present your product information in a uniform and consistent way across all channels.

One challenge here is that your business is not an island. You are part of a business ecosystem, or several of them, as examined in the post Data Management for Business Ecosystems.

“Your customer” may look at “your product” in the sphere of another member of your business ecosystem. It may be at one of your trading partners or at one of your competitors.

So, what can you do about this when it comes to data management?

In the hard case, your competitors, it is about knowing more about your customer. Knowing about your customers relationships. Knowing about your customers relations with products and their categories. Knowing about your customer’s locational belonging. All in all the case of multidomain MDM as seen in the post Multi-Domain MDM and Data Quality Dimensions.

Omni
Expand digitilization across business ecosystems from single purposes to cover an omnichannel view

Besides your own product information you must register what you know about that product information as it is stored and handled by other members in your business ecosystem – trading partners and competitors.

With product information, you must be able to exchange that with your trading partners. You cannot expect that everyone is handling the information about the same product in exact the same way as you. Actually you should not want that. You want to be better than your competitors in some ways and you want to add value for your trading partners. But you would for sure find value in joining a place of intersection where common known characteristics about products are exchanged between trading partners – such as the Product Data lake.

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Self-service Ready Product Data

Henrik Gabs Liliendahl2 Comments

The increased use of self-service based sales approaches as in ecommerce has put a lot of pressure on cross company supply chains. Besides handling the logistics and controlling pricing, you also have to take care of a huge amount of product data and digital assets describing the goods.

You may divide product information into these five levels:

Product Information Levels

Please learn more about the five levels of product information, including how hierarchies, pricing and logistics fits in, by visiting the product information castle.

Level 4 in this model is self-service product data being:

  • Product attributes, also sometimes called product properties or product features. These are up to thousands of different data elements that describes a product. Some are very common for most products like height, length, weight and colour. Some are very specific to the product category. This challenge is actually the reason of being for dedicated Product Information Management (PIM) solutions.
  • Basic product relations are the links between a product and other products like a product that have several different accessories that goes with the product or a product being a successor of another now decommissioned product.
  • Standard digital assets are documents like installation guides, line drawings and data sheets.

These are the product data that helps the end customer comparing products and making an objective choice when buying a product for a specific purpose of use. These data are also helpful in answering the questions a buyer may have when making a purchase.

Every piece of data belonging to any level of product information may be forwarded through the cross company supply chain from the manufacturer to the end seller. Self-service product data are however the data that most obviously will do so.

In order to support end customer self-service when producing, distributing and selling goods you must establish a process driven service that automates the introduction of new products with extensive product data, the inclusion of new kinds of product data and updates to those data. You must be a digitalized member of your business ecosystem. The modern solution for that is the Product Data Lake.

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Kinky Boots and Booths in London

Henrik Gabs Liliendahl1 Comment

Kinky BootsI am looking forward to visiting London in a fortnight and have already secured tickets for the new musical called Kinky Boots.

Another option is to pop in at the Master Data Management Summit Europe 2016 and the co-located Data Governance Conference Europe 2016 and visit the kinky booths where the exhibitors will tell you about their latest inventions. Someone to see could be:

SemarchySemarchy, who has always been kind of kinky with their evolutionary MDM approach as told in the post Eating the MDM Elephant. Last autumn I visited Semarchy in Lyon and it would be good to catch up with FX,  Richard and other good people from this exciting MDM vendor.

AtaccamaAtaccama has a kinky logo. Also on a recent engagement, we have been working with the data quality analyzer tool from Ataccama. So will be good to learn about all the other stuff as for example the big data analyzer.

Stibo SystemsStibo Systems, where I worked some years ago, has just released their new version 8.0 of STEP Trailblazer. This version has an enhanced web user interface. While STEP has always had lots of good functionality, I think many STEP users will welcome a more kinky user interface.

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Multilingual? Mais oui! Natürlich.

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Is that piece of data wrong or right? This may very well be a question about in what language we are talking about.

In an earlier double post on this blog I had a small quiz about the name of the Pope in the Catholic church. The point was that all possible answers were right as explained in post When Bad Data Quality isn’t Bad Data. The thing is that the Pope over the wold has local variants over the English name Francis. François in French, Franziskus in German, Francesco in Italian, Francisco in Spanish Franciszek in Polish, Frans in Danish and Norwegian and so on.

In today’s globalized, or should I say globalised, world, it is important that our data can be represented in different languages and that the systems we use to handle the data is built for that. The user interface may be in a certain flavor/flavour of English only, but the data model must cater for storing and presenting data in multiple languages and even variants of languages as English in its many forms. Add to that the capability of handling other characters than Latin in other script systems than alphabets as examined in the post called Script Systems.

This challenge is very close to me right when we are building a service for sharing product information in business ecosystems. So will the Product Data Lake be multilingual? Mais oui! Natürlich. Jo da.

PDL Example

PS: The Product Data Lake will actually help with collecting product information in multiple languages through the supply chains of product manufacturers, distributors, retailers and end users.

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Starting up at the age of 56

Henrik Gabs Liliendahl8 Comments

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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What is Best Practice: Customer- and Vendor- or Unified Party Master Data Management?

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Right now there is a good discussion going on in the Multi-Domain MDM Group on LinkedIn. A member asks:

“I’d like to hear back from anyone who has implemented party master data in either a single, unified schema or separate, individual schemas (Vendor, Customer, etc.).

What were the pros and cons of your approach? Would you do it the same way if you had it to do again?”

Multi-Side MDMThis is a classic consideration at the heart of multi-domain MDM. As I see it, and what I advise my clients to do, is to have a common party (or business partner) structure for identification, names, addresses and contact data. This should be supported by data quality capabilities strongly build on external reference data (third party data). Besides this common structure, there should be specific structures for customer, vendor/supplier and other party roles.

This subject was also recently examined here on the blog in the post Multi-Side MDM.

What is your opinion and experience with this question? Please have your say either here on the blog or in the LinkedIn Multi-Domain MDM Group.

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No more time zones from 2020

Henrik Gabs Liliendahl2 Comments

The digital age hasno more timezones a lot of consequences in our life and the next big reform is the end of the time zones.

As most shops nowadays are web shops being open 24/7 and many people work around the clock from home, travel and anywhere else, we really don’t need time zones around the world anymore.

Therefore, the United Nations have decided that everyone will be on UTC from 1st January 2020.

There will only be a few exceptions:

  • The US Midwest will g.. d.. it stay one their usual time zone.
  • Switzerland will have their separate time zone, the so called cuckoo clock time.
  • The UK prime minister has decided that there first will be a referendum about this in the UK if he wins the next three general elections.

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Data Management for Business Ecosystems

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Business ecosystems is an important concept of the digital age. The father of business ecosystems, James F. Moore, defined business ecosystems as:

“An economic community supported by a foundation of interacting organizations and individuals—the organisms of the business world. The economic community produces goods and services of value to customers, who are themselves members of the ecosystem. The member organisms also include suppliers, lead producers, competitors, and other stakeholders”.

The problem with data management methodologies and tools today, as I see it, is that they emphasizes on the needs inside the corporate walls of a single company without much attention to, that every single company is a member of one or several business ecosystems as examined in the post called MDM and SCM: Inside and outside the corporate walls.

Opening your data management, including your Master Data Management (MDM), up to the outside is scary business, as the ecosystems often will include your competitors as well as mentioned in the post Toilet Seats and Data Quality.

Nevertheless, if you want your company to survive in the digital age by building up your company’s digitilazation effort you have to extend your data management strategy to encompass the business ecosystems where you are a member.

And now some promotion:

Helene light 03
The Product Data Lake: A tool for business ecosystems

Take A Quick Tour around the Product Data Lake

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