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.
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.
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:
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:
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.
I 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:
Semarchy, 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.
Ataccama 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 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.
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.
You 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.
As reported in the post Gravitational Waves in the MDM World there is a tendency in the MDM (Master Data Management) market and in MDM programmes around to encompass both the party domain and the product domain.
The party domain is still often treated as two separate domains, being the vendor (or supplier) domain and the customer domain. However, there are good reasons for seeing the intersection of vendor master data and customer master data as party master data. These reasons are most obvious when we look at the B2B (business-to-business) part of our master data, because:
These reasons also applies to other party roles as examined in the post 360° Business Partner View.
When we look at the product domain we also have a huge need to connect the buy side and the sell side of our business – and the make side for that matter where we have in-house production.
Multi-Domain MDM has a side effect, so to speak, about bringing the sell-side together with the buy- and make-side. PIM (Product Information Management), which we often see as the ancestor to product MDM, has the same challenge. Here we also need to bring the sell-side and and the buy-side together – on three frontiers:
The Product Data Lake is a cloud service for sharing product data in the eco-systems of manufacturers, distributors, retailers and end users of product information.
As an upstream provider of products data, being a manufacturer or upstream distributor, you have these requirements:
The Product Data Lake facilitates these requirements by letting you push your product data into the lake in your in-house structure that may or may not be fully or partly compliant to an international standard.
As an upstream provider, you may want to push product data and digital assets from several different internal sources.
The product data lake tackles this requirement by letting you operate several upload profiles.
As a downstream receiver of product data, being a downstream distributor, retailer or end user, you have these requirements:
The Product Data Lake facilitates these requirements by letting you pull your product data from the lake in your in-house structure that may or may not be fully or partly compliant to an international standard.
In the Product Data Lake, you can take the role of being an upstream provider and a downstream receiver at the same time by being a midstream subscriber to the Product Data Lake. Thus, Product Data Lake covers the whole supply chain from manufacturing to retail and even the requirements of B2B (Business-to-Business) end users.
The Product Data Lake uses the data lake concept for big data by letting the transformation and linking of data between many structures be done when data are to be consumed for the first time. The goal is that the workload in this system has the resemblance of an iceberg where 10% of the ice is over water and 90 % is under water. In the Product Data Lake manually setting up the links and transformation rules should be 10 % of the duty and the rest being 90 % of the duty will be automated in the exchange zones between trading partners.
When following the articles, blog posts and other inspirational stuff in the data management realm you frequently stumble upon sayings about a unique angle towards what it is all about, like:
The trick is to find a balance between a priceworthy people focused approach, a heartfelt process way of going forward and a solid methodology to exploit technology in the good cause of better data management all aligned with achieving business benefits.
How hard can it be?
This week I had the pleasure of speaking in Copenhagen at an event about The Evolution of MDM. The best speaking experiences is when there are questions and responses from the attendees. At this event, such lovely interuptions took us around some of the tough questions about Master Data Management (MDM), like
Single source of truth
Many seasoned MDM practitioners has experienced attempts to implement a single source of truth for a given MDM domain within a given organization and seen the attempt failed miserably. The obstacles are plentiful including business units with different goals and IT landscapes with heterogenic capabilities.
I think there is a common sentiment in the data management realm about to lower that bar a bit. Perhaps a single place of trust is a more realistic goal as examined in the post Three Stages of MDM Maturity.
MDM in IT
We all know that MDM should belong to the business part of the organization and anchoring MDM (and BI and CRM and so many other disciplines) in the IT part of the organization is a misunderstanding. However, we often see that MDM is placed in the IT department because IT already spans the needs of marketing, sales, logistics, finance and so on.
My take is that the actual vision, goals and holistic business involvement trumps the formal organizational anchoring. Currently I work with two MDM programmes, one anchored in IT and one in finance. As an MDM practitioner, you have to deal with business and IT anyway.
Blockchain
Blockchain is a new technology disrupting business these days. Recently Andrew White of Gartner blogged about how blockchain thinking could go where traditional single view of master data approaches haven’t been able to go. The blog post is called Why not Blockchain Data Synchronization? As Andrew states: “The next year could be very interesting, and very disrupted.”
PS: My slides from the event are available here: MDM before, now and in the future.
Every organization needs Master Data Management (MDM). But does every organization need a MDM tool?
In many ways the MDM tools we see on the market resembles common database tools. But there are some things the MDM tools do better than a common database management tool. The post called The Database versus the Hub outlines three such features being:
Controlling hierarchical completeness and achieving a single business partner view is closely related to the two things data quality tools do better than common database systems as explained in the post Data Quality Tools Revealed. These two features are:
Specialized data profiling tools are very good at providing out-of-the-box functionality for statistical summaries and frequency distributions for the unique values and formats found within the fields of your data sources in order to measure data quality and find critical areas that may harm your business. These capabilities are often better and easier to use than what you find inside a MDM tool. However, in order to measure the improvement in a business context and fix the problems not just in a one-off you need a solid MDM environment.
When it comes to data matching we also still see specialized solutions that are more effective and easier to use than what is typically delivered inside MDM solutions. Besides that, we also see business scenarios where it is better to do the data matching outside the MDM platform as examined in the post The Place for Data Matching in and around MDM.
Looking at the single MDM domains we also see alternatives. Customer Relation Management (CRM) systems are popular as a choice for managing customer master data. But as explained in the post CRM systems and Customer MDM: CRM systems are said to deliver a Single Customer View but usually they don’t. The way CRM systems are built, used and integrated is a certain track to create duplicates. Some remedies for that are touched in the post The Good, Better and Best Way of Avoiding Duplicates.
With product master data we also have Product Information Management (PIM) solutions. From what I have seen PIM solutions has one key capability that is essentially different from a common database solution and how many MDM solutions, that are built with party master data in mind, has. That is a flexible and super user angled way of building hierarchies and assigning attributes to entities – in this case particularly products. If you offer customer self-service, like in eCommerce, with products that have varying attributes you need PIM functionality. If you want to do this smart, you need a collaboration environment for supplier self-service as well as pondered in the post Chinese Whispers and Data Quality.
All in all the necessary components and combinations for a suitable MDM toolbox are plentiful and can be obtained by one-stop-shopping or by putting some best-of-breed solutions together.
Now is the time of the year where you can try predicting what will happen in the next year within a certain field of interest to you.
When we talk about predictions within data management, we usually mean something based on analysing historical data with emphasis on seeing some recent trends.
My precognitions for the Master Data Management (MDM) market I have to admit is of the more traditional kind. Gut feelings. Qualified guessing if you like.
So, here are three foreseeings:
Liliendahl on Data Quality 