Multi-Domain MDM and PIM, Party and Product

Multi-Domain Master Data Management (MDM) and Product Information Management (PIM) are two interrelated disciplines within information management.

While we may see Product Information Management as the ancestor or sister to Product Master Data Management, we will in my eyes gain much more from Product Information Management if we treat this discipline in conjunction with Multi-Domain Master Data Management.

Party and product are the most common handled domains in MDM. I see their intersections as shown in the figure below:

Multi-Side MDM

Your company is not an island. You are part of a business ecosystem, where you may be:

  • Upstream as the maker of goods and services. For that you need to buy raw materials and indirect goods from the parties being your vendors. In a data driven world you also to need to receive product information for these items. You need to sell your finished products to the midstream and downstream parties being your B2B customers. For that you need to provide product information to those parties.
  • Midstream as a distributor (wholesaler) of products. You need to receive product information from upstream parties being your vendors, perhaps enrich and adapt the product information and provide this information to the parties being your downstream B2B customers.
  • Downstream as a retailer or large end user of product information. You need to receive product information from upstream parties being your vendors and enrich and adapt the product information so you will be the preferred seller to the parties being your B2B customers and/or B2C customers.

Knowledge about who the parties being your vendors and/or customers are and how they see product information, is essential to how you must handle product information.  How you handle product information is essential to your trading partners.

You can apply party and product interaction for business ecosystems as explained in the post Party and Product: The Core Entities in Most Data Models.

Infonomics and Second Party Data

The term infonomics does not yet run unmarked through my English spellchecker, but there are some information available on Wikipedia about infonomics. Infonomics is closely related to the often-mentioned phrases in data management about seeing data / information as an asset.

Much of what I have read about infonomics and seeing data / information as an asset is related to what we call first party data. That is data that is stored and managed within your own company.

Some information is also available in relation to third party data. That is data we buy from external parties in order to validate, enrich or even replace our own first party data. An example is a recent paper from among others infonomic guru Doug Laney of Gartner (the analyst firm). This paper has a high value if you want to buy it as seen here.

Anyway, the relationship between data as an asset and the value of data is obvious when it comes to third party data, as we pay a given amount of money for data when acquiring third party data.

Second party data is data we exchange with our trading and other business partners. One example that has been close to me during the recent years is product information that follows exchange of goods in cross company supply chains. Here the value of the goods is increasingly depending on the quality (completeness and other data quality dimensions) of the product information that follows the goods.

In my eyes, we will see an increasing focus on infonomics when it comes to exchanging goods – and the related second party data – in the future. Two basic factors will be:

pdl-top-narrow

PIM Supplier Portals: Are They Good or Bad?

A recent discussion on the LinkedIn Multi-Domain MDM group is about vendor / supplier portals as a part of Product Information Management implementations.

A supplier portal (or vendor portal if you like) is usually an extension to a Product Information Management (PIM) solution. The idea is that the suppliers of products, and thus providers of product information, to you as a downstream participant (distributor or retailer) in a supply chain, can upload their product information into your PIM solution and thus relieving you of doing that. This process usually replace the work of receiving spreadsheets from suppliers in the many situations where data pools are not relevant.

In my opinion and experience, this is a flawed concept, because it is hostile to the supplier. The supplier will have hundreds of downstream receivers of products and thus product information. If all of them introduced their own supplier portal, they will have to learn and maintain hundreds of them. Only if you are bigger than your supplier is and is a substantial part of their business, they will go with you.

Broken data supply chainAnother concept, which is the opposite, is also emerging. This is manufacturers and upstream distributors establishing PIM customer portals, where suppliers can fetch product information. This concept is in my eyes flawed exactly the opposite way.

And then let us imagine that every provider of product information had their PIM customer portal and every receiver had their PIM supplier portal. Then no data would flow at all.

What is your opinion and experience?

Data Born Companies and the Rest of Us

harriThis post is a new feature here on this blog, being guest blogging by data management professionals from all over the world. First up is Harri Juntunen, Partner at Twinspark Consulting in Finland:

Data and clever use of data in business has had and will have significant impact on value creation in the next decade. That is beyond reasonable doubt. What is less clear is, how this is going to happen? Before we answer the question, I think it is meaningful to make a conceptual distinction between data born companies and the rest of us.

Data born born companies are companies that were conceived from data. Their business models are based  on monetising clever use of data. They have organised everything from their customer service to operations to be capable of maximally harness data. Data and capabilities to use data to create value is their core competency. These companies are the giants of data business: Google, Facebook, Amazon, Über, AirBnB. The standard small talk topics in data professionals’ discussions.

However, most of the companies are not data born. Most of the companies were originally established to serve a different purpose. They were founded to serve some physical needs and actually maintaining them physically, be it food, spare parts or factories. Obviously, all of these companies in  e.g. manufacturing and maintenance of physical things need data to operate. Yet, these companies are not organised around the principles of data born companies and capabilities to harness data as the driving force of their businesses.

We hear a lot of stories and successful examples about how data born companies apply augmented intelligence and other latest technology achievements. Surely, technologies build around of data are important. The key question to me is: what, in practice, is our capability to harness all of these opportunities in companies that are not data born?

In my daily practice I see excels floating around and between companies. A lot of manual work caused by unstandardised data, poor governance and bad data quality. Manual data work simply prevents companies to harness the capabilities created by data born companies. Yet, most of the companies follow the data born track without sufficient reflection. They adopt the latest technologies used by the data born companies. They rephrase same slogans: automation, advanced analytics, cognitive computing etc. And yet, they are not addressing the fundamental and mundane issues in their own capabilities to be able to make business and create value with data. Humans are doing machine’s job.

Why? Many things relate to this, but data quality and standardization are still pressing problems in every day practice in many companies. Let alone between companies. We can change this. The rest of us can reborn from data just by taking a good look of our mundane data practices instead of aspiring to go for the next big thing.

P.S. The Google Brain team had reddit a while ago and they were asked “what do you think is underrated?

The answer:

“Focus on getting high-quality data. “Quality” can translate to many things, e.g. thoughtfully chosen variables or reducing noise in measurements. Simple algorithms using higher-quality data will generally outperform the latest and greatest algorithms using lower-quality data.”

https://www.reddit.com/r/MachineLearning/comments/4w6tsv/ama_we_are_the_google_brain_team_wed_love_to/

About Harri Juntunen:

Harri is seasoned data provocateur and ardent advocate of getting the basics right. Harri says: People and data first, technology will follow.

You can contact Harri here:

+358 50 306 9296

harri.juntunen@twinspark.fi

www.twinspark.fi

 

Everyday Digital Transformation

Ben Rund of Informatica has a Youtube video running these days with the title/question: Enough Heard on Digital Transformation by Uber & AirBnB?

I share this sentiment with Ben. You don’t have to disrupt the whole world to take part in digital transformation and you don’t have to start something completely new. As an established enterprise you can transform your current business and combine the good things from the past with the new opportunities aroused from the digital evolution.

Forrester, the other analyst firm, some years ago devided digital transformation into a loop of:

  • Digital Customer Experience
  • Digital Operational Excellence

The below figure visualizes this landscape:

digital

What I would like to elaborate on related to this picture is the business ecosystem of your enterprise, which must be included in the everyday digital transformation.

Let’s take the example of product information management:

However, connect is better than collect. If you are dependent on receiving spreadsheets with product information from your trading partners or you let them put their spreadsheets into your supplier product data portal, you have an everyday digital transformation in front of you.

The solution for that is Product Data Lake.

digital2

A System of Engagement for Business Ecosystems

Master Data Management (MDM) is increasingly being about supporting systems of engagement in addition to the traditional role of supporting systems of record. This topic was first examined on this blog back in 2012 in the post called Social MDM and Systems of Engagement.

The best known systems of engagement are social networks where the leaders are Facebook for engagement with persons in the private sphere and LinkedIn for engagement with people working in or for one or several companies.

But what about engagement between companies? Though you can argue that all (soft) engagement is neither business-to-consumer (B2C) nor business-to-business (B2B) but human-to-human (H2H), there are some hard engagement going on between companies.

pdl-whyOne of the most important ones is exchange of product information between manufacturers, distributors, resellers and large end users of product information. And that is not going very well today. Either it is based on fluffy emailing of spreadsheets or using rigid data pools and portals. So there are definitely room for improvement here.

At Product Data Lake we have introduced a system of engagement for companies when it comes to the crucial task of exchanging product information between trading partners. Read more about that in the post What a PIM-2-PIM Solution Looks Like.