The intersection between Artificial Intelligence (AI) and Master Data Management (MDM) – and the associated discipline Product Information Management (PIM) – is an emerging topic.
A use case close to me
In my work at setting up a service called Product Data Lake the inclusion of AI has become an important topic. The aim of this service is to translate between the different taxonomies in use at trading partners for example when a manufacturer shares his product information with a merchant.
In some cases the manufacturer, the provider of product information, may use the same standard for product information as the merchant. This may be deep standards as eCl@ss and ETIM or pure product classification standards as UNSPSC. In this case we can apply deterministic matching of the classifications and the attributes (also called properties or features).
However, most often there are uncovered areas even when two trading partners share the same standard. And then again, the most frequent situation is that the two trading partners are using different standards.
As always, applying too much human interaction is costly, time consuming and error prone. Therefore, we are very eagerly training our machines to be able to do this work in a cost-effective way, within a much shorter time frame and with a repeatable and consistent outcome to the benefit of the participating manufacturers, merchants and other enterprises involved in exchanging products and the related product information.
Learning from others
This week I participated in a workshop around exchanging experiences and proofing use cases for AI and MDM. The above-mentioned use case was one of several use cases examined here. And for sure, there is a basis for applying AI with substantial benefits for the enterprises who gets this. The workshop was arranged by Camelot Management Consultants within their Global Community for Artificial Intelligence in MDM.
There is a tendency when deploying Product Information Management (PIM) solutions, that you may want to add a portal for your trading partners:
If you are a manufacturer, you could have a customer portal where your downstream re-sellers can fetch the nicely arranged product information that is the result of your PIM implementation.
If you are a merchant, you could have a supplier portal where your upstream suppliers can deliver their information nicely arranged according to your product information standards in your PIM implementation.
This is a death trap for both manufacturers and merchants, because:
As a trading manufacturer and merchant, you probably follow different standards, so one must obey to the other. The result is that one side will have a lot of manual and costly work to do to obey the strongest trading partner. Only a few will be the strongest all time.
If all manufacturers have a customer portal and all merchants have a supplier portal everyone will be waiting for the other and no product information will flow in the supply chains.
In here Frank has this question: Do ecosystems represent an opportunity to establish non-traditional revenue streams (e.g. monetizing data)?
I think so. One example very close to me is how merchants, shippers and manufacturers can work closely together in not only moving the goods between them in an efficient way, but also moving the product information between them in the most efficient way.
There are three kinds of data monetization: Selling data, wrapping data around products and utilizing advanced analytics leading to fast operational decision making. These options were examined in the post Three Flavors of Data Monetization.
If we look at the middle option, wrapping data around products, and narrow it down to wrapping data around tangible products, there are some ways to execute that for supply change delegates, not at least if the participating business entities embraces the business ecosystem where goods are moved through:
Manufacturers need to streamline the handling of product information internally. This includes disciplines as PLM (Product Lifecycle Management) and PIM (Product Information Management). On top of that, manufacturers need to be effective in the way the product information is forwarded to direct customers and distributors/wholesalers and merchants as exemplified in the post How Manufacturers of Building Materials Can Improve Product Information Efficiency.
Merchants need to utilize the best way of getting data into inhouse PIM (Product Information Management) solutions or other kind of solutions where data flows in from trading partners. Many merchants have a huge variety in product information needs as told in the post Work Clothes versus Fashion: A Product Information Perspective. On top of that a merchant will have supplying manufacturers and distributors with varying formats and capabilities to offer product information as discussed in the post PIM Supplier Portals: Are They Good or Bad?.
Work clothes and clothes for private (and white collar) use are as products quite similar. You have the same product groups as shoes, trousers, belts, shirts, jackets, hats and so on.
However, the sales channels have different structures and the product information needed in sales, not at least self-service sales as in ecommerce, are as Venus and Mars.
Online fashion sales are driven by nice images – nice clothes on nice models. The information communicated is often fluffy with only sparse hard facts on data like fabrics, composition, certificates, origin. Many sales channel nodes only deal with fashion.
Selling work clothes, including doing it on the emerging online channels, does include images. But they should be strict to presenting the product as is. There is a huge demand for complete and stringent product information.
Work clothes are often sold in conjunction with very different products as for example building materials, where the requirements for product information attributes are not the same. Work clothes comes, as fashion, in variants in sizes and colors. This is not so often used, or used quite differently, when selling for example building materials.
At Product Data Lake we offer a product information sharing environments for manufacturers of work clothes and their merchants who may have a lot of other products in range with different product information requirements. We call it Product Data Syndication Freedom.
Building materials is a very diverse product group. Even within a manufacturing enterprise there may be considerable variances in what kind of product information you need for different product groups. If production is taking place on plants around the world, then local demands and cultural differences is another source of diversity in how product information is handled.
In many cases building materials are not sold directly to end users, but are forwarded in the supply chain to re-sellers being distributors/wholesalers, merchants/dealers and marketplaces. These trading partners each have their range of products and specific requirements for product information which makes it very hard for the manufacturer to prepare product information that fits all.
The IT enabled discipline aimed at solving such challenges is called product data syndication. There are namely these three kinds of product data syndication relevant to manufacturers:
Enterprise wide product data syndication aiming at linking, transforming and consolidating product information created by various business units and production sites around the world. The goal is to have consistent, accurate and timely information ending up in one place, often being an in-house Product Information Management (PIM) or Master Data Management (MDM) solution.
Ecosystem wide product data syndication push aiming at providing product information to re-sellers in a uniform way. On the other hand, it should be possible for the diverse crowd of re-sellers to pull that information adhering to each one’s requirements for format, completeness and conformity at a certain time.
Ecosystem wide product data syndication pull also in many cases applies to a manufacturer. It is not unusual that a manufacturer complements the own produced product range with special products supplied from other manufacturers, where product information must be provided by those. In addition to that manufacturers buys raw materials, spare parts for machinery and other products where product information is needed when the surrounding processes should be automated.
At Product Data Lake, we offer a solution to these challenges. We emphasize on these capabilities:
Product Data Quality aiming at improvements of completeness of product data, as well as the accuracy, timeliness, consistency and conformity of the product information shared with trading partners and end users.
Product Data Syndication Freedom, as the solution is suited for consolidating enterprise wide diversities and pushing information to trading partners in a uniform way while making it possible for trading partners to pull the product information in their many ways.
Learn more about the solution and the benefits for manufacturers of building materials on the Product Data Push site.
One of the news this week was that Maersk for the first time is taking a large container ship from East Asia to Europe using a Northern Route through the Arctic waters as told in this Financial Times article.
The purpose of this trip is to explore the possibility of avoiding the longer Southern Route including shoehorning the sea traffic through the narrow Suez Canal. A similar opportunity exists around North America as an alternative to going through The Panama Canal.
Similar to moving products and finding new routes for that we may also explore new routes when it comes to moving information about products. Until now the possibilities, besides cumbersome exchange of spreadsheets, have been to shoehorn product information from the manufacturer into a consensus-based data portal or data pool from where the merchant can fetch the information in accurate the same shape as his competitors does.
Gartner, the analyst firm, has a hype cycle for Information Governance and Master Data Management.
Back in 2012 there was a hype cycle for just Master Data Management. It looked like this:
I have made a red circle around the two rightmost terms: “Data Quality Tools” and “Information Exchange and Global Data Synchronization”.
Now, 6 years later, the terms included in the cycle are the below:
The two terms “Data Quality Tools” and “Information Exchange and Global Data Synchronization” are not mentioned here. I do not think it is because the they ever fulfilled their purpose. I think they are being supplemented by something new. One of these terms that have emerged since 2012 is, in red circle, Multienterprise MDM.
As touched in the post Product Data Quality we have seen data quality tools in action for years when it comes to customer (or party) master data, but not that much when it comes to product master data.
Global Data Synchronization has been around the GS1 concept of GDSN (Global Data Synchronization Network) and exchange of product data between trading partners. However, after 40 years in play this concept only covers a fraction of the products traded worldwide and only for very basic product master data. Product data syndication between trading partners for a lot of product information and related digital assets must still be handled otherwise today.
In my eyes Multienterprise MDM comes to the rescue. This concept was examined in the post Ecosystem Wide MDM. You can gain business benefits from extending enterprise wide product master data management to be multienterprise wide. This includes:
Working with the same product classifications or being able to continuously map between different classifications used by trading partners
Utilizing the same attribute definitions (metadata around products) or being able to continuously map between different attribute taxonomies in use by trading partners
Sharing data on product relationships (available accessories, relevant spare parts, updated succession for products, cross-sell information and up-sell opportunities)
Having shared access to latest versions of digital assets (text, audio, video) associated with products.
This is what we work for at Product Data Lake – including Machine Learning Enabled Data Quality, Data Classification, Cloud MDM Hub Service and Multienterprise Metadata Management.
When working with product data syndication in supply chains the big pain is that data standards in use and the preferred exchange methods differ between supply chain participants.
As a manufacturer you will have hundreds of re-sellers who probably have data standards different from you and most likely wants to exchange data in a different way than you do.
As a merchant you will have hundreds of suppliers who probably have data standards different from you and most likely wants to exchange data in a different way than you do.
The aim of Product Data Lake is to take that pain away from both the manufacturer side and the merchant side. We offer product data syndication freedom by letting you as manufacturer push product information using your data standards and your preferred exchange method and letting you as a merchant pull product information using your data standards and your preferred exchange method.
The concept of doing Master Data Management (MDM) not only enterprise wide but ecosystem wide was examined in the post Ecosystem Wide MDM.
As mentioned, product master data is an obvious domain where business outcomes may occur first when stretching your digital transformation to encompass business ecosystems.
The figure below shows the core delegates in the ecosystem wide Product Information Management (PIM) landscape we support at Product Data Lake:
Your enterprise is in the centre. You may have or need an in-house PIM solution where you manipulate and make product information more competitive as elaborated in the post Using Internal and External Product Information to Win.
At Product Data Lake we collaborate with providers of Artificial Intelligence (AI) capabilities and similar technologies in order to improve data quality and analyse product information.
As shown in the top, there may be a relevant data pool with a consensus structure for your industry available, where you exchange some of product information with trading partners. At Product Data Lake we embrace that scenario with our reservoir concept.
Else, you will need to make partnerships with individual trading partners. At Product Data Lake we make that happen with a win-win approach. This means, that providers can push their product information in a uniform way with the structure and with the taxonomy they have. Receivers can pull the product information in a uniform way with the structure and with the taxonomy they have. This product data syndication concept is outlined in the post Sell more. Reduce costs.