20 years ago, when I started working as a contractor and entrepreneur in the data management space, data was not on the top agenda at many enterprises. Fortunately, that has changed.
An example is displayed by Schneider Electric CEO Jean-Pascal Tricoire in his recent blog post on how digitization and data can enable companies to be more sustainable. You can read it on the Schneider Electric Blog in the post 3 Myths About Sustainability and Business.
Manufacturers in the building material sector naturally emphasizes on sustainability. In his post Jean-Pascal Tricoire says: “The digital revolution helps answering several of the major sustainability challenges, dispelling some of the lingering myths regarding sustainability and business growth”.
One of three myths dispelled is: Sustainability data is still too costly and time-consuming to manage.
From my work with Master Data Management (MDM) and Product Information Management (PIM) at manufacturers and merchants in the building material sector I know that managing the basic product data, trading data and customer self-service ready product data is hard enough. Taking on sustainability data will only make that harder. So, we need to be smarter in our product data management. Smart and sustainable homes and smart sustainable cities need smart product data management.
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.
Enterprises are increasingly going to be part of business ecosystems where collaboration between legal entities not belonging to the same company family tree will be the norm.
This trend is driven by digital transformation as no enterprise possibly can master all the disciplines needed in applying a digital platform to traditional ways of doing business.
Enterprises are basically selfish. This is also true when it comes to Master Data Management (MDM). Most master data initiatives today revolve around aligning internal silos of master data and surrounding processes to fit he business objectives within an enterprise as a whole. And that is hard enough.
However, in the future that is not enough. You must also be able share master data in the business ecosystems where your enterprise will belong. The enterprises that, in a broad sense, gets this first will survive. Those who will be laggards are in danger of being left out of business.
In multidomain Master Data Management (MDM) we often focus on the two most frequently addressed domains being Customer MDM and Product MDM.
However, managing the critical data elements that describes the vendors to an enterprise is increasingly being on the agenda in MDM implementations.
Handling vendor master data shares a good deal of the same challenges as with customer master data, as we are describing real world entities that have a role as a second party to our enterprise. In more cases than what often is acknowledged, vendors may also have a role as a customer or other business partner roles at the same time. In my eyes, we should handle vendor master data as a subset of party master data as described in the post about How Bosch is Aiming for Unified Partner Master Data Management.
The self-service theme has also emerged in handling vendor master data as self-service based supplier portals have become common as the place where vendor master data is captured and maintained. Where making the first purchase order or receiving the first invoice was the starting point for vendor master data in the old days, this is often not the case anymore.
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 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.
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.
The Information Difference MDM Landscape Q2 2018 is out.
The report confirms the trend of increasing uptake of cloud Master Data Management solutions as examined in the recent post called The Rise of Cloud MDM.
According to the report the coexistence of big data and master data is another trend and more and more MDM vendors are embracing all master data domains while though as stated “most vendors have their roots in either customer or product data, and their particular functionality and track record of deployment is usually deeper where the software had its roots”.
The plot of vendors looks like this:You can read the full report here.
Cloud as a deployment method for Master Data Management (MDM) solutions is on the rise.
In the latest MDM vendor selection activities I am involved in cloud is not an absolute must but certainly the preferred deployment method.
The MDM vendor market is responding to that trend. Some of the new players offers purely cloud based solutions. In a recent post on this blog I wrote about Three Remarkable Observations about Reltio. The fourth will be that this is a cloud-based MDM (and more) solution – called Reltio Cloud.
Another example of going the cloud path is Riversand. Their new release is put forward as a cloud-native suite of Master Data Management solutions as told in an interview by Katie Fabiszak with CEO & Founder Upen Varanasi of Riversand. The interview is posted as a guest blog post on The Disruptive MDM List. The post is called Cloud multi-domain MDM as the foundation for Digital Transformation.