Governing Product Information

The title of this blog post is also the title of a presentation I will do at the 2019 Data Governance and Information Quality Conference in San Diego, US in June.

There is a little difference between how we can exercise data governance and information quality management when we are handling data about products versus handling the most common data domain being party data (customer, vendor/supplier, employee and other roles).

Multi-Domain MDM and Data Quality DimensionsThis topic was touched here on the blog in the post called Data Quality for the Product Domain vs the Party Domain.

The conference session will go through these topics:

  • Product master data vs. product information
  • How Master Data Management (MDM), Product Information Management (PIM) and Digital Asset Management (DAM) stick together
  • The roles of 1st party data, 2nd party data and 3rd party data in MDM, PIM and DAM
  • Business ecosystem wide product data management
  • Cross company data governance and information quality alignment

You can have a look at the full agenda for the DGIQ 2019 Conference here.

dgiq 2019

The relation between CX and MDM

The title of this blog post is also the title of a webinar I will be presenting on the 28th February 2019. The webinar is hosted by the visionary Multidomain MDM and PIM solution provider Riversand.

Customer experience (CX) and Master Data Management (MDM) must go hand in hand. Both themes involve multiple business units and digital environments within your enterprise and in the wider business ecosystem, where your enterprise operates. Master data is the glue that brings the data you hold about your customers together as well as the glue that combines the data you share about your product offering together.

To be successful within customer experience in the digital era you need classic master data outcomes as a 360-degree view of customers as well as complete and consistent product information. In other words, you need to maintain Golden Records in Multidomain MDM.

You also need to combine your customer data and your product data to get to the right level of personalization. Knowing about your customer, what he/she wants, and their buying behaviour is one side personalization. The other side is being able to match these data with relevant products that is described to a level that can provide reasonable logic against the behavioural data.

Furthermore, you need to be able to make sense of internal and external big data sources and relate those to your prospective and existing customers and the products they have an interest in. This quest stretches the boundaries of traditional MDM towards being a more generic data platform.

You can register to join the webinar here.

webinar data lake

Get Your Hands Dirty with Data

When working with data management – and not at least listening to and reading stuff about data management – there is in my experience too little work with the actual data going around out there.

I know this from my own work. Most often presentations, studies and other decision support in the data management realm is based on random anecdotes about the data rather than looking at the data. And don’t get me wrong. I know that data must be seen as information in context, that the processes around data is crucial, that the people working with data is key to achieving better data quality and much more cleverness not about the data as is.

data management wordsBut time and again I always realize that you get the best understanding about the data when getting your hands dirty with working with the data from various organizations. For me that have been when doing a deduplication of party master data, when calibrating a data matching engine for party master data against third party reference data, when grouping and linking product information held by trading partners, when relating other master data to location reference data and all these activities we do in order to raise data quality and get a grip on Master Data Management (MDM) and Product Information Management (PIM).

Well, perhaps it is just me and because I never liked real dirt and gardening.

B2C vs B2B in Product Information Management

The difference between doing Business-to-Consumer (B2C) or Business-to-Business (B2B) reflects itself in many IT enabled disciplines.

Yin and yangWhen it comes to Product Information Management (PIM) this is true as well. As PIM has become essential with the rise of eCommerce, some of the differences are inherited from the eCommerce discipline. There is a discussion on this in a post on the Shopify blog by Ross Simmonds. The post is called B2B vs B2C Ecommerce: What’s The Difference?

Some significant observations to go into the PIM realm is that for B2B, compared to B2C:

  • The audience is (on average) narrower
  • The price is (on average) higher
  • The decision process is (on average) more thoughtful

How these circumstances affect the difference for PIM was exemplified here on the blog in the post Work Clothes versus Fashion: A Product Information Perspective.

To sum up the differences I would say that some of the technology you need, for example PIM solutions, is basically the same but the data to go into these solutions must be more elaborate and stringent for B2B. This means that for B2B, compared to B2C, you (on average) need:

  • More complete and more consistent attributes (specifications, features, properties) for each product and these should be more tailored to each product group.
  • More complete and consistent product relations (accessories, replacements, spare parts) for each product.
  • More complete and consistent digital assets (images, line drawings, certificates) for each product.

How to achieve that involves deep collaboration in the supply chains of manufacturers, distributors and merchants. The solutions for that was examined in the post The Long Tail of Product Data Synchronization.

Flying by Ultima Thule and Data Management

Ultima Thule is a name for a distant place beyond the known world and the nickname of the most distant object in the solar system closely observed by a man-made object today the 1st January 2019. Before the flyby scientists were unsure if it was two objects, a peanut formed object or another shape. The images probing what it is will be downloaded during the next couple of months.

You can make many analogies between exploring space and data management. On this blog the journey has passed the similarity between Neutron Star Collision and Data Quality. The Gravitational Waves in the MDM World has been observed and so has the Gravitational Collapse in the PIM Space. The notion of A Product Information Management (PIM) Solar System has also been suggested.

Happy New Year and wishing you all well in the data management journey beyond Ultima Thule.

Ultima Thule
Source: Nasa via BBC

Linked Product Data Quality

Some years ago the theme of Linked Data Quality was examined here on the blog.

As stated in the post a lot of product data is already out there waiting to be found, categorized, matched and linked.

Doing this is at the core of the Product Data Lake venture I am involved with. What we aim to do is linking product information stored using different taxonomies at trading partners, preferable by referencing international and industry standards as eCl@ss, ETIM, UNSPSC, Harmonized System, GPC and more.

Our approach is not to reinvent the wheel, but to collaborate with partners in the industry. This include:

  • Experts within a type of product as building materials and sub-sectors in this industry, machinery, chemicals, automotive, furniture and home-ware, electronics, work clothes, fashion, books and other printed materials, food and beverage, pharmaceuticals and medical devices. You may be a specialist in certain standards for product data. As an ambassador you will link the taxonomy in use at two trading partners or within a larger business ecosystem.
  • Product data cleansing specialists who have proven track records in optimizing product master data and product information. As an ambassador you will prepare the product data portfolio at a trading partner and extend the service to other trading partners or within a larger business ecosystem.
  • System integrators who can integrate product data syndication flows into Product Information Management (PIM) and other solutions at trading partners and consult on the surrounding data quality and data governance issues. As an ambassador, you will enable the digital flow of product information between two trading partners or within a larger business ecosystem.
  • Tool vendors who can offer in-house Product Information Management (PIM) / Master Data Management (MDM) solutions or similar solutions in the ERP and Supply Chain Management (SCM) sphere. As an ambassador you will able to provide, supplement or replace customer data portals at manufacturers and supplier data portals at merchants and thus offer truly automated and interactive product data syndication functionality.
  • Technology providers with data governance solutions, data quality management solutions and Artificial Intelligence (AI) / machine learning capacities for classifying and linking product information to support the activities made by ambassadors and subscribers.
  • Reservoirs, as Product Data Lake is a unique opportunity for service providers with product data portfolios (data pools and data portals) for utilizing modern data management technology and offer a comprehensive way of collecting and distributing product data within the business processes used by subscribers.

See more on the Product Data Link site, on the Product Data Link showcase page on LinkedIn or get in contact right away:

 

Become a Product Data lake ambassador!

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Work Clothes versus Fashion: A Product Information Perspective

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.

Work Clothes versus Fashion

MDM Alternative Facts

When searching for information about Master Data Management (MDM) solutions you will stumble on a lot of alternative facts.

Here are three more or less grave examples:

The MDM news is filled with yet a new market research report at sale for a few thousand US dollars. These reports look at first hand to be very thorough and information rich. But usually with a closer look you will become suspicious. It may be the mention of key players where often some are missing and a few actually mentioned will be companies more known from other trades. And the structure and content, as in the below example, seems to be a copy paste from other trades. Hmmm… “Production”, “Gross Margin” …. Seems to be more about the global cement market.

Market Research MDM.png

The next example is from an article called The 4 Best Master Data Management (MDM) Software Tools to Consider. Oracle, Profisee, Talend and SAP are all viable solutions. But Oracle seems to be going away from this market and the below justification for Oracle is very little about MDM.

Oracle MDM

Finally, on the pedantic side, even the recognized analyst firms can make a mistake (or a copy paste from earlier years). Forrester places Informatica as a German company. Well, it is the Product Information Management (PIM) wave and Informatica got into PIM (now Product 360 MDM) by buying the German PIM vendor Heiler.

Infa as German.pngNope, there is no such thing as a single version of the truth.

Data Monetization and Data Quality

Traditionally data quality management has revolved around making data fit for purpose in various business processes and thus data quality has contributed indirectly to business outcomes, as the business benefits were measured and harvested by results created in these business processes.

This situation has also made it very hard to create distinct business cases for data quality improvement. Most often data quality improvement and related disciplines and data governance, Master Data Management (MDM) and Product Information Management (PIM) has been part of wider business cases concerning for example Customer Relationship Management (CRM) and eCommerce perhaps under an even wider specific business objective.

In today’s data driven business world and drastic rising top-level appetite for digital transformation we see more and more examples of how data can be used much more directly to create business outcome through new or fundamentally reshaped business services and business models.

WebinarsOne example close to me is how data quality via completeness of product information can lead directly to selling more online as told in the post Where to Buy a Magic Wand?

On the 7th August I will elaborate on these themes in a webinar together with Rado Kotorov. The webinar is hosted by Information Builders and you can learn more and register on the Data Monetization webinar here.

 

The Emperor´s New Term

Emperor_Clothes“No one dared to admit that he couldn’t see anything, for who would want it to be known that he was either stupid or unfit for his post?”

This is a quote from the story called The Emperors New Clothes by Hans Christian Andersen.

Having been in and around the IT business for nearly 40 years I have seen, and admittedly not seen, a lot. Inflated hype has always been there, and a lot of technologies, companies and gurus did not make it, but came out naked.

What will you say are the emperor’s new clothes within data management today. Here are some suggestions:

  • Social MDM (Social Master Data Management): The idea that master data management will embrace social profiles and social data streams. If not anything else, did GDPR kill that one?
  • Big Data: This term has been killed so many times. But were those always a staged murder?
  • Single source of truth: The vision that we can have one single source that encompasses everything we need to know about a business entity. This has been a long time running question. Will it ever be answered?

What is your suggestion?