Information Quality

B2C vs B2B in Product Information Management

Henrik Gabs Liliendahl4 Comments

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

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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

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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:

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Work Clothes versus Fashion: A Product Information Perspective

Henrik Gabs LiliendahlLeave a comment

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

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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 in 2012.

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

Data Monetization and Data Quality

Henrik Gabs LiliendahlLeave a comment

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

Henrik Gabs Liliendahl4 Comments

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?

Product Data Completeness

Henrik Gabs LiliendahlLeave a comment

Completeness is one of the most frequently mentioned data quality dimensions as touched in the post How to Improve Completeness of Data.

ChecklistWhile every data quality dimension applies to all domains of Master Data Management (MDM), some different dimensions apply a bit more to one of the domains or the intersections of the domains as explained in the post Multi-Domain MDM and Data Quality Dimensions.

With product master data (or product information if you like) completeness is often a big pain. One reason is that completeness means different requirements for different categories of products as pondered in the post Hierarchical Completeness within Product Information Management.

At Product Data Lake we develop a range of cloud service offerings that will help you improve completeness of product data. These are namely:

  • Measuring completeness against these industry standards that have attribute requirements such as eClass and ETIM
  • For manufacturers measuring completeness against downstream trading partner requirements (if not fully governed by an industry standard).
  • For merchants measuring incoming completeness when pulling from merchants.
  • Measuring against completeness required by marketplaces.
  • Transforming product information to meet conformity and thereby ability to populate according to requirements
  • Translating product information in order to populate attributes in more languages
  • Transferring product information by letting manufacturers push it in their way and letting merchants pull it their way as described in the post Using Pull or Push to Get to the Next Level in Product Information Management.

How to Improve Completeness of Data

Henrik Gabs Liliendahl2 Comments

Completeness is one of the most frequently mentioned data quality dimensions. The different data quality dimensions (as completeness, timeliness, consistency, conformity, accuracy and uniqueness) sticks together, and not at least completeness is an aim in itself as well as something that helps improving the other data quality dimensions.

“You can’t control what you can’t measure” is a famous saying. That also applies to data quality dimensions. As pondered in the post Hierarchical Completeness, measuring completeness is usually not something you can apply on the data model level, but something you need to drill down in hierarchies and other segmentation of data.

Party Master Data

A common example is a form where you have to fill a name and address. You may have a field called state/province. The problem is that for some countries (like USA, Canada, Australia and India) this field should be mandatory (and conform to a value list), but for most other countries it does not make sense. If you keep the field mandatory for everyone, you will not get data quality but rubbish instead.

Multi-Domain MDM and Data Quality DimensionsCustomer and other party master data have plenty of other completeness challenges. In my experience the best approach to control completeness is involving third party reference data wherever possible and as early in the data capture as feasible. There is no reason to type something  probably in a wrong and incomplete way, if it is already digitally available in a righter and more complete way.

Product Master Data

With product master data the variations are even more challenging than with party master data. Which product information attributes that is needed for a product varies across different types of products.

There is some help available in some of the product information standards available as told in the post Five Product Classification Standards. A few of these standards actually sets requirements for which attributes (also called features and properties) that are needed for a product of certain classification within that standard. The problem is then that not everyone uses the same standard (to say in the same version) at the same time. But it is a good starting point.

Product data flows between trading partners. In my experience the key to getting more complete product data within the whole supply chain is to improve the flow of product data between trading partners supported by those who delivers solutions and services for Product Information Management (PIM).

Making that happen is the vision and mission for Product Data Lake.

5 Vital Product Data Quality Dimensions

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Data quality when it comes to product master data has traditionally been lesser addressed than data quality related to customer – or rather party – master data.

However, organizations are increasingly addressing the quality of product master data in the light of digitalization efforts, as high quality product information is a key enabler for improved customer experience not at least in self-service scenarios.

We can though still use most of the known data quality dimensions from the party master data management realm, but with the below mentioned nuances of data quality management for product information.

Completeness of product information is essential for self-service sales approaches. A study revealed that 81 % of e-shoppers would leave a web-shop with incomplete product information. The root cause of lacking product information is often a not working cross company data supply chain as reported in the post The Cure against Dysfunctional Product Data Sharing.

Timeliness, or currency if you like, of product information is again a challenge often related to issues in cross company supply chains. You can learn more about this subject in the post How to avoid Stale Product Data.

Conformity of product information is first and foremost achieved by adhering to a public standard for product information. However, there are different international, national and industry standards to choose from. These standards also comes in versions that changes over time. Also your variety of product groups may be best served by different standards. Learn more about Five Product Classification Standards here.

Consistency of product information has to be solved in two scopes. First consistency has to be solved internally within your organisation by consolidating diverse silos of product master data. This is often done using a Product Information Management (PIM) solution. Secondly you have to share your consistent product information with your flock of trading partners as explained in the post What a PIM-2-PIM Solution Looks Like.

Accuracy is usually best at the root, meaning where the product is manufactured. Then accuracy may be challenged when passed along in the cross company supply chain as examined in the post Chinese Whispers and Data Quality. Again, the remedy is about creating transparency in business ecosystems by using a modern data management approach as proposed in the post Data Lakes in Business Ecosystems.

Product DQ Dimension