Product Data Management is Like an Ironman

cofToday we have an Ironman passing through the streets of Copenhagen (and my breakfast). Kudos to the women and men who first have been on a swim lane of 3.86 km (2.4 miles), now is cycling 180.25 km (112 miles) and then will run a full Marathon of 42.2 km (26.22 miles).

Thinking about it doing product data management is a bit like an Ironman too. Overall it is a daunting task. And we have three disciplines to cover:

  • Digital Asset Management (DAM) is an activity where many organizations start. It is about handling product images in various sizes and versions along the way, as well as, depending on the product category, installation guides, line drawings, data sheets and other documents. Also videos with that and other content is becoming popular.
  • Product Information Management (PIM) is about maintaining hundreds (sometimes thousands) of different attributes describing a product. Some of these attributes are common for most products (like height, weight and colour) and some are very specific for a given product category.
  • Master Data Management (MDM) is a Marathon in itself. Here you link the above product data with product data in the overall system landscape including ERP, SCM (Supply Chain Management) and PLM (Product Lifecycle Management). Product data also forms the product domain that must be aligned with the location domain, asset domain, party domain and perhaps other domains in your MDM world.

How these disciplines stick together within your organization and your digital ecosystem was further examined in the post How MDM, PIM and DAM Stick Together.

The Link Between Privacy and Product Data

Do we as a consumer need to be told what to buy? Or do we rather want to be told what we are buying?

This theme was examined in a previous post titled You Must Supplement Customer Insight with Rich Product Data.

Not at least on the European scene with the upcoming General Data Protection Regulation (GDPR) there are limits to how long you can go in profiling your (prospective) costumers. And I am sure those people will value more you are telling them the complete story about your products, rather than guessing what products (from your range) they might need.

As a consumer, we want the facts about the products to make a self-service purchase. We want to be able to search for and navigate precisely to a product suitable for a specific use. We want the facts in a way, so we can compare, perhaps using a comparison service, between different brands and lines. We want to know what accessories goes with what product. We want to know what spare parts goes with what product.

By the way: Business buyers want all that too. And a person being a business buyer is a person (data subject) in the eyes of GDPR too.

For providing complete and consistent product data you as a (re)seller need to maintain high quality product data and if your product portfolio is just above very very simple, you need a Product Information Management (PIM) solution and, if you have trading partners, you need a PIM-2-PIM solution to exchange product information with your trading partners.

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When You Know that Statement is Wrong

1271Oftentimes it still takes a human eye to establish if a number, year, term or other piece of information is wrong.

I had that experience today at Harvard Square in Cambridge (Boston) when looking at the sign in front of our lunch restaurant. Established 1271 it says. Hmmmm. North American natives were not known for establishing restaurants. Also, the Vikings did not stay that long or went that south in North America.

The restaurant website actually admits the sign is wrong and this is a printing flaw (should have been 1971) that they have chosen to keep – maybe also in order to test the clever people hanging around Harvard.

Anyway, without attempting to turn this into a foodie blog, the food is OK but the waiting time for being served does resemble spans of centuries.

You Must Supplement Customer Insight with Rich Product Data

school_420x310This week I attended an event called Retail Summer School at Columbia Business School in New York.

Much of the talking was about how to get insights on your (prospective) customers by collecting data in all kinds of ways – while observing the thin line between cool and creepy.

My thinking, of course biased by my current Product Data Lake venture, is that you should also pay attention to product data. For at least two reasons:

Algorithm effectiveness: Your algorithms on what products to present based on your rich insight into your customers need will only work if you are able to automatically match the needs against very specific product attributes. And most retailers don not have that today if you look at product descriptions on their ecommerce sites.

Also, I am not impressed by the suggestions I get today. They generally fall into two buckets:

  • Things I absolutely do not need
  • Things I just bought

Self-service craving: As a customer, we strike back. We do not need to be told what to buy. But we do want to know what we are buying. This means we want to be able to see rich product information. Therefore retailers must maintain a lot of product data and related digital assets that they should fetch at a trusted source: From the manufactures. And if the manufacturer wants their products to be the ones selected by the end customers, they must be able to deliver these data seamlessly to all their distributors, retailers and marketplaces.

Party Master Data and the Data Subject

Within the upcoming EU General Data Protection Regulation (GDPR) the term data subject is used for the persons for whom we must protect the privacy.

These are the persons we handle as entities within party Master Data Management (MDM).

In the figure below the blue area covers the entity types and roles that are data subjects in the eyes of GDPR

Data Subjects

While GDPR is of very high importance in business-to-consumer (B2C) and government-to-citizen (G2C) activities, GDPR is also of importance for business-to-business (B2B) and government-to-business (G2B) activities.

GDPR does not cover unborn persons which may be a fact of interest in very few industries as for example healthcare. When it comes to minors, there are special considerations within GDPR to be aware of. GDPR does not apply to deceased persons. In some industries like financial services and utility, the handling of the estate after the death of a person is essential, as well as knowing about that sad event is of importance in general as touched in the post External Events, MDM and Data Stewardship.

One tough master data challenge in the light of GDPR will be to know the status of your registered party master data entities. This also means knowing when it is a private individual, a contact at an organization or an organization or department hereof as such. From my data matching days, I know that heaps of databases do not hold that clarity as reported in the post So, how about SOHO homes.

Your General Data Protection Roadmap

Being ready for the EU GDPR (European Union – General Data Protection Regulation) is – or should be – a topic on the agenda for European businesses and international businesses operating with an European reach.

The finish date is fixed: 25th May 2018. What GDPR is about is well covered (perhaps too overwhelmingly) on the internet. But how do you get there?

Below is my template for a roadmap:

GDPR Readiness RoadmapThe roadmap has as all programs should have an as-is phase, here in concrete as a Privacy Impact Assessment covering what should have been done, if the regulation was already in force. Then comes the phase stating the needed to-be state with the action plan that fills the gaps while absorbing business benefits as well. And then implementation of the prioritized tasks.

GDPR is not only about IT systems, but to be honest, for most companies it will mostly be. Your IT landscape determines which applications will be involved. Most companies will have sales and marketing applications holding personal data. Human Resource Management is a given too. Depending on your business model there will be others. Remember, this is about all kind of personal data – that includes for example supplier contact data that identifies a person too.

The skills needed spans from legal, (Master) Data Management and IT security. You may have these skills internally or you may need interim resources of the above-mentioned kind in order to meet the fixed finish date and being sure things are done right.

By the way: My well skilled associates and I are ready to help. Get in contact:

Varying Views on the MDM Market 2017

The Information Difference MDM Landscape Q2 2017 is out.

In the survey behind the vendor with the happiest customers was Agility Multichannel, followed closely by EnterWorks, then Stibo Systems, then Orchestra Networks and Informatica.

If you look at the positioning below these are by the way the ones with highest score on the technology axis (vertical) – but are not rated in the same order on the market strength axis (horizontal).

MDM Landscape Q2 2017
Source: Information Difference

The pack of vendors is organized by Information Difference only somewhat in line with Gartner as seen in the post Who will become Future Leaders in the Gartner Multidomain MDM Magic Quadrant?

Riversand and Tibco are not positioned by Information Difference, nor is Magnitude Software, which is the new wrap of Kalido, that had Andy Hayler of Information Difference as a founder.

Gartner did not position Agility Multichannel, Viamedici, Profisee, Terradata, Veeva and Talend in their quadrant.

All in all we see a market with a lot of unsettled business also considering exciting newer players as Reltio, Semarchy and Uniserv.