Who is Behind with GDPR?

The answer to the question about who is behind with EU General Data Protection Regulation (GDPR) readiness is in short:

  • A lot of companies
  • A lot of governments

When following the vibe around getting prepared for GDPR, and from my own involvement at clients, there is no doubt about that time is short and that not every company (well, probably only a few companies) within the European Union will be 100 % ready on 25th May 2018 and this also counts for those outside EU who is targeting EU citizens or processing personal data from the EU.

However, most EU governments are not any better. According to a recent communication from the EU only two Member States (Germany and Austria) have adopted the necessary national legislation. And from own experience I can tell that the late incoming of the national legislation does not help in getting the details ready for 25th May.

Some areas where national legislation is important were discussed in the post Where GDPR Still Becomes National. In my eyes, the remaining governments do not set an example for companies who are struggling with this (else justified) extra work.

GDPR  adoption.png
What Brussels says 24th January 2018

Providing a Digital Technology Platform

Gartner, the analyst firm, defines five different types of digital technology platforms:

  • Information system platform — Supports the back office operations such as ERP, CRM, PIM and other core systems with associated middleware and development capabilities.
  • Customer experience platform — Contains the main customer-facing elements, such as customer and citizen portals, multichannel commerce, and customer apps.
  • Analytics and intelligence platform — Contains information management and analytical capabilities. Data management programs and analytical applications fuel data-driven decision making, and algorithms automate discovery and action.
  • IoT platform — Connects physical assets for monitoring, optimization, control and monetization.
  • Business ecosystem platform — Supports the creation of, and connection to, external ecosystems, marketplaces and communities.
Gartner Digital Platforms 2
Source: Gartner

As a vendor of a modern data management platform, you will probably identify yourself primarily within one of these five types.

At Product Data Lake we are first and foremost a business ecosystem platform, being a cloud service for sharing product data in the business ecosystems of manufacturers, distributors, merchants and the end users of product information. As such, we are proud to be a part of the The Rise of Business Ecosystems in Data Management.

Of course, there are ties to the other types of digital technology platforms as well. As explained in the post Adding Things to Product Data Lake, the ecosystem approach is necessary to identify and track physical assets. Analytics will encompass data, as for example product data, in the business ecosystem. Customer experience in multichannel commerce when it comes to completeness of product information will require an effective cross company digital technology platform.

An external focused business ecosystem platform will have to be easily connected to the various internal focused information system platforms at trading partners. In our case, this is What a PIM-2-PIM Solution Looks Like.

 

5 Product Data Levels to Consider

When talking about Product Master Data Management (Product MDM) Product Information Management (PIM) I like to divide the different kinds of product data into the schema below:

Five levels

Level 1, Basic Data

At the first level, we find the basic product data that typically is the minimum required for creating a product in any system of record.

Here we find the primary product identification number or code that is the internal key to all other product data structures and transactions related to the product within an organization.

Then there usually is a short product description. This description helps internal employees identifying a product and distinguishing that product from other products. Most often the product is named in the official language of the company.

If an upstream trading partner produces the product, we may find the identification of that supplier here too. If the product is part of internal production, we may have a material type telling about if it is a raw material, semi-finished product, finished good or packing material.

Level 2, Trading Data

The second level has product data related to trading the product. We may have a unique Global Trade Item Number (GTIN) that may be in the form of an International – former European – Article Number (EAN) or a Universal Product Code (UPC). Here we have commodity codes and a lot of other product data that supports buying, receiving, selling and delivering the product.

Level 3, Recognition Data

On the third level, we find the two basic pieces of product information that came to existence when we started producing product catalogues and had the first ecommerce solutions online.

The extended product description is needed because the usual short product description used internally have no meaning to an outsider as told in the post Customer Friendly Product Master Data. Some good best practices for governing the extended product description is to have a common structure of how the description is written, not to use abbreviations and to have a strict vocabulary as reported in the post Toilet Seats and Data Quality.

We often see that the extended product descriptions need to be present in the range of languages covering the locations where business is done either if the business is international or done in a country with multiple countries. The trend of increased user customization (or should I say customisation) drives this point further.

Having a product image is pivotal if you want to sell something without showing the real product face-to-face with the customer or other end user. A missing product image is a sign of a broken business process for collecting product data as pondered in the post Image Coming Soon.

Level 4, Self-service Data

At the fourth level, we have three main sorts of product information: Product attributes, basic product relations and standard digital assets. These data supports when customers makes buying decisions within eCommerce and other self-service scenarios.

Product attributes are also sometimes called product properties or product features. These are up to thousands of different data elements that describes a product. Some are very common for most products like height, length, weight and colour. Some are very specific to the product category. This challenge is the reason of being for dedicated Product Information Management (PIM) solutions as told in the post MDM Tools Revealed.

Basic product relations are the links between a product and other products like a product that have several different accessories that goes with the product or a product being a successor of another now decommissioned product. Product relations is described further in the post Related Products: The Often Overlooked Facet of PIM.

Standard digital assets are documents like installation guides, line drawings and data sheets as examined in the post Digital Assets and Product MDM.

Level 5, Competitive Data

As the fifth level we find elements like on the fourth level, but usually these are elements that you won’t necessarily apply to all products but only to your top products where you want to stand out from the crowd and distance yourself from your competitors. If you are a reseller, you typically make these data yourself, where level 4 hard facts are delivered from the manufacturer, as examined in the post Using Internal and External Product Information to Win.

Special content are descriptions of and stories about the product above the hard features. Here you tell about why the product is better than other products and in which circumstances the product can to be used. A common aim with these descriptions is also Search Engine Optimization.

X-sell (cross-sell) and up-sell product relations applies to your particular mix of products and may be made subjective as for example to look at up-sell from a profit margin point of view. X-sell and up-sell relations may be defined from upstream by you or your upstream trading partners but also dripping down on the roof from the behaviour of your downstream trading partners / customers as manifested in the classic webshop message: “Those who bought product A also bought / looked at product B”.

Advanced digital assets are broader and more lively material than the hard fact line drawings and other documents. Increasingly newer digital media types as video are used for this purpose.

Product Classification, Product Pricing and Product Lifecycle Status

All of the above-mentioned levels of product information is supported by product classification. Usually we see product classification handled as a reference data type across Product Information Management (PIM), ERP and Product Lifecycle Management (PLM) where applicable.

Product pricing is usually also a subject mainly belonging to the ERP side of things.

Product Lifecycle Status again goes across Product Information Management (PIM), ERP and not at least Product Lifecycle Management (PLM) where applicable.

Master Data Management (MDM) is the discipline that connects the dots between these topics.

Take the processes to next level:

You can take your Product Information Management (PIM) and Product Master Data Management (Product MDM) to a higher level by following the processes as described in the post Using Pull or Push to Get to the Next Level in Product Information Management.

The 360 Ways to Improving Customer Experiences

In today’s blog post over on The Disruptive Master Data Management Solutions List the CEO of AllSight, David Corrigan, examines 3 Reasons MDM No Longer Delivers a Customer 360.

In here David explores the topics in the new era of the customer 360 degree view being encompassing all customer data, covering analytical and operational usages and improving customer experience.

The post includes this testimonial from Deotis Harris, Senior Director, MDM at Dell EMC: “We saw an opportunity to leverage AllSight’s modern technology (Customer Intelligence), coupled with our legacy systems such as Master Data Management (MDM), to provide the insight required to enable our sellers, marketers and customer service reps to create better experiences for our customers.”

By the way: Being a MDM practitioner who have spent many years with customer 360 and now spending equal chunks of time with product 360, I find the forward-looking topics being very similar between customer 360 and product 360. In short:

  • The span of product data to handle has increased dramatically in recent years as told in the post Self-service Ready Product Data.
  • We can use the same data architecture for analytical and operational purposes as mentioned in the post The Intersection of MDM and Big Data.
  • It is all about creating better experiences for your customers.

360

 

Welcome AllSight on the Disruptive MDM List

I am thrilled to welcome AllSight as the next disruptive MDM solution on The Disruptive Master Data Management Solutions list.

AllSight2I resonate very well with the AllSight Advantage that is: “The hardest part about understanding the customer is representing them within archaic systems designed to manage ‘customer records’.  AllSight manages all customer data in its original format.  It creates a realistic and accurate likeness of who your customer actually is.  Really knowing your customer is the first step to being intelligent about your customers.”

A true disruptive approach in my eyes.

Check out the full Disruptive Master Data Management Solutions list here.

Gall’s Law, Minimal Viable Product and Product Information Management

Back in 1975 John Gall expressed this law: A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works and cannot be patched up to make it work. You have to start over with a working simple system.

Since then, we have learned the term Minimal Viable Product, which has the same sentiment.

Building a product from scratch by starting small and making that work in a context new to most potential users is close to me these days. The Product Data Lake venture I am involved with is exploring some frontiers as:

Our business model also allows you as a partner or customer to start small and influence the solution while you achieve the business benefits in a more and more profitable way.

As a subscriber you can grow with us through these phases:

  • Skateboard: Start testing with a selected trading partner. As a manufacturer you can push your product information in your way and as a merchant you can pull this information in your way.
  • Bicycle: Pedal further by including more trading partners. It will be a win-win for everyone.
  • Motorbike: Fuel the business benefits by reaching out to dozens of trading partners.
  • Car: Encompass all your regular trading partners in a comfortable journey where you – and your trading partners – will sell more and reduce costs.

mvp

How to Combine eClass and ETIM

eClass and ETIM are two different standards for product information.

eCl@ss is a cross-industry product data standard for classification and description of products and services emphasizing on being a ISO/IEC compliant industry standard nationally and internationally. The classification guides the eCl@ss standard for product attributes (in eClass called properties) that are needed for a product with a given classification.

ETIM develops and manages a worldwide uniform classification for technical products. This classification guides the ETIM standard for product attributes (in ETIM called features) that are needed for a product with a given classification.

It is worth noticing, that these two standards are much more elaborate than for example the well-known classification system called UNSPSC, as UNSPSC only classifies products, but does not tell which attributes (and with what standards) you need to specify a product in detail.

There is a cooperation between eClass and ETIM which means, that you can map between the two standards. However, it will not usually make sense for one organization to try to use both standards at the same time.

PDL How it worksWhat does make sense is combining the two standards, if there are two trading partners where one uses one of these standards and the other one uses the other standard. The place to make the combination is within Product Data Lake, the new service for exchanging product information between manufacturers and merchants. Here, trading partners can make a:

How Bosch is Aiming for Unified Partner Master Data Management

A recurrent theme on this blog is the way organizations should handle party master data management as examined in the post What is Best Practice: Customer- and Vendor- or Unified Party Master Data Management?

What I advise my clients to do, is to have a common party (or business partner) structure for identification, names, addresses and contact data. This should be supported by data quality capabilities strongly build on external reference data (third party data). Besides this common structure, there should be specific structures for customer, vendor/supplier and other party roles.

This week I attended the MDM event arranged by Marcus Evans in Barcelona. The presentation by Udo Couto Klütz of the industrial giant Robert Bosch highlighted the clever way of handling customer master data, by having a partner master data concept.

Bosch

At Bosch they have reached the conclusion, that unified partner master data management is the way to achieve:

  • A single source of truth
  • Transparency
  • Standardized processes
  • Worldwide applicability
  • One compliance standard
  • Increased efficiency

I absolutely agree.

PIM-2-PIM and More Business Outcome

The importance of having a viable Product Information Management (PIM) solution has become well understood at companies participating in supply chains. Having a PIM solution provides business outcome as told here on the blog in a recent guest post featuring the Pimcore PIM solution. The post is called Investing In PIM Is Like Investing In Customer Value.

The next step towards more business outcome from PIM is to handle product information in close collaboration with your trading partners.

At Product Data Lake we provide such a PIM-2-PIM solution. Instead of the old way of collecting product information via spreadsheets and portals, we facilitate that trading partners connect as examined in the post What a PIM-2-PIM Solution Looks Like.

By doing that both manufacturers and merchants will be able to sell more and at the time reduce costs of product information exchange as shown in the post Sell more. Reduce costs.

Sell more Reduce costs