Black Friday 2017 Product Data Push

PDL_Illustration_HowAboutYouAs a manufacturer, your aim for any Black Friday will be that most of the buying going on will include your products. Or perhaps that the products sold is produced by using your products.

One way you can influence that is by ensuring that whoever sells your products have the most complete and accurate product information in front of their customers. Not at least when it comes to online selling.

Using Product Data Push into Product Data Lake is the way to get that done as fast and effortless as possible.

In the Black Friday spirit, we offer a free onboarding of your product portfolio if started before 24. December 2017.

All you have to do to get started is pushing your product portfolio with attached product information to us. And perhaps answer a few questions.

Then we will:

  • Create a Product Data Lake testing account for you free of charge for 6 months *)
  • Put your products into Product Data Lake
  • Put your product attributes into there as well
  • Put your digital assets up there too
  • Even put any related products in play also

You will the be able to push your product information in one uniform way and let your re-sellers pull it in their many ways.

Read more about Product Data Push here.

Interested? Get in touch:

*) A testing account allows 3 partners, 4,000 products, 2,000 digital assets and 3 users

Automatic for the People

R.E.M._-_Automatic_for_the_PeopleThe title of this blog post is the title of, in my rapid eye movements, one the best albums ever: Automatic for the People by R.E.M., which came out 25 years ago in 1992.

It began in manufacturing

Automation began in the manufacturing industry. Since then automation has been part of most other industries. Not at least within Information Technology, automation is part of the promise in almost every initiative.

When automating stuff, we should always be aware of not just automating old bad processes. To the most extreme, as Michael Hammer said back in 1990: Don’t Automate, Obliterate.

However, some of the most successful companies today are companies born in the information age and delivering services that in a high degree automates processes of value to their customers based on working intensively with information technology.

How can we close the loop and bring that kind of modern automation back to where it began: In the manufacturing industry? The challenges of doing that was examined by Harri Juntunen in a guest blog post called Data Born Companies and the Rest of Us.

IT will come back to manufacturing

In all humbleness we want to be part of that endeavor at Product Data Lake. Therefore, we are setting up a Product Data Push solution for manufacturers, in order to solve one of most severe issues for manufacturers today, being a dysfunctional flow of product information out to whoever is managing the point of sales for the produced goods.

Automation is the end goal. But in order to get started, we accept upload of product information in whatever format, structure and state it is available in. We will then get it in shape to be pulled by retailers, etailers and other trading partners. We will use manual workforce for that and we will use Artificial Intelligence for that too. And in the end, it will be automatic for the people.

Passive vs Active Product Information Exchange

Product information is the kind of data that usually flows cross company. The most common routes start with that the hard facts about a product originates at the manufacturer. Then the information may be used on the brands own website, propagated to a marketplace (online shop-in-shop) and also propagated downstream to distributors and retailers.

The challenge to the manufacturer is that this represent many different ways of providing product information, not at least when it comes to distributors and retailers, as these will require different structurers and formats using various standards and not being on the same maturity level.

Looking at this from the downstream side, the distributors and retailers, we have the opposite challenge. Manufacturers provide product information in different structurers and formats using various standards and are not on the same maturity level.

Supply chain participants can challenge this in a passive or an active way. Unfortunately, many have chosen – or are about to choose – the passive way. It goes like this:

  • As a manufacturer, we have a product data portal where trading partners who wants to do business with us, who obviously is the best manufacturer in our field, can download the product information we have in our structure and format using the standards we have found best.
  • As a distributor/retailer we have a supplier product data portal where trading partners who wants to do business with us, the leading player in our field, can upload the product information we for the time being will require in our structure and format using the standard(s) we have found best.

Passive vs ActiveThis approach seems to work if you are bigger than your trading partner. And many times one will be bigger than the other. But unless you are very big, you will in many cases not be the biggest. And in all cases where you are the biggest, you will not be seen as a company being easy to do business with, which eventually will decide how big you will stay.

The better way is the active way creating a win-win situation for all trading partners as described in the article about Product Data Lake Business Benefits.

Five Product Classification Standards

When working with Product Master Data Management (MDM) and Product Information Management (PIM) one important facet is classification of products. You can use your own internal classification(s), being product grouping and hierarchy management, within your organization and/or you can use one or several external classification standards.

Five External Standards

Some of the external standards I have come across are:

UNSPSC

The United Nations Standard Products and Services Code® (UNSPSC®), managed by GS1 US™ for the UN Development Programme (UNDP), is an open, global, multi-sector standard for classification of products and services. This standard is often used in public tenders and at some marketplaces.

GPC

GS1 has created a separate standard classification named GPC (Global Product Classification) within its network synchronization called the Global Data Synchronization Network (GDSN).

Commodity Codes / Harmonized System (HS) Codes

Commodity codes, lately being worldwide harmonized and harmonised, represent the key classifier in international trade. They determine customs duties, import and export rules and restrictions as well as documentation requirements. National statistical bureaus may require these codes from businesses doing foreign trade.

eClass

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

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.

pdl-whyThe Competition and The Neutral Hub

If you click on the links to some of these standards you may notice that they are actually competing against each other in the way they represent themselves.

At Product Data Lake we are the neutral hub in the middle of everyone. We cover your internal grouping and tagging to any external standard. Our roadmap includes more close integration to the various external standards embracing both product classification and product attribute requirements in multiple languages where provided. We do that with the aim of letting you exchange product information with your trading partners, who probably do the classification differently from you.

What a PIM-2-PIM Solution Looks Like

The importance of having a viable Product Information Management (PIM) solution has become well understood for companies who participates in supply chains.

The next step towards excellence in PIM is to handle product information in close collaboration with your trading partners. Product Data Lake is the solution for that. Here upstream providers of product information (manufacturers and upstream distributors) and downstream receivers of product information (downstream distributors and retailers) connect their choice of in-house PIM solution or other product master data solution as PLM (Product Lifecycle Management) or ERP.

The PIM-2-PIM solution resembles a social network where you request and accept partnerships with your trading partners from the real world.

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After connecting the next to set up is how your product attributes and digital asset types links with the one used by your trading partner. In Product Data Lake we encompass the use of these different scenarios (in prioritized order):

  • You and your trading partner uses the same standard in the same version
  • You and your trading partners uses the same standard in different versions
  • You and your trading partner uses different standards
  • You and/or your trading partners don’t use a public standard

Read more about that and the needed data governance in the post Approaches to Sharing Product Information in Business Ecosystems.

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Then it is time to link your common products. This can be done automatically if you both use a GTIN (or the older implementations as EAN number or UPC) as explained in the post Connecting Product Information. Alternatively, model numbers can be used for matching or, as a last option, the linking can be done in the interactive user interface.

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Now you and your trading partner are set to start automating the process of sharing product information. In Product Data Lake upstream providers of product information can push new products, attribute values and digital assets from the in-house PIM solution to a hot folder, where from the information is uploaded by Product Data Lake. Downstream receivers can set up pull requests, where the linked product information is downloaded, so it is ready to be consumed by the in-house PIM solution.

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This process can now be repeated with all your other trading partners, where you reuse the elements that are common between trading partners and build new linking where required.

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Self-service Ready Product Data

The increased use of self-service based sales approaches as in ecommerce has put a lot of pressure on cross company supply chains. Besides handling the logistics and controlling pricing, you also have to take care of a huge amount of product data and digital assets describing the goods.

You may divide product information into these five levels:

Product Information Levels

Please learn more about the five levels of product information, including how hierarchies, pricing and logistics fits in, by visiting the product information castle.

Level 4 in this model is self-service product data being:

  • Product attributes, 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 actually the reason of being for dedicated Product Information Management (PIM) solutions.
  • 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.
  • Standard digital assets are documents like installation guides, line drawings and data sheets.

These are the product data that helps the end customer comparing products and making an objective choice when buying a product for a specific purpose of use. These data are also helpful in answering the questions a buyer may have when making a purchase.

Every piece of data belonging to any level of product information may be forwarded through the cross company supply chain from the manufacturer to the end seller. Self-service product data are however the data that most obviously will do so.

In order to support end customer self-service when producing, distributing and selling goods you must establish a process driven service that automates the introduction of new products with extensive product data, the inclusion of new kinds of product data and updates to those data. You must be a digitalized member of your business ecosystem. The modern solution for that is the Product Data Lake.

Chinese Whispers and Data Quality

There is a game called Chinese Whispers or Broken Telephone or some other names. In that game, one person whispers a message to another person. The message is passed through a line of people until the last player announces the message to the entire group. At that point the message is often quite different or very shortened. The reasons for that is human unreliability including how we put our own perceptions and filters into a message.

When working with data quality you often see the same phenomenon when data is passed through a chain. One area I have observed in recent years is within Product Information Management (PIM). Here the chain is not just the data chain within a given company but the whole data chain in ecosystems of manufacturers, distributors, retailers and end users.

While Product Information Management (PIM) solutions and Product Master Data Management (Product MDM) solutions – if there is a difference – address the issues within a given company, we haven’t seen adequate solutions for solving the problem in the exchange zones between trading partners.

Broken data supply chain

From what I have seen the solutions that upstream providers of product data work with and the solutions that downstream receivers of product data work with will not go well together.

Consequently, I am right now working with a solution to end Chinese whispers in product data supply chains. Check out the Product Data Lake.