Sustainability Data in PIM

Henrik Gabs LiliendahlLeave a comment

The collection of product data to be handled within PIM (Product Information Management) systems are ever increasing. End customers want more and more data to support purchase decisions.

This theme was pondered in the post Self-Service Ready Product Data.

One new kind of product data to beware of in the future is information about sustainability measures related to a given product. This is information about the environmental impact and the social impact from producing and consuming a product.

As the founder of the Product Data Lake, a solution for exchanging product data in business ecosystems, I am very pleased that sustainability information will be included as an important kind of product data ready to be exchanged between trading partners.

EA
Earth Accounting

This is due to a cooperation with Earth Accounting. The Product Data Lake will be an integrated part of the information cooperative, where the Product Data Lake will facilitate forward looking manufacturers in providing their own sustainability measures along with all other kind of product data and where progressive distributors and retailers can receive and eventually publish sustainability data along with all other self-service ready product data.

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Putting Context into Data Lakes

Henrik Gabs LiliendahlLeave a comment

The term data lake has become popular along with the raise of big data. A data lake is a new of way of storing data that is more agile than what we have been used to in data warehouses. This is mainly based on the principle that you should not have thought through every way of consuming data before storing the data.

This agility is also the main reason for fear around data lakes. Possible lack of control and standardization leads to warnings about that a data lake will quickly develop into a data swamp.

LakeIn my eyes we need solutions build on the data lake concept if we want business agility – and we do want that. But I also believe that we need to put data in data lakes in context.

Fortunately, there are many examples of movements in that direction. A recent article called The Informed Data Lake: Beyond Metadata by Neil Raden has a lot of good arguments around a better context driven approach to data lakes.

As reported in the post Multi-Domain MDM 360 and an Intelligent Data Lake the data management vendor Informatica is on that track too.

In all humbleness, my vision for data lakes is that a context driven data lake can serve purposes beyond analytical use within a single company and become a driver for business agility within business ecosystems like cross company supply chains as expressed in the LinkedIn Pulse post called Data Lakes in Business Ecosystems.

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Reducing the Reverse Supply Chain by Improving the Forward Data Supply Chain

Henrik Gabs LiliendahlLeave a comment

An increasing issue arisen in the customer self-service age – first and foremost as seen in e-commerce – is the increasing reverse supply chain. A reverse supply chain is the flow of products being returned down the supply chain because the end customer did not want or like the product.

There are several reasons for returned products. Bad product quality is an old known reason. Bad data quality is a new important reason. Bad data quality is when the end customer did not have the right data to support the purchase. The main root cause for this is incomplete data as missing specification, missing images and other digital assets as well as missing information about related products.

Some different kinds of product data was examined in the post Self-Service Ready Product Data. Data that supports customer self-service sales approaches are mainly those data that should be provided through the forward supply chain, meaning that they are originated at the manufacturer and then passed and possibly value added by distributors and retailers.

Increasing reverse supply chains is a huge problem both from a business standpoint due to increased costs and from a society standpoint due to increased environmental impact. To decrease the reverse supply chain we need better means to put comprehensive product information through the forward supply chain in a timely matter.

The Product Data Lake is a solution to do so, as the Product Data Lake ensures:

  • Completeness of product information by enabling trading partners to exchange product data in a uniform way
  • Timeliness of product information by connecting trading partners in a process driven way

Further more, the Product Data Lake ensures:

  • Conformity of product information by encompassing various international standards for product information
  • Consistency of product information by allowing upstream trading partners and downstream trading partners to interact with in-house structure of product information
  • Accuracy of product information by ensuring transparency of product information across the supply chain

Please find more information about the Product Data Lake here.pdl-diagram-new

 

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Cultured Freshwater Pearls of Wisdom

Henrik Gabs LiliendahlLeave a comment

One of my current engagements is within jewelry – or is it jewellery? The use of these two respectively US English and British English words is a constant data quality issue, when we try to standardize – or is it standardise? – to a common set of reference data and a business glossary within an international organization – or is it organisation?

Looking for international standards often does not solve the case. For example, a shop that sells this kind of bijouterie, may be classified with a SIC code being “Jewelry store” or a NACE code being “Retail sale of watches and jewellery in specialised stores”.

shiny thingsA pearl is a popular gemstone. Natural pearls, meaning they have occurred spontaneously in the wild, are very rare. Instead, most are farmed in fresh water and therefore by regulation used in many countries must be referred to as cultured freshwater pearls.

My pearls of wisdom respectively cultured freshwater pearls of wisdom for building a business glossary and finding the common accepted wording for reference data to be used within your company will be:

  • Start looking at international standards and pick what makes sense for your organization. If you can live with only that, you are lucky.
  • If not, grow the rest of the content for your business glossary and reference data by imitating the international or national standards for your industry, and use your own better wording and additions that makes the most sense across your company.

And oh, I know that pearls of wisdom are often used to imply the opposite of wisdom 🙂

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Social Selling: Does it Work?

Henrik Gabs Liliendahl3 Comments

Social Master Data Management (Social MDM) has been on my radar for quite a long time. Social MDM is the natural consequence of Social CRM and social selling.

Social MDMNow social selling has become very close to me in the endeavour of putting a B2B (Business-to-Business) cloud service called Product Data Lake on the market.

In our quest to do that we rely on social selling for the following reasons:

  • If we do not think too much about, that time is money, social selling is an inexpensive substitution for a traditional salesforce, not at least when we are targeting a global market.
  • We have a subscription model with a very low entry level, which really does not justify many onsite meetings outside downtown Copenhagen – but we do online meetings based on social engagement though 🙂
  • The Product Data Lake resembles a social network itself by relying on trading partnerships for exchange of product information.

I will be keen to know about your experiences and opinions about social selling. Does it work? Does it pay off to sell socially? Does it feel good to buy socially?

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Choosing the Best Term to Use in MDM

Henrik Gabs LiliendahlLeave a comment

Right now I am working with a MDM (Master Data Management) service for sharing product data in the business ecosystems of manufacturers, distributors, retailers and end users of product information.

One of the challenges in putting such a service to the market is choosing the best term for the entities handled by the service.

Below is the current selection with the chosen term and some recognized alternate terms used frequently and found in various standards that exists for exchanging product data:

Terms

Please comment, if you think there are other English (or variant of English) terms that deserves to be in here.

Related Products: The often Overlooked Facet of PIM

Henrik Gabs Liliendahl4 Comments

Related products

As examined in the post Self-service Ready Product Data, there are three main different kinds of information, which we deal with within Product Information Management (PIM). These are

  • 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 PIM solutions.
  • Digital assets are documents like product images, installation guides, line drawings, data sheets and more advanced formats as videos. You may handle these digital assets in a dedicated Digital Asset Management (DAM) system or use facilities within a PIM solution or other kind of solutions for that.
  • Related products 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. Spare parts for a given product is another kind of product relation. And then we have cross-sell and up-sell relations.

While PIM solutions usually have good capabilities for handling related products, it is my experience that many organizations does not utilize this very well.

One challenge is that related products can be sourced in various ways as told in the post Related Parties, Products and Locations. These ways are:

  • From the manufacturer of the product. This source is often good when it comes to product relationship types as accessory and replacement (succession) as well as spare part relations.
  • From the customer. We know this approach from the online sales trick prompting us with the message “People who bought A also bought B”.
  • From internal considerations. Facilitating up-sell can be done by enhancing product data with that kind of product relation.

Sourcing product relations from the manufacturer through the supply chain is a must for solutions that facilitates exchange of product data in business ecosystems. In the Product Data Lake we consequently handle the sharing of product attributes, digital assets and related products.

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Multi-Domain MDM 360 and an Intelligent Data Lake

Henrik Gabs LiliendahlLeave a comment

This week I had the pleasure of being at the Informatica MDM 360 event in Paris. The “360” predicate is all over in the Informatica communication. There are the MDM 360 events around the world.  The Product 360 solution – the new wrap of the old Heiler PIM solution, as I understand it. The Supplier 360 solution. Some Customer 360 stuff including the Cloud Customer 360 for Salesforce edition.

GW MDMAll these solutions constitutes one of the leading Multi-Domain MDM offerings on the market – if not the leading. We will be wiser on that question when Gartner (the analyst firm) makes their first Multi-Domain MDM Magic Quadrant later this year as reported in the post Gravitational Waves in the MDM World.

Until now, Informatica has been very well positioned for Customer MDM, but not among the leaders for Product MDM in the ranking according to Gartner. Other analysts, as Information Difference, have Informatica in the top right corner of the (Multi-Domain) MDM landscape as seen here.

MDM and big data is another focus area for Informatica and Informatica has certainly been one of the first MDM vendors who have embraced big data – and that not just with wording in marketing. Today we cannot say big data without saying data lake. Informatica names their offering the Intelligent Data Lake.

For me, it will be interesting to see how Informatica can take full Multi-Domain MDM leadership with combining a good Product MDM solution with an Intelligent Data Lake.

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1st Party, 2nd Party and 3rd Party Master Data

Henrik Gabs LiliendahlLeave a comment

Until now, much of the methodology and technology in the Master Data Management (MDM) world has been about how to optimize the use of what can be called first party master data. This is master data already collected within your organization and the approaches to MDM and the MDM solutions offered has revolved around federating internal silos and obtain a single source of truth within the corporate walls.

Besides that third-party data has been around for many years as described in the post Third-Party Data and MDM. Use of third party data in MDM has mainly been about enriching customer and supplier master data from business directories and in some degree utilizing standardized pools of product data in various solutions.

open doorUsing third party data for customer and supplier master data seems to be a very good idea as exemplified in the post Using a Business Entity Identifier from Day One. This is because customer and supplier master looks pretty much the same to every organization. With product master data this is not case and that is why third party sources for product master data may not be fully effective.

Second party data is data you get directly from the external source. With customer and supplier master data we see that approach in self-registration services. My recommendation is to combine self-registration and third party data in customer and supplier on-boarding processes. With product master data I think leaning mostly to second party connections in business ecosystems seems like the best way forward. There is more on that in a discussion on the LinkedIn  MDM – Master Data Management Group.

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Using a Business Entity Identifier from Day One

Henrik Gabs LiliendahlLeave a comment

One of the ways to ensure data quality for customer – or rather party – master data when operating in a business-to-business (B2B) environment, is to on-board new entries using an external defined business entity identifier.

By doing that, you tackle some of the most challenging data quality dimensions as:

  • Uniqueness, by checking if a business with that identifier already exist in your internal master data. This approach is superior to using data matching as explained in the post The Good, Better and Best Way of Avoiding Duplicates.
  • Accuracy, by having names, addresses and other information defaulted from a business directory and thus avoiding those spelling mistakes that usually are all over in party master data.
  • Conformity, by inheriting additional data as line-of-business codes and descriptions from a business directory.

Having an external business identifier stored with your party master data helps a lot with maintaining data quality as pondered in the post Ongoing Data Maintenance.

Busienss Entity IdentifiersWhen selecting an identifier there are different options as national IDs, LEI, DUNS Number and others as explained in the post Business Entity Identifiers.

At the Product Data Lake service I am working on right now, we have decided to use an external business identifier from day one. I know this may be something a typical start-up will consider much later if and when the party master data population has grown. But, besides being optimistic about our service, I think it will be a win not to have to fight data quality issues later with guarantied increased costs.

For the identifier to use we have chosen the DUNS Number from Dun & Bradstreet. The reason is that this currently is the only worldwide covered business identifier. Also, Dun & Bradstreet offers some additional data that fits our business model. This includes consistent line-of-business information and worldwide company family trees.

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