There is no PIM quadrant, but there is a PIM wave

2018-Forrester-PIM-WaveWith the, in my eyes well justified, merge of the two Master Data Management (MDM) quadrants Gartner, the analyst firm, is somehow missing some ranking of specialised Product Information Management (PIM) vendors.

However, Forrester, the other analyst firm, still have their wave with the fresh new Forrester Wave™: Product Information Management Solutions, Q2 2018.

Two of the leaders have already announced their position as you can see here with Enterworks and Contentserv.

If you want to know more about the best PIM solutions on the market, you can also read about Enterworks, Contentserv, Stibo Systems and Riversand on the disruptive list of MDM, PIM and DAM solutions.

 

Ecosystem Wide Product Information Management

The concept of doing Master Data Management (MDM) not only enterprise wide but ecosystem wide was examined in the post Ecosystem Wide MDM.

As mentioned, product master data is an obvious domain where business outcomes may occur first when stretching your digital transformation to encompass business ecosystems.

The figure below shows the core delegates in the ecosystem wide Product Information Management (PIM) landscape we support at Product Data Lake:

Ecosystem Wide PIM.png

Your enterprise is in the centre. You may have or need an in-house PIM solution where you manipulate and make product information more competitive as elaborated in the post Using Internal and External Product Information to Win.

At Product Data Lake we collaborate with providers of Artificial Intelligence (AI) capabilities and similar technologies in order to improve data quality and analyse product information.

As shown in the top, there may be a relevant data pool with a consensus structure for your industry available, where you exchange some of product information with trading partners. At Product Data Lake we embrace that scenario with our reservoir concept.

Else, you will need to make partnerships with individual trading partners. At Product Data Lake we make that happen with a win-win approach. This means, that providers can push their product information in a uniform way with the structure and with the taxonomy they have. Receivers can pull the product information in a uniform way with the structure and with the taxonomy they have. This concept is outlined in the post Sell more. Reduce costs.

Where to Buy a Magic Wand?

Sometimes you may get the impression that sales, including online sales, is driven by extremely smart sales and marketing people targeting simple-minded customers.

Let us look at an example with selling a product online. Below are two approaches:

Magic wand

Bigger picture is available here.

My take is that the data rich approach is much more effective than the alternative (but sadly often used one). Some proof is delivered in the post Ecommerce Su…ffers without Data Quality.

In many industries, the merchant who will cash in on the sale will be the one having the best and most stringent data, because this serves the overwhelming majority of buying power, who do not want to be told what to buy, but what they are buying.

So, pretending to be an extremely smart data management expert, I will argue that you can monetize on product data by having the most complete, timely, consistent, conform and accurate product information in front of your customers. This approach is further explained in the piece about Product Data Lake.

Product Data Lake Version 1.7 is Live

Win-Win

The good thing about providing Software-as-a-Service is that you do not have to ship the software to all your users and the good thing about using Software-as-a-Service is that program updates are immediately available to the users without that an IT department has to schedule, plan, test and go live with a new version of an application.  This is also true for Product Data Lake, the cloud service also being a win-win application by providing business benefits to both manufacturers and merchants of goods.

Using Application Programming Interfaces (APIs)

Already existing means to feed to and consume product information from Product Data Lake include FTP file drops, traditional file upload from your desktop or network drives or actually entering data into Product Data Lake. With version 1.7, that went live this week, you can now also use our APIs for system to system data exchange by both pushing (put) data into the lake and pulling (get) data from the lake.

Get the Overview

Get the full Product Data Lake Overview here (opens a PDF file).

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The Cases for Data Matching in Multi-Domain MDM

Data matching has always been a substantial part of the capabilities in data quality technology and have become a common capability in Master Data Management (MDM) solutions.

We use the term data matching when talking about linking entities where we cannot just use exact keys in databases.

The most prominent example around is matching names and addresses related to parties, where these attributes can be spelled differently and formatted using different standards but do refer to the same real-world entity. Most common scenarios are deduplication, where we clean up databases for duplicate customer, vendor and other party role records and reference matching, where we identify and enrich party data records with external directories.

A way to pre-process party data matching is matching the locations (addresses) with external references, which has become more and more available around the world, so you have a standardized address in order to reduce the fuzziness. In some geographies you can even make use of more extended location data, as whether the location is a single-family house, a high-rise building, a nursing home or campus. Geocodes can also be brought into the process.

matching MDMHandling the location as a separate unique entity can also be used in many industries as utility, telco, finance, transit and more.

For product data achieving uniqueness usually is a lesser pain point as told in the post Multi-Domain MDM and Data Quality Dimensions. But for sure requirements for matching products arises from time to time.

In the old days this was quite difficult as you often only had a product description that had to be parsed into discrete elements as examined in the post Matching Light Bulbs.

With the rise of Product Information Management (PIM) we now often do have the product attributes in a granular form. However, using traditional matching technology made for party master data will not do the trick as this is a different and more complex scenario. My thinking is that graph technology will help as touched in the post Three Ways of Finding a Product.

Data Pool vs Data Lake

Within Product Information Management (PIM) – or Product Master Data Management if you like – there is a concept of a data pool.

Recently Justine Rodian of Stibo Systems made a nice blog post with the title Master Data Management Definitions: The Complete A-Z of MDM. Herein Justine explains a lot of terms within Master Data Management (MDM). A data pool is described as this:

“A data pool is a centralized repository of data where trading partners (e.g., retailers, distributors or suppliers) can obtain, maintain and exchange information about products in a standard format. Suppliers can, for instance, upload data to a data pool that cooperating retailers can then receive through their data pool.”

Now, during the last couple of year I have been working on the concept of applying the data lake approach to product information exchange between trading partners. Justine describes a data lake this way:

“A data lake is a place to store your data, usually in its raw form without changing it. The idea of the data lake is to provide a place for the unaltered data in its native format until it’s needed…..” 

Product Data Lake
MacRitchie Reservoir in Singapore

For a provider of product information, typically a manufacturer, the benefit of interacting via a data lake opposite to a data pool is that they do not have to go through standardization before uploading and thus have to shoehorn the data into a specific form and thereby almost certainly leave out important information and being depending on consensus between competing manufacturers.

For a receiver of information, typically a merchant as a retailer and B2B dealer, the benefit of interacting via a data lake opposite to a data pool is that they can request the data in the form they will use to be most competitive and thereby sell more and reduce costs in product information sharing. This will be further accelerated if the merchant uses several data pools.

In Product Data Lake we even combine the best of the two approaches by encompassing data pools in our reservoir concept – to stay in the water body lingo. Here data pools are refreshed with modern data management technology and less rigid incoming and outgoing streams as announced in the post Product Data Lake Version 1.3 is Live.

Seven Flavors of MDM

Master Data Management (MDM) can take many forms. An exciting side of being involved in MDM implementations is that every implementation is a little bit different which also makes room for a lot of different technology options. There is no best MDM solution out there. There are a lot of options where some will be the best fit for a given MDM implementation.

The available solutions also change over the years – typically by spreading to cover more land in the MDM space.

In the following I will shortly introduce the basic stuff with seven flavours of MDM. A given MDM implementation will typically be focused on one of these flavours with some elements of the other flavors and a given piece of technology will have an origin in one of these flavours and in more or less degree encompass some more flavors.

7 flavours

The traditional MDM platform

A traditional MDM solution is a hub for master data aiming at delivering a single source of truth (or trust) for master data within a given organization either enterprise wide or within a portion of an enterprise. The first MDM solutions were aimed at Customer Data Integration (CDI), because having multiple and inconsistent data stores for customer data with varying data quality is a well-known pain point almost everywhere. Besides that, similar pain points exist around vendor data and other party roles, product data, assets, locations and other master data domains and dedicated solutions for that are available.

Product Information Management (PIM)

Special breed of solutions for Product Information Management aimed at having consistent product specifications across the enterprise to be published in multiple sales channels have been around for years and we have seen a continuously integration of the market for such solutions into the traditional MDM space as many of these solutions have morphed into being a kind of MDM solution.

Digital Asset Management (DAM)

Not at least in relation to PIM we have a distinct discipline around handling digital assets as text documents, audio files, video and other rich media data that are different from the structured and granular data we can manage in data models in common database technologies. A post on this blog examines How MDM, PIM and DAM Stick Together.

Big Data Integration

The rise of big data is having a considerable influence on how MDM solutions will look like in the future. You may handle big data directly inside MDM og link to big data outside MDM as told in the post about The Intersection of MDM and Big Data.

Application Data Management (ADM)

Another area where you have to decide where master data stops and handling other data starts is when it comes to transactional data and other forms data handled in dedicated applications as ERP, CRM, PLM (Product Lifecycle Management) and plenty of other industry specific applications. This conundrum was touched in a recent post called MDM vs ADM.

Multi-Domain MDM

Many MDM implementations focus on a single master data domain as customer, vendor or product or you see MDM programs that have a multi-domain vision, overall project management but quite separate tracks for each domain. We have though seen many technology vendors preparing for the multi-domain future either by:

  • Being born in the multi-domain age as for example Semarchy
  • Acquiring the stuff as for example Informatica and IBM
  • Extend from PIM as for example Riversand and Stibo Systems

MDM in the cloud

MDM follows the source applications up into the cloud. New MDM solutions naturally come as a cloud solution. The traditional vendors introduce cloud alternatives to or based on their proven on-promise solutions. There is only one direction here: More and more cloud MDM – also as customer as business partner engagement will take place in the cloud.

Ecosystem wide MDM

Doing MDM enterprise wide is hard enough. But it does not stop there. Increasingly every organization will be an integrated part of a business ecosystem where collaboration with business partners will be a part of digitalization and thus we will have a need for working on the same foundation around master data as reported in the post Ecosystem Wide MDM.

Ecosystem Wide MDM

Doing Master Data Management (MDM) enterprise wide is hard enough. The ability to control master data across your organization is essential to enable digitalization initiatives and ensure the competitiveness of your organization in the future.

But it does not stop there. Increasingly every organization will be an integrated part of a business ecosystem where collaboration with business partners will be a part of digitalization and thus we will have a need for working on the same foundation around master data.

The different master data domains will have different roles to play in such endeavors. Party master will be shared in some degree but there are both competitive factors, data protection and privacy factors to be observed as well.

MDM Ecosystem

Product master data – or product information if you like – is an obvious master data domain where you can gain business benefits from extending master data management to be ecosystem wide. This includes:

  • Working with the same product classifications or being able to continuously map between different classifications used by trading partners
  • Utilizing the same attribute definitions (metadata around products) or being able to continuously map between different attribute taxonomies in use by trading partners
  • Sharing data on product relationships (available accessories, relevant spare parts, updated succession for products, cross-sell information and up-sell opportunities)
  • Having access to latest versions of digital assets (text, audio, video) associated with products

The concept of ecosystem wide Multi-Domain MDM is explored further is the article about Master Data Share.

(PS: Ecosystem wide MDM is coined by Gartner, the analyst firm, as multienterprise MDM).

Spreadsheets, Business Process Re-engineering and Robots

Product information is the data a potential buyer of a product needs to make a purchasing decision. Today purchasing is more and more made by self-services as in e-commerce. The product information is usually obtained through a supply chain between trading partners stretching from the manufacturer to the end merchant.

The most common way of exchanging product information between trading partners is using spreadsheets. Spreadsheets are marvellous, because you can do almost anything you want with them. However, spreadsheets are also horrendous, because you can do almost anything you want with them. Therefore, trading partners are often stuck with manual, cumbersome and error prone processes on both the providing and receiving end.

At Product Data Lake we have developed a new mechanism that enables a whole new process for exchanging product information between trading partners. We have kept the flexibility of spreadsheets when it comes to choosing the data standards on the providing and receiving end but at the same time introduced automation and correctness when it comes to transferring, translating and transforming the data.

When telling about our service I am often asked if we have a nice feature for on-boarding spreadsheets. We don’t. Because the process is designed to omit the spreadsheets and transfer directly from the providers in-house product information data store(s) to the receiving in-house product information data store.

RobotThis reminds me of when we talk about using robots to substitute human labor. Then we often think about a machine that looks like a human. But effective industrial robots do not look like humans. They a designed to do a specific process much more effective than a human and will therefore not look like a human. The same is true in digitalization. When we redesign business processes to be much more effective they should not include spreadsheets.

Product Information on Demand

Video on demand has become a popular way to watch television series, films and other entertainment and Netflix is probably the most known brand for delivering that.

The great thing about watching video on demand is that you do not have to enjoy the service at the exact same time as everyone else, as it was the case back in the days when watching TV or going to the movies were the options available.

At Product Data Lake we will bring that convenience to business ecosystems, as the situation today with broadcasting product information in supply chains very much resembles the situation we had before video on demand came around in the TV/Movie world.

As a provider of product information (being a manufacturer or upstream distributor), you will push your product information into Product Data lake, when you have the information available. Moreover, you will only do that once for each product and piece of information. No more coming to each theatre near your audience and extensive reruns of old stuff.

As a receiver of product information (being a downstream distributor, reseller or large end user), you will pull product information when you need it. That will be when you take a new product into your range or do a special product sale as well as when you start to deal with a new piece of information. No more having to be home at a certain time when your supplier does the show or waiting in ages for a rerun when you missed it.

Learn more about how Product Data Lake makes your life in Product Information Management (PIM) easier by following us here on LinkedIn.

Product Data Lake