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.

Product Data Lake Behind the Scenes

Product Data Lake is a cloud service for exchanging product information between manufacturers, distributors and merchants. When telling about the service I usually concentrate on the business benefits and how the service will make you sell more and reduce costs.

However, there will always be one or two persons in the audience who wants to know about the technology behind. And for sure, this is important too.

The service is built using some of the newest and best-of-breed technologies available for this purpose today. This includes Amazon Elastic Computing Cloud for hosting the public cloud version, MongoDB for storing data, RabbitMQ for handling data streams and ElasticSearch for finding data.

PDL Architecture

You can dive into the geeky parts in this PDF document: Product Data Lake Architecture.

MDM in The Cloud, On-Premise or Both

One of the forms of Master Data Management (MDM) is the rising cloud deployment model as touched in the Disruptive MDM List blog post about 8 Forms of Master Data Management.

If we look at the MDM solution vendors, they may in that sense be divided into three kinds:

  • Cloud only, which are vendors born in the cloud age and who are delivering their service in the cloud only. Reltio is an example of that kind of MDM vendor.
  • Cloud or on-premise, which are vendors that can deliver both in the cloud and on premise, but where it makes most sense that you as a customer chooses the one that fits you the best. An example is Semarchy.
  • Cloud and on-premise. Informatica is the example of an MDM vendor that embraces both deployment models (together with other data management disciplines) at the same time (called hybrid) as told in an article by Kristin Nicole of SiliconANGLE. The title goes like this: Balancing act: Informatica straddles on-prem needs with cloud data at Informatica World 2018

Cloud MDM

Happiness vs Market Strength

When following analyst market reports one thing that always strike me is that the vendors who have charged the most for licenses (being to the right on the market strength axis) seldom are the same as those having the most satisfied customers.

The Data Quality Product Landscape 2018 from Information Difference has no surprises there either.

On the technology vertical axis, the vendors are pretty even, while they stretch out on the horizontal market strength axis.

DQ Landscape 2018

The report states: “The happiest customers based on this survey were those of Datactics followed by ActivePrime”. You will find those to the left.

(Innovative Systems, Experian and Syncsort were the better of the rest it must be said.)

See the full report here.

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).

Get

What is Interenterprise Data Sharing?

The term “Interenterprise Data Sharing” has been used a couple of times by Gartner, the analyst firm, during the last two decades.

Lately it has been part of the picturing in conjunction with a recent research document with the title Fundamentals for Data Integration Initiatives.

Data Integration.png
Source: Gartner Inc with red ovals added

The term was also used back in 2001 in the piece about that Data Ownership Extends Outside the Enterprise. Here on the blog it was included in the title of the post about Interenterprise Data Sharing and the 2016 Data Quality Magic Quadrant.

In my eyes interenterprise data sharing is closely related to how you can achieve business benefits from taking part in the ecosystem flavor of a digital business platform. Some of the data types where we will see such business ecosystem platform flourish will be around sharing product model master data and data about and coming from things related to the Internet of Things (IoT) theme. This is further explained in the blog page about Master Data Share.

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.

Trending Topic: Graph and MDM

Using graph data stores and utilizing the related capabilities has become a trending topic in the Master Data Management (MDM) space. This opportunity was first examined 5 years ago here on the blog in the post Will Graph Databases become Common in MDM? It seems so.

Recently David Borean, Chief Data Science Officer at the disruptive MDM vendor AllSight, wrote the blog post The real reason why Master Data Management needs Graph. In here David confirms the common known understanding of that graph databases are superior compared to relational databases when it comes to handle relationships within master data. But David also brings up how graph databases can support multiple versions of the truth.

graph MDMSeveral other vendors as Semarchy and Reltio are emphasizing on graph in MDM in their market messaging.

Aaron Zornes of The MDM Institute is another proponent of using graph technology within MDM as mentioned over at The Disruptive MDM Solutions blog in the post MDM Fact or Fiction: Who Knows?

What do you think: Will graph databases really brake through in MDM soon? Will it be as stand alone graph technology (as for example from neo4j) or embedded in MDM vendor portfolios?

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.

Welcome Enterworks, Contentserv and SyncForce on The Disruptive MDM List

I am happy to welcome three new entries on The Disruptive Master Data Management Solutions List.

This site is meant to be a list of available:

  • Master Data Management (MDM) solutions
  • Customer Data Integration (CDI) solutions
  • Product Information Management (PIM) solutions
  • Digital Asset Management (DAM) solutions

Organizations on the look for a solution of the above kind can use this site as an alternative to the likes of Gartner, Forrester, MDM Institute and others, not at least because this site will include the market leaders as well as smaller and disruptive solutions with specific use case, geographical, industry or other best of breed capabilities.

The new entries are:

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  • EnterWorks who is among the market leaders in multi-domain master data solutions for acquiring, managing and transforming a company’s multi-domain master data into persuasive and personalized content for marketing, sales, digital commerce and new market opportunities.
  • Contentserv thumbCONTENTSERV who offers a real-time Product Experience Platform. This integrated and product centric solution seamlessly combines the functionalities of multi domain Master Data Management, Product Information Management & Marketing Content Management.
  • SyncForce-plus-iconSyncForce who makes your product portfolio digitally available with a click of a button, in every shape and form, both internal and external, so you can shift your attention from fire fighting to building successful business with your trading partners.

You can visit the list here.

New logos 20180313

 If you are a vendor, you can register your solution here.