The PIM Market as Seen by IDC

The are not so many reliable market reports dealing with the Product Information Management (PIM) market. So, it is interesting that a new one from an acknowledged source has arrived.

IDC has published their first PIM MarketSpace report. As stated in the report: “Historically, PIM has been closely associated with master data management (MDM) software, which provides a central source of truth for product data. However, MDM applications, as they were originally conceived, have some shortcomings when it comes to supporting today’s digital commerce requirements.”

When ranking the vendors, IDC reached this result:

IDC Marketscape PIM 2019 20

You can, against your personal data, get a partial report from Akeneo and Salsify.

PS: You can learn more about some of the other solutions on The Disruptive MDM /PIM /DQM List.

The Two Data Quality Definitions

If you search on Google for “data quality” you will find the ever-recurring discussion on how we can define data quality.

This is also through for the top ranked none sponsored articles as the Wikipedia page on data quality and an article from Profisee called Data Quality – What, Why, How, 10 Best Practices & More!

The two predominant definitions are that data is of high quality if the data:

  • Is fit for the intended purpose of use.
  • Correctly represent the real-world construct that the data describes.

Personally, I think it is a balance.

Data Quality Definition

In theory I am on the right side. This is probably because I most often work with master data, where the same data have multiple purposes.

However, as consultant helping organizations with getting the funding in place and getting the data quality improvement done within time and budget I do end up on the other side.

What about you? Where do you stand in this question?

When Can We Expect the Next Major MDM/PIM/DQM Market Report?

When selecting a solution for Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) buyers and their advisers are using the market reports from the acknowledged analyst firms operating in these markets.

The latest such ones are:

The publication schedule from the analyst firms can be unpredictable.

Information Difference is an exception. There have during the years every year been a Data Quality landscape named Q1 and published shortly after that quarter and an MDM landscape named Q2 and published shortly after that quarter. However, these reports are relying on participation from relevant vendors and not all vendors prioritize this scheme.

Forrester is quite unpredictable both with timing and which market segments (MDM, PIM, DQM) to be covered.

Gartner is a bit steadier. However, for example the MDM solution reports have been coming in varying intervals during the latest years. Let us see when the next ones are published and what news they bring.

MDM PIM DQM Solutions

PS: You can check out many of the included solutions on The Disruptive MDM / PIM / DQM List.

It Is Black Friday and Cyber Monday All the Time at the Disruptive MDM / PIM / DQM List

The upcoming Black Friday and Cyber Monday are synonymous with good deals.

At the Disruptive MDM / PIM / DQM List there are good deals all the days.

As a potential buyer on the look for a solution covering your Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) needs you can use the free service that based on your context, scope and requirement selects the best fit solution(s). You can start here.

Black Friday

As a solution provider you can against a very modest fee register your solution here.

Happy Black Friday and Cyber Monday.

Kalido is Back

One of the first Master Data Management (MDM) solutions on the market was Kalido.

According to the IT History Society, the ideas behind Kalido started in 1985 and the first software version was around year 2000 deployed at Royal Dutch Shell Group powering dozens of projects and generating tens of millions of dollars of annual cost savings.

When the term MDM emerged in the mid 00’es, Kalido was already a capable MDM solution and the software has over the years been well positioned in analyst market reports.

In 2014 Magnitude Software acquired Kalido and the solution became a component in the Magnitude software offering without the Kalido brand.

However, Kalido is now back as a brand name. In my eyes turning back to the Kalido brand is a smart move as this name is still remembered among MDM practitioners as a pioneer solution in the MDM realm.

Since the take over Magnitude has further developed the solution adding some of the capabilities you will expect from an MDM solution today. This include enhancements to the data matching and master data survivorship functionality, elastic search capabilities and data integration now also utilizing the Magnitude Simba stack. Learn more about the latest version and the future roadmap in the post Welcome to Kalido MDM 11.

The Kalido MDM solution is now a part of a Magnitude data management suite that also covers Data Warehouse Automation and Business Information Modeling. Get more information about the Magnitude data management offering under the Kalido brand on the Magnitude MDM site.

Kalido years

Why Flexible Data Models are Crucial in Data Sharing

Master data and reference data are two types of data that are shared enterprise wide and even in the wider business ecosystem where your company operates.

In your organization and business ecosystem the data that is shared is basically held in applications like ERP and CRM solutions that have come with a data model provided by the solution vendor. These data models are built to facilitate the operations that is supported by each of these applications and is a data model that must suite every kind of organization.

A core reason of being for a Master Data Management (MDM) solution is to provide a data store where master data is represented in a way that reflects the business model of your organization. This data store serves many purposes as for example being a data integration hub and the place where the results of data quality improvements (eg de-duplication) are stored.

Data model

Such a data hub can go beyond master data entities and represent reference data and critical application data that is shared across your organization and the wider business ecosystem within a given industry.

Learn more about flexible data models in a data hub context in the Semarchy whitepaper authored by me and titled The Intelligent Data Hub: Taking MDM to the Next Level.

So, you have the algorithm! But do you have the data?

In the game of winning in business by using Artificial Intelligence (AI) there are two main weapons you can use: Algorithms and data. In a recent blog post Andrew White of Gartner, the analyst firm, says that It’s all about the data – not the algorithm.

AI iconIn the Master Data Management (MDM) space the equipment of solutions with AI capabilities has been going on for some time as reported in the post Artificial Intelligence (AI) and Master Data Management (MDM).

So, next thing is how to provide the data? It is questionable if every single organization has the sufficient (and well managed) master data to make a winning formula. Most organizations must, for many use cases, look beyond the enterprise firewall to get the training data or better the data fuelled algorithms to win the battles and the whole game.

An example of such a scenario is examined in the post Artificial Intelligence (AI) and Multienterprise MDM.

Are These Familiar Hierarchies in Your MDM / DQM / PIM Solution?

The term family is used in different contexts within Master Data Management (MDM), Data Quality Management (DQM) and Product Information Management (PIM) when working with hierarchy management and entity resolution.

Here are three frequent examples:

Consumer / citizen family

Family consumer citizenWhen handling party master data about consumers / citizens we can deal with the basic definition of a family, being a group consisting of two parents and their children living together as a unit.

This is used when the business scenario does not only target each individual person but also a household with a shared economy. When identifying a household, a common parameter is that the persons live on the same postal address (at the same time) while observing constellations as:

  • Nuclear families consisting of a female and a male adult (and their children)
  • Rainbow families where the gender is not an issue
  • Extended families consisting of more than two generations
  • Persons who happen to live on the same postal address

There are multicultural aspects of these constellations including the different family name constructions around the world and the various frequency and acceptance of rainbow families as well of frequency of extended families.

Company family tree

When handling party master data about companies / organizations a valuable information is how the companies / organizations are related most commonly pictured as a company family tree with mothers and sisters. This can in theory be in infinite levels. The basic levels are:

  • A global ultimate mother being the company that ultimately owns (fully or partly) a range of companies in several countries.
  • A national ultimate mother being the company that owns (fully or partly) a range of companies in a given country.
  • A legal entity being the basic registered company within a country having some form of a business entity identifier.
  • A branch owned by a legal entity and operating from a given postal / visiting address.

Family companyYou can build your own company tree describing your customers, suppliers and other business partners. Alternatively or supplementary, you can rely on third party business directories. It is here worth noticing that a national source will only go to the ultimate national mother level while a global source can include the global ultimate mother and thus form larger families.

Having a company family view in your master data repository is a valuable information asset within credit risk, supply risk, discount opportunities, cross-selling and more.

Product family

The term “product family” is often used to define a level in a homegrown product classification / product grouping scheme. It is used to define a level that can have levels above and levels below with other terms as “product line”, “product category”, “product class”, “product group”, “product type” and more.

Family productSometimes it is also used as a term to define a product with a family of variants below, where variants are the same product produced and kept in stock in different colours, sizes and more.

Read more about Stock Keeping Units (SKUs), product variants, product identification and product classification in the post Five Product Information Management Core Aspects.

What are the Current MDM Trends?

Even though that Master Data Management (MDM) has been around as a discipline for about 15 years now, there is still a lot of road to be covered for many organizations and for the discipline as a whole.

Some of the topics I find to be the most promising visit points on this journey are:

  • Cloud deployment of MDM solutions
  • Inclusion of Artificial Intelligence (AI) and Machine Learning (ML) in MDM
  • Multienterprise (aka ecosystem wide) MDM
  • MDM and the Internet of Things (IoT)
  • Extended MDM platforms

cloud-mdm

Cloud deployment of MDM has increased slowly but steadily over the recent years. According to Gartner the share of cloud-based MDM deployment has increased from 19% in 2017 year to 24 % in 2018 and I am sure that number will increase again this year. Quite naturally the implementation of MDM in the cloud will follow the general adoption of cloud solutions deployed in each organization as master data is the glue between the data held in each application.

Doing MDM in the cloud or not is, as with most things in life, not a simple question with a yes or no answer, as there are different deployment styles as examined in the post MDM, Cloud, SaaS, PaaS, IaaS and DaaS.

AI icon

Inclusion of Artificial Intelligence (AI) and Machine Learning (ML) in the MDM discipline will, in my eyes, be one of the hottest topics in the years to come. MDM is not the easiest IT enabled discipline in which AI and ML can be applied. Handling master data has many manual processes today because it is highly interactive, and the needed day-to-day decisions requires much knowledge input. But we will get there step by step and we must start now as told in the post It is time to apply AI to MDM and PIM.

There is an interdependency between MDM and Artificial Intelligence (AI). AI and Machine Learning (ML) depends on data quality, that is sustained with MDM, as examined in the post Machine Learning, Artificial Intelligence and Data Quality. And you can use AI and ML to solve MDM issues as told in the post Six MDM, AI and ML Use Cases.

Master Data Share

Multienterprise MDM is emerging as a necessity following the rise of digitalization. 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. This theme was pondered in the post Why Multienterprise MDM will Underpin Digital Transformation.

The scope of MDM will increase with the rise of Internet of Things (IoT) as reported in the post IoT and MDM.

IoT

The Multienterprise MDM trend and the IoT trend will go hand in hand as handling asset master data involved in IoT embraces many parties involved included manufacturers of smart devices, operators of these devices, maintainers of the devices, owners of the devices and the data subjects these devices gather data about.

Probably we will see the highest maturity for that first in Industrial Internet of Things (IIoT), also referred to as Industry 4.0, as pondered in the post IIoT (or Industry 4.0) Will Mature Before IoT.

Master Data and Other DataExtended MDM platforms are emerging, as there is a tendency to, that solutions providers on the Master Data Management (MDM) market aim to deliver an extended MDM platform to underpin customer experience efforts and encompass all kinds of data governance. Such a platform will not only handle traditional master data, but also reference data, as well as linking to big data (as in data lakes) and transactions. This trend was examined in the post Maturing RDM, MDM and ADM With Collaborative Data Governance.

Welcome EntityWise on The Disruptive MDM / PIM / DQM / List

EntityWiseThere is yet a new entry on the Disruptive MDM / PIM /DQM List.

EntityWise is a data matching solution specializing in the healthcare sector. At EntityWise they use machine learning and artificial intelligence (AI) based technology to overcome the burden of inspecting suspect duplicates.

As such EntityWise is a good example of the long tail of Data Quality Management (DQM) solutions that provides a good return of investment at organizations with specific data quality issues.

Learn more about EntityWise here.