Mapping MDM and PIM Solutions

There are several parameters considered by organizations on the look for solutions that handles Master Data Management (MDM) and Product Information Management (PIM) or both. One is how MDM’ish or PIM’ish the solution is as examined in the post MDM, PIM or Both.

Another aspect is the geographical presence. This includes where the solution provider is based and of course also the presence around the world through local offices and partner network.

Here are some of the solution providers from North America and Europe on a map:

MDM World Map

Reltio is a Silicon Valley based MDM provider. Learn more about Reltio Cloud here.

Semarchy has moved their head quarter to Silicon Valley but has their origin and most of the operation still in Lyon, France. Learn more about Semarchy xDM here.

Riversand is coming out of Houston, Texas. Learn more about Riversand here.

EnterWorks is based in Sterling, Virginia. Learn about Enterworks here.

CONTENTSERV is head quartered in Baar, Switzerland. Learn more about CONTENTSERV here.

SyncForce is located in Eindhoven, Netherlands. Learn about SyncForce here.

Dynamicweb PIM is from Aarhus, Denmark. Learn more about Dynamicweb PIM here.

Informatica is another Silicon Valley firm. Informatica has bought firms from around the world as lately Toronto, Canada based AllSight, now branded as Informatica Customer 360 Insights. Learn more about Informatica Customer 360 Insights here.

Magnitude and Agility® are now married. They are respectively located in Austin, Texas and York, UK. Learn more about Magnitude MDM here and learn more about Agility here.

Where is your (preferred) MDM / PIM solution located? – and what is the world reach?

MDM, PIM, CX and User Types

Customer Experience (CX) is a trendy driver for Master Data Management (MDM) and Product Information Management (PIM).

When talking about CX we may have to distinguish between 3 main kind of user types:

  • Consumers (in households)
  • Small Office, Home Office (SOHO) users
  • Corporate users

CX user types

The critical differences between pleasing consumers in B2C and pleasing business users in B2B was discussed in the post B2C vs B2B in Product Information Management. A crucial distinction is the use of data as told in the post Where to Buy a Magic Wand?

Business users can be divided into those in small self-owned business’s as craftsmen, farmers, small shop owners, freelance consultants and many more and then corporate users who buys on behalf of a legal entity typically within a team of users.

There are intersections of customer experience preference patterns between these groups and then we are all humans regardless of our role in time. Earlier this year I presented a webinar, hosted by Riversand, on this topic. Find the link and the introduction in the post The relation between CX and MDM.

IoT and Business Ecosystem Wide MDM

Two of the disruptive trends in Master Data Management (MDM) are the intersection of Internet of Things (IoT) and MDM and business ecosystem wide MDM (aka multienterprise MDM).

These two trends will go hand in hand.

IoT and Ecosystem Wide MDM

The latest MDM market report from Forrester (the other analyst firm) was mentioned in the post Toward the Third Generation of MDM.

In here Forrester says: “As first-generation MDM technologies become outdated and less effective, improved second generation and third-generation features will dictate which providers lead the pack. Vendors that can provide internet-of-things (IoT) capabilities, ecosystem capabilities, and data context position themselves to successfully deliver added business value to their customers.”

This saying is close to me in my current job as co-founder and CTO at Product Data Lake as told in the post Adding Things to Product Data Lake.

In business ecosystem wide MDM business partners collaborate around master data. This is a prerequisite for handling asset master data involved in IoT as there are 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.

In the same way forward looking solution providers involved with MDM must collaborate as pondered in the post Linked Product Data Quality.

CDP: Is that part of CRM or MDM?

The notion of a data centred application type called a Customer Data Platform (CDP) seems to be trending these days. A CDP solution is a centralized registry of all data related to parties regarded as (prospective) customers at an enterprise.

This kind of solution comes from two solution markets:

  • Customer Relationship Management (CRM)
  • Master Data Management (MDM)

The CRM track was recently covered in a Venture Beat article telling that Salesforce announces a Customer Data Platform to unify all marketing data. In this article it is also stated that Oracle just announced a similar solution named CX Unity and Adobe announced triggered journeys based on a rich pool of centralized data.

Add to that last year´s announcement from Microsoft, Adobe and SAP on their Open Data Initiative as told in the LinkedIn article Using a Data Lake for Data Sharing.

Some MDM solution providers are also on that track. Reltio Cloud embraces all customer data and Informatica Customer 360 Insights, formerly known as Allsight, is also going there as reported in the post Extended MDM Platforms.

Will be interesting to follow how CDP solutions evolve and if it is CRM or MDM vendors who will do best in this discipline. One guess could be that MDM vendors will provide “the best” solutions but CRM vendors will sell most licenses. We will see.

CDP CRM MDM

The Future of Disruptive MDM is in the Cloud

Two recent posts on the Gartner blog is about databases in the cloud. The Future of Database Management Systems Is Cloud by Merv Adrian ponders why cloud is now the default platform for managing data and The Future of Database Management Systems Is Cloud by Donald Feinberg does the same. Well, the two posts are identical.

This will also mean that the default platform for Master Data Management (MDM) will be in the cloud. Add to that, that the other disruptive MDM trends also will work best in the cloud.

Disruptive MDM in the Cloud

  • We increasingly see Extended MDM Platforms that also handles reference data and big data. Both these data types are predominantly external in nature and therefore they are better collected, or even better connected, in the cloud.
  • Services for Artificial Intelligence (AI) and Master Data Management (MDM) is delivered by vendors as cloud solutions.
  • Encompassing IoT and MDM means collaboration between many parties and this is, with all the relationships to take care of, only possible with cloud platforms.
  • We will see several other use cases for business ecosystem wide cross company sharing of master data in what Gartner coins as Multienterprise MDM.

MDM Trend: Data as a Service

A recent post on this blog was called Five Disruptive MDM Trends. One of the trends mentioned herein is MDM in the cloud and one form of Master Data Management in the cloud in the picture is Data as a Service (DaaS).

DaaS within MDM

Using Data as a Service in the cloud within MDM solutions is a great way of ensuring data quality. You have access to real-time validation and enrichment of master data and you can also use third party and second party services in the on-boarding processes and then avoid typing in data with the unavoidable human errors that else is the most common root cause of data quality issues.

Some of the most common data services useful in MDM are:

Address Verification and Geocoding

When handling location data having a valid and standardized description of postal addresses and in many cases also a code that tells about the geographic position is crucial in MDM.

Postal address verification can either be exploited by a global service such as Loqate from GB Group or AddressDoctor, which is part of the Informatica offering. Alternatively, you can use national services that are better (but also narrowly) aligned with a given address format within a country and the specific extra services available in some countries.

Geocodes can either by latitude and longitude or flat map friendly geocoding systems such as UTM coordinates or WGS84 coordinates.

Business Directory Services

When handling party master data as B2B customers, suppliers and other business partners in is useful to validate and enrich the data with third party reference data and in some cases even onboard through these sources.

Again, there are global and local options. The most commonly used global is Dun & Bradstreet, who operates a database called WorldBase that holds business entities from all over the world in a uniform format and also provides data about the company family trees on a global basis. Alternatively, many countries have a national service provided by each government with formats and data elements specific to that country.

Citizen Directory Services

When handling party master data as B2C customers, employees and other personal data the third-party possibilities are sparser in general, naturally because of privacy concerns.

In Scandinavia, where I live, these data are available from public sources based on either our national ID or a correct name and address.

Data pools and Product Data Lake

When handling product master data and product information there are for some product groups and product attributes in some geographies data pools available. The most commonly used global service is GDSN from GS1.

Alternatively (or supplementary), for all other product groups, product attributes and digital assets and in all other geographies, you can use a service like the one I am working with and is called Product Data Lake.

Connecting Silos

The building next to my home office was originally two cement silos standing in an industrial harbor area among other silos. These two silos are now transformed into a connected office building as this area has been developed into a modern residence and commercial quarter.

Master Data Management (MDM) is on similar route.

The first quest for MDM has been to be a core discipline in transforming siloed data stores within a given company into a shared view of the core entities that must be described in the same way across different departmental views. Going from the departmental stage to the enterprise wide stage is examined in the post Three Stages of MDM Maturity.

But as told in this post, it does not stop there. The next transformation is to provide a shared view with trading partners in the business ecosystem(s) where your company operates. Because the shared data in your organization is also a silo when digital transformation puts pressure on each company to become a data integrated part of a business ecosystem.

A concept for doing that is described on the blog page called Master Data Share.

Silos
Connected silos in Copenhagen North Harbor – and connecting data silos enterprise wide and then business ecosystem wide

The Intelligent Enterprise of the Future, Informatica Style

Yesterday I had the pleasure of attending the Informatica MDM 360 and Data Governance Summit in London including being in a panel discussing best practices for your MDM 360 journey. The rise of Artificial Intelligence (AI) in Master Data Management (MDM) was a main theme at this event.

Informatica has a track record of innovating in new technologies in the data management space while also acquiring promising newcomers in order to fast track their market offering. So it is with AI and MDM at Informatica too. Informatica currently has two tracks:

  • clAIre – the clairvoyant component in the Informatica portfolio that “using machine learning and other AI techniques leverages the industry-leading metadata capabilities of the Informatica Intelligent Data Platform to accelerate and automate core data management and governance processes”.
  • Informatica Customer 360 Insights which is the new branding of the recent AllSight acquisition. You can learn about that over at The Disruptive Master Data Management Solutions List in the entry about Informatica Customer 360 Insights.

At the Informatica event the synergy between these two tracks was presented as the Intelligent 360 View. Naturally, marketing synergies are the first results of an acquisition. Later we will – hopefully – see actual synergies when the technologies are to be aligned, positioned and delivered to customers who want to be an intelligent enterprise of the future.

Infa Intelligent Enterprise of the Future

Five Disruptive MDM Trends

As any other IT enabled discipline Master Data Management (MDM) continuously undergo a transformation while adopting emerging technologies. In the following I will focus on five trends that seen today seems to be disruptive:

Disruptive MDM

MDM in the Cloud

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. But does it come as SaaS (Software as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service)? And what about DaaS (Data as a Service). Learn more in the post MDM, Cloud, SaaS, PaaS, IaaS and DaaS.

Extended MDM Platforms

There is a tendency on the Master Data Management (MDM) market that solutions providers aim to deliver an extended MDM platform to underpin customer experience efforts. Such a platform will not only handle traditional master data, but also reference data, big data (as in data lakes) as well as linking to transactions. Learn more in the post Extended MDM Platforms.

AI and MDM

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.

IoT and MDM

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

Ecosystem wide MDM

Doing Master Data Management (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. Learn more in the post Multienterprise MDM.

Six MDM, AI and ML Use Cases

One of the hottest trends in the Master Data Management (MDM) world today is how to exploit Artificial Intelligence (AI) and ignite that with Machine Learning (ML).

This aspiration is not new. It has been something that have been going on for years and you may argue about when computerized decision support and automation goes from being applying advanced algorithms to being AI. However, the AI and ML theme is getting traction today as part of digital transformation and whatever we call it, there are substantial business outcomes to pursue.

As told in the post Machine Learning, Artificial Intelligence and Data Quality perhaps all use cases for applying AI is dependent on data quality and MDM is playing a crucial role in sustaining data quality efforts.

Some of the use cases for AI and ML in the MDM realm I have come across over the years are:

6 MDM, AI and ML use cases

Translating between taxonomies: As reported in the post Artificial Intelligence (AI) and Multienterprise MDM emerging technologies can help in translating between the taxonomies in use when digital transformation sets a new bar for utilizing master data in business ecosystems.

Transforming unstructured to structured: A lot of data is kept in an unstructured way and to in order to systematically exploit these data in AI supported business process we need make data more structured. AI and ML can help with that too.

Data quality issue prevention: Simple rules for checking integrity and validating data is good – but unfortunately not good enough for ensuring data quality. AI is a way to exploit statistical methods and complex relationships.

Categorizing data: Digital transformation, spiced up with increasing compliance requirements, has made data categorization a must and AI and ML can be an effective way to solve this task that usually is not possible for humans to cover across an enterprise.

Data matching: Establishing a link between multiple descriptions of the same real-world entity across an enterprise and out to third party reference data has always been a pain. AI and ML can help as examined in the post The Art in Data Matching.

Improving insight: The scope of MDM can be enlarged to Extended MDM Platforms where other data as transactions and big data is used to build a 360-degree of the master data entities. AI and ML is a prerequisite to do that.