Master Data Management (MDM) and Product Information Management (PIM) solutions are, as many other enabling technologies, often purchased using a formal Request for Proposal (RFP) process.
In such a process the theory is that the buying organization, often with the help from an external consultancy, states the functional and non-functional requirements, measure the solutions against these requirements using a weighted score model and then objectively selects the solution with the highest score.
In practice there are in my experience some more subjective and soft sides to this process. This is not at least the case in the longlist/shortlist phase and when taking the final decision. I have seen my share of overruling the scoring.
One aspect is the geographical presence of the vendor. This includes where the solution provider is based and of course also the presence around the world through local offices and partner network as told in the post Mapping MDM and PIM Solutions.
Another aspect is the subject matter expertise shown by the vendor. This includes written material provided but also available on website and blogs and of course during presentations. An example could be the emphasis on master data versus product information as exemplified in the post MDM, PIM or Both.
I have had the fortunate opportunity of being at both sides of the table during the years and are still doing that as shown in the article about Popular Offerings.
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:
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.
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.”
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.
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:
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.
Looking back at the first blog posts I think the themes touched are still valid.
The first post from June 2009 was about data architecture. 2000 years ago, the roman writer, architect and engineer Marcus Vitruvius Pollio wrote that a structure must exhibit the three qualities of firmitas, utilitas, venustas — that is, it must be strong or durable, useful, and beautiful. This is true today – both in architecture and data architecture – as told in the post Qualities in Data Architecture.
A recurring topic on this blog has been a discussion around the common definition of data quality as being that the data is fit for the intended purpose of use. The opening of this topic as made in the post Fit for what purpose?
Diversity in data quality has been another repeating topic. Several old tales including in the Genesis and the Qur’an have stories about a great tower built by mankind at a time with a single language of all people. Since then mankind was confused by having multiple languages. And indeed, we still are as pondered in the post The Tower of Babel.
Thanks to all who are reading this blog and not least to all who from time to time takes time to make a comment, like and share.
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.
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.
We also see statistics showing a development towards melting ice masses with rising sea levels as the foreseeable result. However, statistics can always be questioned. Is the ice thickening somewhere else? Has this happened many times before?
These kind of questions shows the layers we must go through getting from data quality to information quality, then decision quality and on top the wisdom in applying the right knowledge whether that is to achieve business outcomes or avoiding climate change.
Reference Data Management (RDM) is a small but important extension to Master Data Management (MDM). Together with a large extension, being big data and data lakes, mastering reference data is increasingly being part of the offerings from MDM solution vendors as told in the post Extended MDM Platforms.
Reference data are these smaller lists of values that gives context to master data and ensures that we use the same (or linkable) codes for describing master data entities. Examples are:
Reference data tend to be externally defined and maintained typically by international standardization bodies or industry organizations, but reference data can also be internally defined to meet your specific business model.
3 RDM Solutions from MDM Vendors
Informatica has recently released their first version of a new RDM solution: MDM – Reference 360. This is by the way the first true Software as a Service (SaaS) solution from Informatica in the MDM space. This solution emphasizes on building a hierarchy of reference data lists, the ability to make crosswalks between the lists, workflow (approval) around updates and audit trails.
Reltio has embraced RDM has an integral part of their Reltio Cloud solution where the “RDM capabilities improves data governance and operational excellence with an easy to use application that creates, manages and provisions reference data for better reporting and analytics.”
Semarchy has a solution called Semarchy xDM. The x indicates that this solution encompasses all kinds of enterprise grade data and thus both Master data and Reference data while “xDM extends the agile development concept to its implementation paradigm”.