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 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.
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.
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.
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.
Extended 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.
Liliendahl on Data Quality 