The title of this blog post is also the title of a webinar I will be presenting on the 28th February 2019. The webinar is hosted by the visionary Multidomain MDM and PIM solution provider Riversand.
Customer experience (CX) and Master Data Management (MDM) must go hand in hand. Both themes involve multiple business units and digital environments within your enterprise and in the wider business ecosystem, where your enterprise operates. Master data is the glue that brings the data you hold about your customers together as well as the glue that combines the data you share about your product offering together.
To be successful within customer experience in the digital era you need classic master data outcomes as a 360-degree view of customers as well as complete and consistent product information. In other words, you need to maintain Golden Records in Multidomain MDM.
You also need to combine your customer data and your product data to get to the right level of personalization. Knowing about your customer, what he/she wants, and their buying behaviour is one side personalization. The other side is being able to match these data with relevant products that is described to a level that can provide reasonable logic against the behavioural data.
Furthermore, you need to be able to make sense of internal and external big data sources and relate those to your prospective and existing customers and the products they have an interest in. This quest stretches the boundaries of traditional MDM towards being a more generic data platform.
When working with data management – and not at least listening to and reading stuff about data management – there is in my experience too little work with the actual data going around out there.
I know this from my own work. Most often presentations, studies and other decision support in the data management realm is based on random anecdotes about the data rather than looking at the data. And don’t get me wrong. I know that data must be seen as information in context, that the processes around data is crucial, that the people working with data is key to achieving better data quality and much more cleverness not about the data as is.
But time and again I always realize that you get the best understanding about the data when getting your hands dirty with working with the data from various organizations. For me that have been when doing a deduplication of party master data, when calibrating a data matching engine for party master data against third party reference data, when grouping and linking product information held by trading partners, when relating other master data to location reference data and all these activities we do in order to raise data quality and get a grip on Master Data Management (MDM) and Product Information Management (PIM).
Well, perhaps it is just me and because I never liked real dirt and gardening.
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) in MDM and multienterprise MDM.
Cloud deployment of MDM has increased slowly but steadily over the recent years. 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.
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 Share or be left out of business.
Ultima Thule is a name for a distant place beyond the known world and the nickname of the most distant object in the solar system closely observed by a man-made object today the 1st January 2019. Before the flyby scientists were unsure if it was two objects, a peanut formed object or another shape. The images probing what it is will be downloaded during the next couple of months.
A while ago the trend of having the possibility to deploy a Master Data Management (MDM) solution in the cloud was covered in the post The Rise of Cloud MDM.
The latest Gartner MDM Magic Quadrant report has some numbers on that trend as mentioned in the post Who Will Make the Next Disruption on the MDM Market? Cloud based deployment has increased from 19% in 2017 year to 24 % in 2018 among Gartner’s respondents. While the organizations included here are the larger ones, I will guestimate that the cloud portion of MDM implementations are higher among midsize and smaller organizations.
As mentioned in the Gartner report there are however some confusion about what a cloud MDM solution really is. Does it come as SaaS (Software as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service)? In this spectrum the vendor will provide most things in a SaaS solution, lesser stuff as PaaS and only the ability for the software to be hosted somewhere out there as IaaS.
One “as a Service” component in relation to master data you could expect in SaaS, but not necessarily in IaaS, is DaaS (Data as a Service) as for example out-of-the-box address verification and business directory integration services. A common address verification service is the one from Loqate, while Informatica though have their own solution based on their AddressDoctor acquisition. The most common business directory provider is Dun & Bradstreet.
Else the difference follows the general difference between SaaS, PaaS and IaaS which is about what the organization has do themselves (or through system integrators) around software updates, configuration, maintenance, monitoring and more.
On the brink to 2019 my guess is that we will see more MDM in the cloud next year as well as a movement from IaaS over PaaS to SaaS. This will include more DaaS covering more master data domains not at least in the product data space – a reason of being for the Product Data Lake service I am involved with.
There are many market reports covering the Master Data Management (MDM) and Product Information Management (PIM) market. Below you can find 4 of these coming from who is usually considered as the more reliable analyst houses around:
In a comment to this post Nadim observes that this Gartner quadrant is mixing up pure MDM players and PIM players.
That is true. It has always been a discussion point if one should combine or separate solutions for Master Data Management (MDM) and Product Information Management (PIM). This is a question to be asked by end user organizations and it is certainly a question the vendors on the market(s) ask themselves.
If we look at the vendors included in the 2018 Magic Quadrant the PIM part is represented in some different ways.
I would say that two of the newcomers, Viamedici and Contentserv (yellow dots in below figure), are mostly PIM players today. This is also mentioned as a caution by Gartner and is a reason for the current left-bottom’ish placement in the quadrant. But both companies want to be more multidomain MDM’ish.
8 years ago, I was engaged at Stibo Systems as part of their first steps on the route from PIM to multidomain MDM. Enterworks and Riversand (the orange dots in above figure) is on the same road.
Informatica has taken a different path towards the same destination as they back in 2012 bought the PIM player Heiler. Gartner has some cautions about how well the MDM and PIM components makes up a whole in the Informatica offerings and similar cautions was expressed around the Forrester PIM Wave as seen in the comments to the post There is no PIM quadrant, but there is a PIM wave.
The Master Data Management (MDM) discipline is something that belongs in the backbone of digitalization and enterprise architecture and therefore new ways of doing things always have a hard time in this realm. Fore sure there have been talk about big data and MDM for years, but actual implementations are few compared to ongoing traditional system of record implementations. The same will be the case with Artificial Intelligence (AI) and MDM. We will still see a lot of clerking around MDM for years.
So, I am stretching it far when working with yet a new must do thing for MDM (besides working with MDM, big data and AI).
But I have no doubt about that shareconomy (or sharing economy) will affect the way we work with MDM in the future. A few others are on the same path as for example the Swiss consultancy CDQ as presented on their page about Shareconomy for Customer and Supplier Data and The Corporate Data League (CDL).
Doing Master Data Management (MDM) enterprise wide is hard enough. The ability to control master data across your organization is essential to enable digitalization initiatives and ensure the competitiveness of your organization in the future.
But it does not stop there. Increasingly every organization will be an integrated part of a business ecosystem where collaboration with business partners and through market places will be a part of digitalization and thus, we will have a need for working on the same foundation around master data.
This new aspect of MDM is also called multienterprise MDM. It will take years to be widespread. But you better start thinking about how this will be a part of your MDM strategy. Because in the long run you must Share or be left out of business.
If Gartner is still postponing this year’s MDM quadrant, they may even manage to reflect this change. We are of course also waiting to see if newcomers will make it to the quadrant and make the crowd of vendors in there go back to an above 10 number. Some of the candidates will be likes of Reltio and Semarchy.
Else, back to the takeover of Orchestra by Tibco, this is not the first time Tibco buys something in the MDM and Data Quality realm. Back in 2010 Tibco bought the data quality tool and data matching front runner Netrics as reported in the post What is a best-in-class match engine?
Then Tibco didn’t defend Netrics’ position in the Gartner Magic Quadrant for Data Quality Tools. The latest Data Quality Tool quadrant is also as the MDM quadrant from 2017 and was touched on this blog here.
So, will be exciting to see how Tibco will defend the joint Tibco MDM solution, which in 2017 was a sliding niche player at Gartner, and the Orchestra MDM solution, which in 2017 was a leader at the Gartner MDM quadrant.