Where is ADM in your MDM Roadmap?

The three-letter acronym ADM stands, in a data management context, for Application Data Management.

Well, besides from that ADM is part of the word roADMap I see more and more signs of that the line between master data and other application data is blurring and Application Data Management will be part of the MDM roadmaps around.

The difference – and the intersection – between Master Data Management (MDM) and Application Data Management was examined here on the blog some time ago in the post called MDM vs ADM.

The pros and cons of seeing master data as something separate from any other data was also discussed in the post Master Data or Shared Data or Critical Data or What?

As also put forward then, I think it is useful to look at the data within the whole Enterprise Information Management (EIM) theme in lens of what is specific to your enterprise and what you have in common with other enterprises. Master data will typically be the data you share – or could share – with other enterprises, not at least your business partners.

In what degree do you find it useful to separate master data and other data in a MDM and/or ADM roadmap?

ADM MDM

Multi Enterprise MDM

The title of this post is also the title of my presentation at the Master Data Management Summit Europe 2019. This conference is co-located with the Data Governance Conference Europe 2019.

The session will go through these topics:

  • Why business ecosystem wide MDM will be on the future agenda as elaborated in a post on this blog. The post is called Ecosystem Wide MDM.
  • What exactly is multienterprise MDM as examined in a post on The Disruptive Master Data Management Solutions List.
  • How does it apply to party master data and what about data privacy and data protection?
  • How can multienterprise MDM be used within product MDM and what is the link to IoT (Internet of Things).
  • Learn from a concrete use case encompassing product information and AI (Artificial Intelligence) as mentioned in the post It is time to apply AI to MDM and PIM.

You can have a look at the entire agenda at the MDMDG Summit Europe 2019 here.

MDM Ecosystem

MDM Use Case Status

As reported in the post Counting MDM Licenses there is movement in the MDM landscape when it comes to the offerings for the various use cases we have been working with the last 15 years and those we will be working with in the future.

Borrowing from the Gartner lingo, we can sketch the MDM use case overview this way:

  • Party MDM, meaning handling master data about persons and companies interacting with your company. Their role may be as employee, partner, supplier/vendor and customer. With the customer role we can make a distinction between:
    • MDM of B2C (Business-to-Consumer) customer data, meaning handling master data about persons in their private roles as consumers, citizens, patients, students and more. This may also cover how persons are part of a household.
    • MDM of B2B (Business-to-Business) customer data, meaning handling master data about organizations with a customer role in your company. This may also cover the hierarchy these organizations form (typically company family trees) and the persons who are your contacts at these organizations.
  • Product MDM, meaning handling data about product models and their item variants as well as each instance of a product as an asset. This can be divided into:
    • MDM of buy-side product data covering the procurement and Supply Chain Management (SCM) view of products going into your company from suppliers.
    • MDM of sell-side product data covering the sales and marketing view of products being sold directly to customers or through partners.
  • Multidomain MDM being combining product and party master data possibly with other domains as locations, general ledger accounts and specific master data domains in your industry.
  • Multivector MDM being a special Gartner term meaning use case split into multiple domains (as mentioned above), multiple industries, operational/analytical usage scenarios, organizational structures and implementation styles (registry, consolidation, coexistence, centralized).
  • Multienterprise MDM being handling master data in collaboration with your business partners as told in this post about Multienterprise MDM.

In the latest Gartner MDM quadrant, the status of the use cases is:

  • Customer MDM and Product MDM continue to climb the Slope of Enlightenment toward the Plateau of Productivity in Gartner’s Hype Cycle for Information Governance and Master Data Management.
  • Multidomain MDM solutions are sliding toward the bottom of the Trough of Disillusionment, while Multivector MDM solutions continue their climb toward the Peak of Inflated Expectations in the Hype Cycle.
  • Multienterprise MDM is near the Peak of Inflated Expectations on the Hype Cycle as well as mentioned in the post MDM Hype Cycle, GDSN, Data Quality, Multienterprise MDM and Product Data Syndication. In the Gartner MDM quadrant 2018 multienterprise MDM was only mentioned as a strength at one of the included vendors – Enterworks.

Stay tuned on this blog for more news about Multienterprise MDM as how it looks like from standing on the peak.

mdm hype cycle
Based on Gartner sources

1,000 Blog Posts and More to Come

number_1000I just realized that this post will be number 1,000 published on this blog. So, let me not say something new but just recap a little bit on what it has been all about in the last nearly 10 years of running a blog on some nerdy stuff.

Data quality has been the main theme. When writing about data quality one will not avoid touching Master Data Management (MDM). In fact, the most applied category used here on this site, with 464 and counting entries, is Master Data.

The second most applied category on this blog is, with 219 entries, Data Architecture.

The most applied data quality activity around is data matching. As this is also where I started my data quality venture, there has been 192 posts about Data Matching.

The newest category relates to Product Information Management (PIM) and is, with 20 posts at the moment, about Product Data Syndication.

Even though that data quality is a serious subject, you must not forget to have fun. 66 posts, including a yearly April Fools post, has been categorized as Supposed to be a Joke.

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.

Counting MDM Licenses

The Gartner Magic Quadrant for Master Data Management (MDM) Solutions 2018 was published last month.

Some of the numbers in the market that were revealed in the report was the number and distribution of MDM licenses from the included vendors. These covered their top-three master data domains and estimated license counts as well as the number of customers managing multiple domains:

mdm licenses

One should of course be aware of the data quality issues related to comparing these numbers, as they in some degree are estimates based on different perceptions at the included vendors. So, let me just highlight these observations:

  • The overall number of MDM licenses and unique MDM customers (at the included vendors) is not high. Under 10,000 organizations world-wide is running such a solution. The potential new market out there for the salesforce at the MDM vendors is huge.
  • If you find an existing MDM solution user organization, they probably have a solution from SAP or Informatica – or maybe IBM. To be complete, Oracle has been dropped from the MDM quadrant, they practically do not promote their MDM solutions anymore, but there are still existing solutions operating out there.
  • The reign of Customer MDM is over. Product MDM is selling and multidomain is becoming the norm. Several MDM vendors are making their way into the quadrant from a Product Information Management (PIM) base as reported in the post The Road from PIM to Multidomain MDM.

PS: If you, as an end customer organization or a MDM and PIM vendor, want to work with me on the consequences for MDM solutions, here are some Popular Offerings for you.

The relation between CX and MDM

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.

You can register to join the webinar here.

webinar data lake

Get Your Hands Dirty with Data

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.

data management wordsBut 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.

The Road Ahead for MDM

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.

vestre kirkegaardSome 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.

Flying by Ultima Thule and Data Management

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.

You can make many analogies between exploring space and data management. On this blog the journey has passed the similarity between Neutron Star Collision and Data Quality. The Gravitational Waves in the MDM World has been observed and so has the Gravitational Collapse in the PIM Space. The notion of A Product Information Management (PIM) Solar System has also been suggested.

Happy New Year and wishing you all well in the data management journey beyond Ultima Thule.

Ultima Thule
Source: Nasa via BBC

MDM, Cloud, SaaS, PaaS, IaaS and DaaS

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.

Cloud MDM