Ecosystems are The Future of Digital and MDM

A recent blog post by Dan Bieler of Forrester ponders that you should Power Your Digital Ecosystems with Business Platforms.

In his post, Dan Bieler explains that such business platforms support:

·      The infrastructure that connect ecosystem participants. Business platforms help organizations transform from local and linear ways of doing business toward virtual and exponential operations.

·      A single source of truth for ecosystem participants. Business platforms become a single source of truth for ecosystems by providing all ecosystem participants with access to the same data.

·      Business model and process transformation across industries. Platforms support agile reconfiguration of business models and processes through information exchange inside and between ecosystems.

A single source of truth (or trust) for ecosystem participants is something that rings a bell for every Master Data Management (MDM) practitioner. The news is that the single source will not be a single source within a given enterprise, but a single source that encompasses the business ecosystem of trading partners.

Gartner Digital Platforms.png

Gartner, the other analyst firm, has also recently been advocating about digital platforms where the ecosystem type is the top right one. As stated by Gartner: Ecosystems are the future of digital.

I certainly agree. This is why all of you should get involved at Master Data Share.

 

I am afraid that Gartner does not help

“The average financial impact of poor data quality on organizations is $9.7 million per year.” This is a quote from Gartner, the analyst firm, used by them to promote their services in building a business case for data quality.

AverageWhile this quote rightfully emphasizes on that a lot of money is at stake, the quote itself holds a full load of data and information quality issues.

On the pedantic side, the use of the $ sign in international communication is problematic. The $ sign represents a lot of different currencies as CAD, AUD, HKD and of course also USD.

Then it is unclear on what basis this average is measured. Is it among the +200 million organizations in the Dun & Bradstreet Worldbase? Is it among organizations on a certain fortune list? In what year?

Even if you knew that this is an average in a given year for the likes of your organization, such an average would not help you justify allocation of resources for a data quality improvement quest in your organization.

I know the methodology provided by Gartner actually is designed to help you with specific return on investment for your organization. I also know from being involved in several business cases for data quality (as well as Master Data Management and data governance) that accurately stating how any one element of your data may affect your business is fiendishly difficult.

I am afraid that there is no magic around as told in the post Miracle Food for Thought.

Infonomics and Second Party Data

The term infonomics does not yet run unmarked through my English spellchecker, but there are some information available on Wikipedia about infonomics. Infonomics is closely related to the often-mentioned phrases in data management about seeing data / information as an asset.

Much of what I have read about infonomics and seeing data / information as an asset is related to what we call first party data. That is data that is stored and managed within your own company.

Some information is also available in relation to third party data. That is data we buy from external parties in order to validate, enrich or even replace our own first party data. An example is a recent paper from among others infonomic guru Doug Laney of Gartner (the analyst firm). This paper has a high value if you want to buy it as seen here.

Anyway, the relationship between data as an asset and the value of data is obvious when it comes to third party data, as we pay a given amount of money for data when acquiring third party data.

Second party data is data we exchange with our trading and other business partners. One example that has been close to me during the recent years is product information that follows exchange of goods in cross company supply chains. Here the value of the goods is increasingly depending on the quality (completeness and other data quality dimensions) of the product information that follows the goods.

In my eyes, we will see an increasing focus on infonomics when it comes to exchanging goods – and the related second party data – in the future. Two basic factors will be:

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Who will become Future Leaders in the Gartner Multidomain MDM Magic Quadrant?

Gartner emphasizes that the new Magic Quadrant for Master Data Management Solutions Published 19 January 2017 is not solely about multidomain MDM or a consolidation of the two retired MDM quadrants for customer and product master data. However, a long way down the report it still is.

If you want a free copy both Informatica here and Riversand here offers that.

The Current Pole Position and the Pack

The possible positioning was the subject in a post here on the blog some while ago. This post was called The Gartner Magic Quadrant for MDM 2016. The term 2016 has though been omitted in the title of the final quadrant probably because it took into 2017 to finalize the report as reported in the post Gartner MDM Magic Quadrant in Overtime.

Below is my look at the positioning in the current quadrant:

mdm-mq

Starting with the multidomain MDM point the two current leaders, Informatica and Orchestra, have made their way to multidomain in two different ways. Pole position vendor Informatica has used mergers and acquisitions with the old Siperian MDM solution and the Heiler PIM (Product Information Management) solution to build the multidomain MDM leadership. Orchestra Networks has built a multidomain MDM solution from the gound.

The visionary Riversand is coming in from the Product MDM / PIM world as a multidomain MDM wannabe and so is the challenger Stibo. I think SAP is in their right place: Enormous ability to execute with not so much vision.

If you go through the strengths and cautions of the various vendors, you will find a lot of multidomain MDM views from Gartner.

The Future Race

While the edges of the challengers and visionaries’ quadrants are usually empty in a Gartner magic quadrant, the top right in this first multidomain MDM quadrant from Gartner is noticeably empty too. So who will we see there in the future?

Gartner mentions some interesting upcoming vendors earning too little yet. Examples are Agility Multichannel (a Product Data Lake ambassador by the way), Semarchy and Reltio.

The future race track will according to Gartner go through:

  • MDM and the Cloud
  • MDM and the Internet of Things
  • MDM and Big Data

PS: At Product Data Lake we are heading there in full speed too. Therefore, it will be a win-win to see more MDM vendors joining as ambassadors or even being more involved.

MDM: The Technology Trends

There are certainly many things going on in the Master Data Management (MDM) realm when it comes to technologies applied.

The move from on premise based solutions to cloud based solutions has been visible for some years. It is not a rush yet, but we see more and more master data services being offered as cloud services as well as many vendors of full stack MDM platforms offers both on premise, cloud and even hybrid solutions.

As reported in the post Emerging Database Technologies for Master Data new underlying database technologies are put in place instead of the relational database solutions that until now have ruled the MDM world. As mentioned graph databases as Neo4J and document databases as MongoDB (which now also support graph) are examples of new popular choices.

blockchainAs examined by Gartner (the analyst Firm) there are Two Ways of Exploiting Big Data with MDM, either doing it directly or by linking. Anyway, the ties between big data and master data management is in my eyes going to be a main focus for the technology trends in the years to come. Other important ties includes the raise of Industry 4.0 / Internet of Things and blockchain approaches.

We are still waiting for The Gartner Magic Quadrant for Master Data Management Solutions 2016 and the related Critical Capabilities document, so it will be very exciting, in fact more exciting that the vendor positioning, to learn about how Gartner sees the technology trends affecting the MDM landscape.

What are your expectations about Master Data Management and new emerging technologies?

Gartner MDM Magic Quadrant in Overtime

The Gartner Master Data Management Solutions Magic Quadrant 2016 did not go live in 2016. Estimated release date was 19th November 2016, but still there is no sign of the quadrant either on the Gartner site or at vendor bragging on social media.

We can only guess about why the quadrant is delayed, but a possible explanation is that vendor feedback on the suggested positioning has been harsh. I am not among the ones who believes Gartner actually takes money from vendors for inclusion and positioning in the quadrant. Still, Gartner has a substantial business relationship with those vendors. If a vendor feels they are really wrongly misplaced, they may question the judgement in the other payable services from Gartner.

While waiting, there is still time to have your guess on who has persuaded Gartner to be where in the quadrant as already many have done in the post The Gartner Magic Quadrant for MDM 2016.

And yes, the prize for best guess is still a genuine Product Data Lake t-shirt.

t-shirt

The Gartner Magic Quadrant for MDM 2016

The Gartner Magic Quadrant for Master Data Management Solutions 2016 is …… not out.

Though it can be hard for a person not coming from the United States to read those silly American dates, according to this screenshot from today, it should have been out the 19th November 2016.

gartner-mdm-2016

I guess no blue hyperlink means it has not be aired yet and I do not recall having seen any vendor bragging on social media yet either.

The plan that Gartner will retire the old two quadrants for Customer MDM and Product MDM was revealed by Andrew White of Gartner earlier this year in the post Update on our Magic Quadrant’s for Master Data Management 2016.

Well, MDM implementations are often delayed, so why not the Multidomain MDM quadrant too.

In the meantime, we can take a quiz. Please comment with your guess on who will be the leaders, visionaries, challengers and niche players. Closest guess will receive a Product Data Lake t-shirt in your company’s license level size (See here for options).