The building next to my home office was originally two cement silos standing in an industrial harbor area among other silos. These two silos are now transformed into a connected office building as this area has been developed into a modern residence and commercial quarter.
Master Data Management (MDM) is on similar route.
The first quest for MDM has been to be a core discipline in transforming siloed data stores within a given company into a shared view of the core entities that must be described in the same way across different departmental views. Going from the departmental stage to the enterprise wide stage is examined in the post Three Stages of MDM Maturity.
But as told in this post, it does not stop there. The next transformation is to provide a shared view with trading partners in the business ecosystem(s) where your company operates. Because the shared data in your organization is also a silo when digital transformation puts pressure on each company to become a data integrated part of a business ecosystem.
As any other IT enabled discipline Master Data Management (MDM) continuously undergo a transformation while adopting emerging technologies. In the following I will focus on five trends that seen today seems to be disruptive:
MDM in the Cloud
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. But does it come as SaaS (Software as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service)? And what about DaaS (Data as a Service). Learn more in the post MDM, Cloud, SaaS, PaaS, IaaS and DaaS.
Extended MDM Platforms
There is a tendency on the Master Data Management (MDM) market that solutions providers aim to deliver an extended MDM platform to underpin customer experience efforts. Such a platform will not only handle traditional master data, but also reference data, big data (as in data lakes) as well as linking to transactions. Learn more in the post Extended MDM Platforms.
The scope of MDM will increase with the rise of Internet of Things (IoT) as reported in the post IoT and MDM. 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.
Ecosystem wide MDM
Doing Master Data Management (MDM) enterprise wide is hard enough. But it does not stop there. 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. Learn more in the post Multienterprise MDM.
One of the bottlenecks in Product Information Management (PIM) is getting product data ready for presentation to the buying audience as fast as possible.
Product data travels a long way from the origin at the manufacturing company, perhaps through distributors and wholesalers to the merchant or marketplace. In that journey the data undergo transformation (and translation) from the state it has at the producing organization to the state chosen by the selling organization.
However, time to market is crucial. This applies to when a new product range is chosen by the merchant or when there are changes and improvements at the manufacturer.
At Product Data Lake we enable a much faster pace in these quests than when doing this by using emails, spreadsheets and passive portals.
Leading up to the Nordic Midsummer I am pleased to join Informatica and their co-hosts Capgemini and CGI at two morning seminars on how successful organizations can leverage data to drive their digital transformation, the needed data strategy and the urge to have a 360-view of data relationships and interactions.
My presentations will be an independent view on the question: What are the latest and hottest trends within Master Data Management?
In this session, I will give the audience a quick walk-through visiting some in vogue topics as MDM in the cloud, MDM for big data, embracing Internet of Things (IoT) within MDM, business ecosystem wide MDM and the impact of Artificial Intelligence (AI) on MDM.
The events will take place, and you can register to be there, as follows:
The Forrester Report has this saying on that theme: “The internet of things has led to systems of automation and systems of design, which introduce new MDM usage scenarios to support co-design and the exchange of information on customers, products, and assets within ecosystems”.
Else, the report of course ranks the best selling MDM solutions as seen below:
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.
In software architecture, publish–subscribe is a messaging pattern where senders of messages, called publishers, do not program the messages to be sent directly to specific receivers, called subscribers, but instead categorize published messages into classes without knowledge of which subscribers, if any, there may be. Similarly, subscribers express interest in one or more classes and only receive messages that are of interest, without knowledge of which publishers, if any, there are.
This kind of thinking is behind the service called Product Data Lake I am working with now. Whereas a publish-subscribe service is usually something that goes on behind the firewall of an enterprise, Product Data Lake takes this theme into the business ecosystem that exists between trading partners as told in the post Product Data Syndication Freedom.
Therefore, a modification to the publish-subscribe concept in this context is that we actually do make it possible for publishers of product information and subscribers of product information to care a little about who gets and who receives the messages as exemplified in the post Using a Business Entity Identifier from Day One. However, the scheme for that is a modern one resembling a social network where partnerships are requested and accepted/rejected.
As messages between global trading partners can be highly asynchronous and as the taxonomy in use often will be different, there is a storage part in between. How this is implemented is examined in the post Product Data Lake Behind the Scenes.
The term narcissism originates from Greek mythology, where the young Narcissus fell in love with his own image reflected in a pool of water. While this is about how a natural person may behave it can certainly also be applied to how a company behaves.
Not to show empathy to customers
I think we all know the classic sales presentation with endless slides about how big and wonderful the selling company is and how fantastic the products they sell are. This approach contradicts everything we know about selling, which is to start with the needs and pain points at the buying company and then how the selling company effectively can fulfill the needs and make the pain points go away.
Not to show empathy to trading partners
While business outcomes originate from selling to your customers it certainly also is affected by how you treat your trading partners and how you can put yourself in their place.
An example close to me is exchange of product information (product data syndication) between trading partners. We often see solutions which is made to make it easy for you but then being difficult for your trading partner. This includes requiring your spreadsheet format to filled out by your trading partner, may be a customer data portal set up by a manufacturer or opposite a supplier data portal set up by a merchant. These are narcissistic dead ends as told in the post The Death Trap in Product Information Management: Your Customer/Supplier Portal.
A couple of weeks ago Microsoft, Adobe and SAP announced their Open Data Initiative. While this, as far as we know, is only a statement for now, it of course has attracted some interest based on that it is three giants in the IT industry who have agreed on something – mostly interpreted as agreed to oppose Salesforce.com.
Forming a business ecosystem among players in the market is not new. However, what we usually see is that a group of companies agrees on a standard and then each one of them puts a product or service, that adheres to that standard, on the market. The standard then caters for the interoperability between the products and services.
In this case its seems to be something different. The product or service is operated by Microsoft based on their Azure platform. There will be some form of a common data model. But it is a data lake, meaning that we should expect that data can be provided in any structure and format and that data can be consumed into any structure and format.
In all humbleness, this concept is the same as the one that is behind Product Data Lake.
The Open Data Initiative from Microsoft, Adobe and SAP focuses at customer data and seems to be about enterprise wide customer data. While it technically also could support ecosystem wide customer data, privacy concerns and compliance issues will restrict that scope in many cases.
At Product Data Lake, we do the same for product data. Only here, the scope is business ecosystem wide as the big pain with product data is the flow between trading partners as examined here.