A previous post on this blog was called A Business Oriented Data Mind Map. In here, I took a snapshot of my brain when thinking through some different kinds of data used within doing business.
One omission of a kind of data was though metadata. So, let me just think metadata into those other kinds of data:
Metadata is data about data. Therefore, metadata applies to any other kind of data.
Metadata could be part of structured data but is unfortunately often only sparsely treated as data dictionaries and does only in a few cases reach the level of a business glossary not to say being continuously maintained in such a glossary. Thus, a lot of structured data is not used as the valuable information it could be.
Metadata should be applied to unstructured data. If not, unstructured data will just be data, and will not be transformed to information and used as knowledge.
Metadata must be used to link data on premise and data in the cloud. Metadata is the key to syndicate local data and global within an enterprise and across business ecosystems. Metadata must be used to combine internal and external data and make the transformation from data over information and knowledge to wisdom.
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.
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?
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.
I 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.
A recent post on this blog was called B2C vs B2B in Product Information Management. This post was my take on the differences, if any, between doing Product Information Management (PIM) in a Business-to-Consumer (B2C) scenario versus in a Business-to-Business (B2B) scenario.
“For many of our B2B customers information plays a bigger role in the Market-to-Order process than for consumer products. But most of our customers (Consumer & Professional Packaged Goods Manufacturers) serve both retail and professional/wholesale channels, which have different information needs, even regarding the same products. So, any manufacturer targeted solution should be able to serve both channels with the right content via the right data channels. In our vision a more relevant question is: What is your take on the differences on doing PIM in Manufacturing versus Wholesale / Retail.”
Indeed, there are several ways to slice the PIM space and the supply chain position of a company as a supply chain delegate is for sure very relevant. Exchanging product information between trading partners in upstream and downstream (and midstream) positions must be very flexible and one size fits all thinking will not work.
The different positions of a company as they are in my mind is illustrated below:
The possible combinations when exchanging product information between supply chain delegates are plentiful. To mention a few channels:
Manufacturer to wholesaler to retailer to end private consumer
Manufacturer to distributor to dealer to end business customer
Manufacturer to distributor to dealer to manufacturer as raw material
Manufacturer to merchant to marketplace to end customer
Manufacturer to marketplace to end customer
Manufacturer to/from brand owner to any midstream/downstream delegate
This variety is why the means of exchanging product information (product data syndication) between trading partners is essential in almost any PIM solution.
At Product Data Lake we offer the remedy to this challenge and in combination with any PIM solution or other application where in-house product information is managed.
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:
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 title of this blog post is also the title of a presentation I will do at the 2019 Data Governance and Information Quality Conference in San Diego, US in June.
There is a little difference between how we can exercise data governance and information quality management when we are handling data about products versus handling the most common data domain being party data (customer, vendor/supplier, employee and other roles).
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.