Seven Flavors of MDM

Master Data Management (MDM) can take many forms. An exciting side of being involved in MDM implementations is that every implementation is a little bit different which also makes room for a lot of different technology options. There is no best MDM solution out there. There are a lot of options where some will be the best fit for a given MDM implementation.

The available solutions also change over the years – typically by spreading to cover more land in the MDM space.

In the following I will shortly introduce the basic stuff with seven flavours of MDM. A given MDM implementation will typically be focused on one of these flavours with some elements of the other flavors and a given piece of technology will have an origin in one of these flavours and in more or less degree encompass some more flavors.

7 flavours

The traditional MDM platform

A traditional MDM solution is a hub for master data aiming at delivering a single source of truth (or trust) for master data within a given organization either enterprise wide or within a portion of an enterprise. The first MDM solutions were aimed at Customer Data Integration (CDI), because having multiple and inconsistent data stores for customer data with varying data quality is a well-known pain point almost everywhere. Besides that, similar pain points exist around vendor data and other party roles, product data, assets, locations and other master data domains and dedicated solutions for that are available.

Product Information Management (PIM)

Special breed of solutions for Product Information Management aimed at having consistent product specifications across the enterprise to be published in multiple sales channels have been around for years and we have seen a continuously integration of the market for such solutions into the traditional MDM space as many of these solutions have morphed into being a kind of MDM solution.

Digital Asset Management (DAM)

Not at least in relation to PIM we have a distinct discipline around handling digital assets as text documents, audio files, video and other rich media data that are different from the structured and granular data we can manage in data models in common database technologies. A post on this blog examines How MDM, PIM and DAM Stick Together.

Big Data Integration

The rise of big data is having a considerable influence on how MDM solutions will look like in the future. You may handle big data directly inside MDM og link to big data outside MDM as told in the post about The Intersection of MDM and Big Data.

Application Data Management (ADM)

Another area where you have to decide where master data stops and handling other data starts is when it comes to transactional data and other forms data handled in dedicated applications as ERP, CRM, PLM (Product Lifecycle Management) and plenty of other industry specific applications. This conundrum was touched in a recent post called MDM vs ADM.

Multi-Domain MDM

Many MDM implementations focus on a single master data domain as customer, vendor or product or you see MDM programs that have a multi-domain vision, overall project management but quite separate tracks for each domain. We have though seen many technology vendors preparing for the multi-domain future either by:

  • Being born in the multi-domain age as for example Semarchy
  • Acquiring the stuff as for example Informatica and IBM
  • Extend from PIM as for example Riversand and Stibo Systems

MDM in the cloud

MDM follows the source applications up into the cloud. New MDM solutions naturally come as a cloud solution. The traditional vendors introduce cloud alternatives to or based on their proven on-promise solutions. There is only one direction here: More and more cloud MDM – also as customer as business partner engagement will take place in the cloud.

Ecosystem wide MDM

Doing 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 as reported in the post Ecosystem Wide MDM.

Ecosystem Wide MDM

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 will be a part of digitalization and thus we will have a need for working on the same foundation around master data.

The different master data domains will have different roles to play in such endeavors. Party master will be shared in some degree but there are both competitive factors, data protection and privacy factors to be observed as well.

MDM Ecosystem

Product master data – or product information if you like – is an obvious master data domain where you can gain business benefits from extending master data management to be ecosystem wide. This includes:

  • Working with the same product classifications or being able to continuously map between different classifications used by trading partners
  • Utilizing the same attribute definitions (metadata around products) or being able to continuously map between different attribute taxonomies in use by trading partners
  • Sharing data on product relationships (available accessories, relevant spare parts, updated succession for products, cross-sell information and up-sell opportunities)
  • Having access to latest versions of digital assets (text, audio, video) associated with products

The concept of ecosystem wide Multi-Domain MDM is explored further is the article about Master Data Share.

Product Information on Demand

Video on demand has become a popular way to watch television series, films and other entertainment and Netflix is probably the most known brand for delivering that.

The great thing about watching video on demand is that you do not have to enjoy the service at the exact same time as everyone else, as it was the case back in the days when watching TV or going to the movies were the options available.

At Product Data Lake we will bring that convenience to business ecosystems, as the situation today with broadcasting product information in supply chains very much resembles the situation we had before video on demand came around in the TV/Movie world.

As a provider of product information (being a manufacturer or upstream distributor), you will push your product information into Product Data lake, when you have the information available. Moreover, you will only do that once for each product and piece of information. No more coming to each theatre near your audience and extensive reruns of old stuff.

As a receiver of product information (being a downstream distributor, reseller or large end user), you will pull product information when you need it. That will be when you take a new product into your range or do a special product sale as well as when you start to deal with a new piece of information. No more having to be home at a certain time when your supplier does the show or waiting in ages for a rerun when you missed it.

Learn more about how Product Data Lake makes your life in Product Information Management (PIM) easier by following us here on LinkedIn.

Product Data Lake

 

Three Major Sectors within Product Information Exchange

When working with Product Information Management (PIM) and not at least with product information exchange between trading partners, I have noticed three major sectors where the requirements and means differs quite a bit.

These sectors are:

  • Food, beverage at pharmaceuticals: These are highly regulated sectors where the rules for taxonomy, completeness and exchange formats are advanced. Exchange standards and underpinning services as GS1/GDSN are well penetrated at least for basic data elements among major players. This sector counts for circa 1/6 of the world trade.
  • Fashion, books and mainstream electronics: The products within this sector can be described with common accepted taxonomies and do not differ that much though there certainly are room for more common adhered standards in some areas. The trade here is becoming more penetrated by marketplaces with their specific product information requirements. This sector counts for circa 1/6 of the world trade.
  • The rest (including building materials, special electronics, machinery, homeware): This is a diverse segment of products groups and the product groups themselves are diverse. The requirements for product information completeness and other data quality dimensions are overwhelming and the choice of standards are many, so most often two trading partners will be on different pages. This sector counts for circa 2/3 of the world trade.

Note: Automotive (vehicles) is a special vertical, where the main products (for example cars) resembles mainstream electronics and all the spare parts resembles special electronics. Some retailers (like department stores) covers all sectors and therefore need hybrid solutions to their product information exchange handling challenges.

The main drivers for better product information handling are compliance – not at least within food, beverage and pharmaceuticals – and self-service purchasing (as in ecommerce), where the latter has raged many years within fashion, books and mainstream electronics and now also is raising in more B2B (business-to-business) biased product groups as building materials, special electronics and machinery.

Learn more about how to tackle these diverse needs in product information exchange in the article and discussion about Product Data Lake.

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The Wide End-to-End Solution for Product Information Management (PIM)

The term End-to-End is used a lot in marketing jargon. Now, I will jump on that wagon too.

In reality, no solution will be an End-to-End solution for all your business needs. Therefore, my take will merely be to cast some light on an End-to-End need for which there are only very scattered solutions today.

If we look at Product Information Management (PIM) there are many good solutions for taking care of the End-to-End needs within your organisation. The aim is to gather the product information that exist within your organisation in various silos, have one trusted place for all this information and being able to publish this information in a consistent way across all sales channels – the omnichannel theme.

However, product information does in many cases not live just within your organization. In most cases, it lives in a business ecosystem of manufacturers, distributors, merchants and large end users.

Therefore we need an End-to-End solution for product information that encompasses the path from manufacturers over distributors to merchants and large end users and in some cases the way back.

Whether you are a manufacturer, distributor, merchant, large end user or a provider of tools and services for product information you can join the business ecosystem oriented End-to-End solution for product information. Please find some more information about Product Data Lake here.

As a manufacturer, you can find your benefits on the Product Data Push site here.

As a merchant, you can find your benefits on the Product Data Pull site here.

If you are a vendor in the Product Information Management space, you can join forces with us a explained here.

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The Old PIM World and The New PIM World

Standoff both sides narrow

Product Information Management (PIM) is challenged by the fact that product data is the kind of data that usually flows cross company. The most common route starts with that the hard facts about a product originates at the manufacturer. Then the information may be used on the brand’s own website, propagated to a marketplace (online shop-in-shop) and propagated downstream to distributors and merchants.

The challenge to the manufacturer is that this represent many ways of providing product information, not at least when it comes to distributors and merchants, as these will require different structurers and formats using various standards and not being on the same maturity level.

Looking at this from the downstream side as a merchant you have the opposite challenge. Manufacturers provide product information in different structurers and formats using various standards and are not on the same maturity level.

Supply chain participants can challenge this in a passive or an active way. Unfortunately, many have chosen – or are about to choose – the passive way. It goes like this:

  • As a manufacturer, we have a product data portal where trading partners who wants to do business with us, who obviously is the best manufacturer in our field, can download the product information we have in our structure and format using the standards we have found best.
  • As a merchant we have a supplier product data portal where trading partners who wants to do business with us, the leading player in our field, can upload the product information we for the time being will require in our structure and format using the standard(s) we have found best.
  • As a distributor, you could take both these standpoints.

This approach seems to work if you are bigger than your trading partner. And many times, one will be bigger than the other. But unless you are very big, you will in many cases not be the biggest. And in all cases where you are the biggest, you will not be a company being easy to do business with, which eventually will decide how big you will stay.

Using (often local) industry product data hubs is a workaround, but the challenges shines through and often it leads to that everyone will only benefit from what anyone can do and still calls for many touch points when doing business internationally and across several product data groups.

The better way is the active way creating a win-win situation for all trading partners as described in the article about Product Data Lake Business Benefits.

Providing a Digital Technology Platform

Gartner, the analyst firm, defines five different types of digital technology platforms:

  • Information system platform — Supports the back office operations such as ERP, CRM, PIM and other core systems with associated middleware and development capabilities.
  • Customer experience platform — Contains the main customer-facing elements, such as customer and citizen portals, multichannel commerce, and customer apps.
  • Analytics and intelligence platform — Contains information management and analytical capabilities. Data management programs and analytical applications fuel data-driven decision making, and algorithms automate discovery and action.
  • IoT platform — Connects physical assets for monitoring, optimization, control and monetization.
  • Business ecosystem platform — Supports the creation of, and connection to, external ecosystems, marketplaces and communities.
Gartner Digital Platforms 2
Source: Gartner

As a vendor of a modern data management platform, you will probably identify yourself primarily within one of these five types.

At Product Data Lake we are first and foremost a business ecosystem platform, being a cloud service for sharing product data in the business ecosystems of manufacturers, distributors, merchants and the end users of product information. As such, we are proud to be a part of the The Rise of Business Ecosystems in Data Management.

Of course, there are ties to the other types of digital technology platforms as well. As explained in the post Adding Things to Product Data Lake, the ecosystem approach is necessary to identify and track physical assets. Analytics will encompass data, as for example product data, in the business ecosystem. Customer experience in multichannel commerce when it comes to completeness of product information will require an effective cross company digital technology platform.

An external focused business ecosystem platform will have to be easily connected to the various internal focused information system platforms at trading partners. In our case, this is What a PIM-2-PIM Solution Looks Like.

 

Disruptive Forces in MDM Land

MDM 2017 disruptionFor the second time this year there is a Gartner Magic Quadrant for Master Data Management Solutions out. The two leaders, Orchestra Networks and Informatica, have released their free copies here and here.

Now Gartner have stopped having a list of vendors on the market too small to be in the actual quadrant. So, if you are looking for new thinking, you will have to read the section about disruptive forces in the MDM market.

Gartner says that every market experiences disruptive forces that influence its overall shape and trajectory over time, and that inspire innovation, both transformational and incremental. According to Gartner, those most prominent in the MDM market appear diametrically opposed.

The current market is dominated by vendors who have predominantly taken a platform-centric approach involving robust technology stacks categorized as application-neutral hub-based solutions. Thus, the business value of the resulting master data is realized through utilization of that data within business applications or suites, or analytics platforms, external to the MDM solution — such as CRM, ERP and e-commerce systems, and data warehouses.

One disruptive force against that is an increase in business applications or suites with embedded ADM (Application Data Management) capabilities that address organizational needs for data management, including MDM (to varying degrees), while also managing nonmaster data for the pertinent application. Gartner states that application-centric approaches for some organizations can return greater value than platform-centric approaches in the short term and do so at reduced cost.

The opposing disruptive force stems from the emergence of more generalized data management solutions. These provide for unified execution logic on top of what is effectively an integrated technology stack. Vendors envision the primary consumption model to be cloud-based subscription. As such, these solutions will also provide a means for midmarket organizations and SMBs to procure advanced data management capabilities (such as MDM) using this model of consumption. Executed crisply, cloud-based subscriptions to these solutions may even moderate the rise of cloud-based MDM offerings.

Regular readers of this blog may guess, that I see a coming third disruptive force in MDM land, being specialized data management services for business ecosystems as explored in the post Ecosystems are The Future of Digital and MDM.

The Good, the Better and the Best Kinds of Data Quality Technology

If I look at my journey in data quality I think you can say, that I started with working with the good way of implementing data quality tools, then turned to some better ways and, until now at least, is working with the best way of implementing data quality technology.

It is though not that the good old kind of tools are obsolete. They are just relieved from some of the repeating of the hard work in cleaning up dirty data.

The good (old) kind of tools are data cleansing and data matching tools. These tools are good at finding errors in postal addresses, duplicate party records and other nasty stuff in master data. The bad thing about finding the flaws long time after the bad master data has entered the databases, is that it often is very hard to do the corrections after transactions has been related to these master data and that, if you do not fix the root cause, you will have to do this periodically. However, there still are reasons to use these tools as reported in the post Top 5 Reasons for Downstream Cleansing.

The better way is real time validation and correction at data entry where possible. Here a single data element or a range of data elements are checked when entered. For example the address may be checked against reference data, phone number may be checked for adequate format for the country in question or product master data is checked for the right format and against a value list. The hard thing with this is to do it at all entry points. A possible approach to do it is discussed in the post Service Oriented MDM.

The best tools are emphasizing at assisting data capture and thus preventing data quality issues while also making the data capture process more effective by connecting opposite to collecting. Two such tools I have worked with are:

·        IDQ™ which is a tool for mashing up internal party master data and 3rd party big reference data sources as explained further in the post instant Single Customer View.

·        Product Data Lake, a cloud service for sharing product data in the business ecosystems of manufacturers, distributors, retailers and end users of product information. This service is described in detail here.

DQ

Sell more. Reduce costs.

Business outcome is the end goal of any data management activity may that be data governance, data quality management, Master Data Management (MDM) and Product Information Management (PIM).

Business outcome comes from selling more and reducing costs.

At Product Data Lake we have a simple scheme for achieving business outcome through selling more goods and reducing costs of sharing product information between trading partners in business ecosystems:

Sell more Reduce costs

Interested? Get in touch: