Organizations can deliver more value when they collaborate in sharing data externally

The saying in the title of this blog post is taken from a recent Gartner article with the tile Data Sharing Is a Business Necessity to Accelerate Digital Business.

In this article authored by Laurence Goasduff a key takeaway is that:

‘By 2023, organizations that promote data sharing will outperform their peers on most business value metrics”.

Regular readers of this blog will know that many good data things come from data sharing as for example pondered in the 11 years old post called Sharing data is key to a single version of the truth.

A consequence of the business benefits in sharing data will be a rise in data management disciplines aiming at business ecosystem wide data sharing, where product data syndication is an obvious opportunity.

During the last years I have been working on such a solution. This one is called Product Data Lake.

30% Network Economy and MDM

McKinsey Digital Network Economy and Digital Ecosystem

McKinsey Digital recently published an article with the title How do companies create value from digital ecosystems?

In here it is said that: “The integrated network economy could represent a global revenue pool of $60 trillion in 2025 with a potential increase in total economy share from about 1 to 2 percent today to approximately 30 percent by 2025”.

This dramatic shift will in my eyes mean a change of direction in the way we see Master Data Management (MDM) as well as Product Information Management (PIM) and Data Quality Management (DQM) solutions.

360 is a magic number in the master data and data quality world. It is about having a 360-degree view of customers, suppliers, and products. This is an inside-out view. The enterprise is looking at a world revolving around the enterprise just as back then when we thought the universe revolved around the planet Earth.

By 2025 forward looking enterprises must have changed that view and directed master data, product information and data quality management into a state fit for the network economy by having a business ecosystem wide MDM (PIM and DQM) solution landscape.

Gartner, the analyst firm, coins this Multienterprise MDM.

Data Marketplaces, Exchanges and Multienterprise MDM

In the recent Gartner Top 10 Trends in Data and Analytics for 2020 trend number 8 is about data marketplaces and exchanges. As stated by Gartner: “By 2022, 35% of large organizations will be either sellers or buyers of data via formal online data marketplaces, up from 25% in 2020.”

The topic of selling and buying data was touched here on the blog in the post Three Flavors of Data Monetization

A close topic to data marketplaces and exchanges is Multienterprise MDM.

In the 00’s the evolution of Master Data Management (MDM) started with single domain / departmental solutions dominated by Customer Data Integration (CDI) and Product Information Management (PIM) implementations. These solutions were in best cases underpinned by third party data sources as business directories as for example the Dun & Bradstreet (D&B) world base and second party product information sources as for example the GS1 Global Data Syndication Network (GDSN).

In the previous decade multidomain MDM with enterprise wide coverage became the norm. Here the solution typically encompasses customer-, vendor/supplier-, product- and asset master data. Increasingly GDSN is supplemented by other forms of Product Data Syndication (PDS). Third party and second party sources are delivered in the form of Data as a Service that comes with each MDM solution.

Data Marketplaces and Exchange

In this decade we will see the rise of multienterprise MDM where the solutions to some extend become business ecosystem wide, meaning that you will increasingly share master data and possibly the MDM solutions with your business partners – or else you will fade in the wake of the overwhelming data load you will have to handle yourself.

The data sharing will be facilitated by data marketplaces and exchanges.

On July 23rd I will, as a representative of The Disruptive MDM/PIM/DQM List, present in the webinar How to Sustain Digital Ecosystems with Multi-Enterprise MDM. The webinar is brought to you by Winshuttle / Enterworks. It is a part of their everything MDM & PIM virtual conference. Get the details and make your free registration here.

B2B2C in Data Management

The Business-to-Business-to-Consumer (B2B2C) scenario is increasingly important in Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM).

This scenario is usually seen in manufacturing including pharmaceuticals as examined in the post Six MDMographic Stereotypes.

One challenge here is how to extend the capabilities in MDM / PIM / DQM solutions that are build for Business-to-Business (B2B) and Business-to-Consumer (B2C) use cases. Doing B2B2C requires a Multidomain MDM approach with solid PIM and DQM elements either as one solution, a suite of solutions or as a wisely assembled set of best-of-breed solutions.B2B2C MDM PIM DQMIn the MDM sphere a key challenge with B2B2C is that you probably must encompass more surrounding applications and ensure a 360-degree view of party, location and product entities as they have varying roles with varying purposes at varying times tracked by these applications. You will also need to cover a broader range of data types that goes beyond what is traditionally seen as master data.

In DQM you need data matching capabilities that can identify and compare both real-world persons, organizations and the grey zone of persons in professional roles. You need DQM of a deep hierarchy of location data and you need to profile product data completeness for both professional use cases and consumer use cases.

In PIM the content must be suitable for both the professional audience and the end consumers. The issues in achieving this stretch over having a flexible in-house PIM solution and a comprehensive outbound Product Data Syndication (PDS) setup.

As the middle B in B2B2C supply chains you must have a strategic partnership with your suppliers/vendors with a comprehensive inbound Product Data Syndication (PDS) setup and increasingly also a framework for sharing customer master data taking into account the privacy and confidentiality aspects of this.

This emerging MDM / PIM / DQM scope is also referred to as Multienterprise MDM.

Customer Data Platform (CDP) vs Master Data Management (MDM)

A recent Gartner report states that: “Organizations that fail to understand their use cases, desired business outcomes and customer data governance requirements have difficulty choosing between CDPs and MDM solutions, because of overlapping capabilities.”

Indeed. This topic was examined here on the blog last year in the post CDP: Is that part of CRM or MDM?

Gartner compare the two breeds of solutions this way:

  • CDPs are marketing-managed tools designed for the creation, segmentation and activation of customer profiles. … These platforms have less governance functionality than MDM solutions and tend to focus on delivering a complete view through the amalgamation of data generated by digital customer interactions.
  • MDM solutions are more mature technology that also enable customer 360 insights by creating and managing a central, persisted system or index of record for master customer records. They enable governance and management of the core data that uniquely identifies one customer as distinct from another. They were built to support enterprisewide sources and applications of customer data.

CDP platforms (via CRM applications) seems to hit from outside in without getting to the core of customer centrecity. MDM solutions are hitting the bullseye and some of the MDM solutions are moving inside out in the direction of extended MDM, where all customer data, not just customer master data, is encompassed under the same data governance umbrella.

CDP vs MDM

Get the Gartner report Choose Between Customer Data Platforms and MDM Solutions for 360-Degree Customer Insights through Reltio here.

Four Themes That Will Take MDM Beyond MDM as We Have Known It

The Master Data Management (MDM) discipline is emerging. A certain trend is that MDM solutions will grow beyond handling traditional master data entities and encompass other kinds of data and more capabilities that can be used for other kinds of data as well.

Semarchy XDMThis include:

  • Utilizing data discovery to explore data sources with master data, reference data, critical application data and other kinds of data as described in the post How Data Discovery Makes a Data Hub More Valuable.
  • Governing the full set of data that needs to be governed as examined in the post Maturing RDM, MDM and ADM With Collaborative Data Governance.
  • Building a data hub that encompass the right representation of data that needs to be shared enterprise wide and even business ecosystem wide as explained in the post Why Flexible Data Models are Crucial in Data Sharing.
  • Measuring data quality in conjunction with general key performance indicators in dashboards that besides master data also embraces other internal and external sources as for example aggregated data from data warehouses and data lakes.

These themes were also covered in a webinar I presented with Semarchy last month. Watch the webinar The Intelligent Data Hub: MDM and Beyond.

Why Flexible Data Models are Crucial in Data Sharing

Master data and reference data are two types of data that are shared enterprise wide and even in the wider business ecosystem where your company operates.

In your organization and business ecosystem the data that is shared is basically held in applications like ERP and CRM solutions that have come with a data model provided by the solution vendor. These data models are built to facilitate the operations that is supported by each of these applications and is a data model that must suite every kind of organization.

A core reason of being for a Master Data Management (MDM) solution is to provide a data store where master data is represented in a way that reflects the business model of your organization. This data store serves many purposes as for example being a data integration hub and the place where the results of data quality improvements (eg de-duplication) are stored.

Data model

Such a data hub can go beyond master data entities and represent reference data and critical application data that is shared across your organization and the wider business ecosystem within a given industry.

Learn more about flexible data models in a data hub context in the Semarchy whitepaper authored by me and titled The Intelligent Data Hub: Taking MDM to the Next Level.

MDM, PIM, DAM and 7 More Data Management TLAs

What is CDI (in a data management context)? What does PDS stand for? And what about RDM?

Well, here is an under 2 minutes silent video going through what 10 common Three Letter Acronyms in the data management world is:

Read more about the Three Letter Acronyms in the post 10 Data Management TLAs You Should Know.

Learn more about some of the best solutions in this space on The Disruptive MDM / PIM / DQM List.

What MDMographic Stereotype is Your Organization?

In marketing we use the term demographic stereotype for segmenting individual persons according to known data elements as age and where we live. There is also a lesser used term called firmographic stereotypes, where companies are segmented according to industry sector, size and other data elements.

Solutions for Master Data Management (MDM) and related disciplines are often presented by industry sector. In my work with tool selection – either as a thorough engagement or a quick select your solution report – I have identified some MDMographic stereotypes, where we have the same requirements based on the distribution of party (customer and supplier/vendor) entities and product entities:

MDMographic Stereotypes and Venn

These stereotypes are further explained in the post Six MDMographic Stereotypes.

Who are the ADM Solution Providers?

ADM MDMAs examined in the post MDM vs ADM there is a sister discipline to Master Data Management (MDM) called Application Data Management (ADM).

While there are plenty of analyst market reports on who are the MDM solution providers, there are no similar ADM solution market reports. Not even by Gartner, who has coined the ADM term.

So, let me try to present three (to seven) examples of who might be some of the leading ADM solutions:

Oracle (CDM Cloud and Product MDM Cloud)

Oracle was thrown out of the latest Gartner Magic Quadrant for MDM Solutions as their approach reflects an exclusively ADM approach to MDM, thus meeting the associated Gartner defined exclusion criteria.

This indicates that you can use Oracle technology to underpin data management encompassing master data and other critical application data as long as these data are managed in an Oracle application or brought from somewhere else into the Oracle application before the data management capabilities are applied.

SAP ECC, S/4HANA, MDG

A lot of master/application data management takes place inside SAP’s ERP application which was called ECC and is now being replaced by S/4HANA. As SAP ERP do not provide much help for master data management, there are third-party applications that helps with that. One example I have worked with is it.mds.

SAP has introduced their newest MDM solution called SAP MDG (Master Data Governance). While this MDM solution in theory may be a solution that embraces all master data within an enterprise, it is, as I see it, in practice used to govern master data that sits in SAP ECC or S/4HANA as the core advantage of SAP MDG is that it fits with the SAP ERP data model and technology set up.

Semarchy xDM

The Semarchy solution is called xDM, implying that x can be everything as M for MDM, R for RDM (Reference Data Management) and A for ADM. In this approach the data management capabilities as data governance, hierarchy management and workflow management are applied in their Intelligent Data Hub™ regardless of the brand of the source (and target) application.

xDM from Semarchy is one of the featured solutions on The Disruptive MDM / PIM / DQM List. Learn more her.