Multi-Domain MDM

The Intersection Between MDM, PIM and ESG

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As touched on in the post Three Essential Trends in Data Management for 2024, the Environmental, Social and Governance (ESG) theme is high on the data management agenda in most companies. Lately I have worked intensively with the intersection of ESG and Master Data Management (MDM) / Product Information Management (PIM).

In this post I will go through some of the learnings from this.

Digital Product Passport

The European Union concept called the Digital Product Passport (DPP) is on its way, and it will affect several industries, including textile, apparel, and consumer electronics. The first product category that will need to comply with the regulation is batteries. Read more about that in the article from PSQR on the Important Takeaways from CIRPASS’ Final Event on DPP.

I have noticed that the MDM and PIM solution providers are composing a lot of their environmental sustainability support message around the DPP. This topic is indeed valid. However, we do not know many details about the upcoming DPP at this moment.

EPD, the Existing DPP Like Concept

There is currently a concept called Environmental Product Declaration (EPD) in force for building materials. It is currently not known to what degree the DPP concept will overlap the EPD at some point in the future. The EPD is governed by national bodies, but there are quite a lot of similarities between the requirements across countries. The EPD only covers environmental data whereas the DPP is expected to cover wider ESG aspects.

Despite the minor differences between DPP and EPD, there is already a lot to learn from the data management requirements for EPD in the preparation for the DPP when that concept materializes – so to speak.

Environmental Data Management

The typical touchpoint between the EPD and PIM today is that the published EPD document is a digital asset captured, stored, tagged, and propagated by the PIM solution along with other traditional digital assets as product sheets, installation guides, line drawings and more.

The data gathering for the EPD is a typical manual process today. However, as more countries are embracing the EPD, more buyers are looking for the EPD and the requirements for product granularity for the EPD are increasing, companies in the building material industry are looking for automation of the process.

The foundation for the EPD is a Life Cycle Assessment (LCA). That scope includes a lot of master data that reaches far beyond the finished product for which the EPD is created. This includes:

  • The raw materials that go into the Bill of Materials.
  • The ancillary materials that are consumed during production.
  • The supplier’s location from where the above materials are shipped.
  • The customer’s location to where the finished product is shipped.
  • The end user location from where recycling products is shipped.
  • The recycled product that goes back into the Bill of Materials.

All-in-all a clear case of Multi-Domain Master Data Management.

It is easy to imagine that the same will apply to products such as textile, apparel and electronics which are on the radar for the DPP.

Examples of Environmental Data

CO2 (or equivalent) emission is probably the most well known and quoted environmental data element as this has a global warming potential impact.

However, the EPD covers more than twenty other data elements relating to potential environmental impact including as for example:

  • Ozone layer depletion potential – measured as CFC (or equivalent) emission.
  • Natural resource (abiotic) depletion potential – measured as antimony (or equivalent) consumption.
  • Use of fresh water – measured as H2O volume consumption.

Can I help You?

If you are in a company where environmental sustainability and data management is an emerging topic, I can help you set the scene for this. If you are at an MDM/PIM solution provider and need to enhance your offering around supporting environmental sustainability, I can help you set the scene for this. Book a short introduction meeting with me here.

MDM For the Finance Domain

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Most Master Data Management implementations revolve around the business partner (customer/supplier) domain and/or the product domain. But there is a growing appetite for also including the finance domain in MDM implementations. Note that the finance domain here is about finance related master data that every organization has and not the specific master data challenges that organizations in the financial service sector have. The latter topic was covered on this blog in a post called “Master Data Management in Financial Services”.

In this post I will examine some high-level considerations for implementing an MDM platform that (also) covers finance master data.

Finance master data can roughly be divided into these three main object types:

  • Chart of accounts
  • Profit and cost centers
  • Accounts receivable and accounts payable

Chart of Accounts

The master data challenge for the chart of accounts is mainly about handling multiple charts of accounts as it appears in enterprises operating in multiple countries, with multiple lines of business and/or having grown through mergers and acquisitions.

For that reason, solutions like Hyperion have been used for decades to consolidate finance performance data for multiple charts of accounts possibly held in multiple different applications.

Where MDM platforms may improve the data management here is first and foremost related to the processes that take place when new accounts are added, or accounts are changed. Here the MDM platform capabilities within workflow management, permission rights and approval can be utilized.

Profit and Cost Centers

The master data challenge for profit and cost centers relates to an extended business partner concept in master data management where external parties as customers and suppliers/vendors are handled together with internal parties as profit and cost centers who are internal business units.

Here silo thinking still rules in my experience. We still have a long way to go within data literacy in order to consolidate financial perspectives with HR perspectives, sales perspectives and procurement perspectives within the organization.

Accounts Receivable and Accounts Payable

The accounts receivable data has an overlap and usually share master data with customer master data that are mastered from a sales and service perspective and accounts payable data has an overlap and usually share master data with supplier/vendor master data that are mastered from a procurement perspective.

But there are differences in the selection of parties covered as touched on in the post “Direct Customers and Indirect Customers”. There are also differences in the time span of when the overlapping parties are handled. Finally, the ownership of overlapping attributes is always a hard nut to crack.

A classic source of mess in this area is when you have to pay money to a customer and when you get money from a supplier. This most often leads to creation of duplicate business partner records.

MDM Platforms and Finance Master Data

Many large enterprises use SAP as their ERP platform and the place of handling finance master data. Therefore, the SAP MDG-F offer for MDM is a likely candidate for an MDM platform here with the considerations explored in the post “SAP and Master Data Management”.

However, if the MDM capabilities in general are better handled with a non-SAP MDM platform as examined in the above-mentioned post, or the ERP platform is not (only) SAP, then other MDM platforms can be used for finance master data as well.

Informatica and Stibo STEP are two examples that I have worked with. My experience so far is though that compared to the business partner domain and the product domain these platforms at the current stage do not have much to offer for the finance domain in terms of capabilities and methodology.

4 Key Aspects of Master Data Management in Manufacturing

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Master Data Management (MDM) has a lot of common considerations regardless of the industry where MDM will be blueprinted and implemented and then there are some key aspects to consider specifically for a given industry.

For manufacturing I have found these 4 aspects as key areas when making the roadmap and deciding on Master Data Management architecture principles:

● The impact of Internet of Things (IoT) and Industrial Internet of Things (IIoT)
● Balancing global and local
● Mix of implementation styles
● Direct and indirect customers

The impact of Internet of Things (IoT) and Industrial Internet of Things (IIoT)

More and more produced products are smart devices. This goes for household appliances, power tools, cars and much more. Thus, they are part of the Internet of Things (IoT) meaning that each instance of the product (each produced thing) has its own identity, with a specific configuration, with specific ownership and caretaker-ship and each thing is producing streams of data. This will considerably extend the reach of Data Management and will require your Master Data Management to be open towards business partners.

For manufacturing the producing equipment is also smart devices with a lot of data involved and this can only be sustainable maintained and governed by a master data approach. This realm is sometimes called Industrial Internet of Things (IIoT) which is a facet of Industry 4.0.

Balancing global and local

In manufacturing you can only centralize master data management to a certain degree. There are manufacturing and adjacent processes that are best kept localized due to essential variances in product characteristics, geographic differences and other specializations in line of work.

Therefore, finding the right balance between global and local is a crucial point in blueprinting your manufacturing data management solution, reaching the best fit Master Data Management architecture and building the overarching data governance framework.

Mix of implementation styles

For the same reasons you will not be able to follow a full-blown centralized Master Data Management implementation style. You will need to go for a consolidation Master Data Management style but not necessarily for all data domains and subdomains. You can mix these two styles in what can be seen as a co-existence Master Data Management style.

Direct and indirect customers

In manufacturing your direct customers are typically distributors and retailers who have the end-user customers as their direct customers. However, it happens that you have a business scenario where the same end-users also become your direct customer as a manufacturer. Also, you as a manufacturer for many reasons will benefit from loyally share the end-user customer data with your business partners.

Your Master Data Management implementation should cater for providing a true 360-degree view on customers in this complex business setup.

Learn more

One good resource for a deeper dive into the Master Data Management Architecture considerations in manufacturing is a presentation by my long-time data management peer Magnus Wernersson and Pekka Tamminen of Solita. Find the YouTube video here provided by Semarchy: Volvo Cars – Data Centricity & Digital Innovations with MDM Architecture.

A Guide to Master Data Management

Henrik Gabs Liliendahl1 Comment

Over the recent months, I have been engaged with the boutique Master Data Management (MDM) consultancy parsionate on some visionary projects in the multi-domain MDM area.

The overall approach at parsionate is that MDM is much more than just an IT issue. It is a strategic necessity. This is based on an experience that I share, which is that if you treat MDM as an isolated technological problem, you will inevitably fail!

Multi-domain MDM

MDM implementations today are increasingly becoming enterprise wide and are thus also multi-domain meaning that they cover business partner domains as customer and supplier/vendor, the product domain and a longer tail of other core business entities that matters within the given business model.

The primary goal of a multi-domain MDM implementation is to unify and consolidate heterogeneous data silos across the enterprise. The more source systems are integrated, the more information will be available and the more your enterprise will benefit from a 360° view of its data.

To achieve the desired goal for your multi-domain MDM program, you need to have a clear vision and a long-term strategic roadmap that shows how the various MDM implementation stages fit together with other related initiatives within your enterprise and where the Return of Investment (ROI) is expected and how it can be measured.

Seven Phases of Forming the Roadmap

In the approach at parsionate there are seven phases to go through when forming the roadmap and launching an MDM program.

Phase 1: Identify business needs

Before embarking on an MDM program, consider what data is most important to your business and where you can create the most value by putting this data under the MDM umbrella.

The main rationale is that through MDM organizations can control the way their most important data is viewed and shared in a more efficient, traceable way and thus improve their performance and productivity. An effective MDM implementation helps you streamline your workflows. It breaks down data silos that prevent data from being reused and shared across the enterprise

Phase 2: Set up a data committee

Establishing a data committee (with any equivalent name for a data focussed body) is perhaps the most frequently mentioned aspect of an MDM strategy. This team would usually consist of many different stakeholders who are in a position to enforce the roadmap and the underlaying activities.

This body must be able to convey the importance of data across the enterprise at all organizational levels. The main concern is to make data a top priority in the enterprise.

Phase 3: Set up a data governance program before defining the MDM roadmap

Many organizations shy away from data governance programs that consulting firms suggest because they seem too complex and costly.

The bitter truth is though that if you fail to implement data governance or embed it strategically into the way the organization works, you will inevitably have to address it at a later stage – in a more time-consuming and costly process.

Phase 4: Set clear goals to ingrain the MDM vision in the organization’s culture

It is difficult to promote an MDM program without a clear vision and objectives. At the executive level, the program could be misunderstood as a technological issue. Sometimes decision-makers struggle to understand the value an MDM program will generate. In this case, they will either not fund it or, if it does go ahead, consider it a failure because the result does not meet their expectations.

It is crucial to involve all relevant stakeholders in the roadmap development process at an early stage and engage with them to understand their expectations and requirements. Only then can you ensure that the core elements of a successful MDM program are aligned with the needs of your entire organization.

Phase 5: Choose a step-by-step approach and rapidly implement sub-projects

The most effective way to implement an MDM program is to start with a few key sources and provide meaningful information in a very short time. It is always difficult to implement everything at once (multiple sources, several integration points, and com­plex data) with a „big bang“ or build a data hub without a specific goal in mind.

If a pilot project quickly realizes a series of short-term bene­fits, users and business leaders will appreciate the value of the MDM program. As soon as it becomes clear that the initial project is successful, you can promote a wider roadmap that shows how the next steps will be carried out in line with the strategic goals of your organization. With this iterative approach, the long-term benefits will become clearer.

Phase 6: The mammoth task: Adopt a data-driven mindset

Building a data-driven corporate culture may be considered the supreme challenge of any MDM program. Data culture goes far beyond a simple corporate strategy that uses data for business processes. Rather, it is a mindset that encourages employees to appreciate the tremendous added value of high-quality data.

Many organizations believe they can simply buy new tools to drive digital transformation. That is an illusion. 

Phase 7: Integrate technology

This illusion does however not mean that that MDM technology is not important.

Hardly any other IT system affects as many different departments and data domains in a company and is, implemented the right way, as powerful as an MDM solution. The extreme importance of this type of software within the entire corporate IT infrastructure means that you need to select a system very carefully and strategically.

The Full Guide If you want to read the full guide to MDM mapping out the high road to a successful implementation you can download it from this parsionate site: Master Data Management

2022 Data Management Predictions

Henrik Gabs Liliendahl2 Comments

On the second last day of the year it is time to predict about next year. My predictions for the year gone were in the post Annus Horribilis 2020, Annus Mirabilis 2021?. These predictions were fortunately fluffy enough to claim that they were right.

There is no reason not to believe that the wave of digitalization will go on and even intensify. Also, it seems obvious that data management will be a sweet spot of digitalization.

The three disciplines within data management focussed on at this blog are:

  • MDM: Master Data Management
  • PIM: Product Information Management
  • DQM: Data Quality Management

So, let`s look at what might happen next year within these overlapping disciplines.

MDM in 2022

MDM will keep inflating as explained in the post How MDM inflates. 

More organizations will go for enterprise wide MDM implementations and those who accomplish that will continue to do interenterprise MDM.

More business objects will be handled within the MDM discipline. Multidomain MDM will in more and more cases extend beyond the traditional customer, supplier and product domain.

Intelligent capabilities as Machine Learning (ML) and Artificial Intelligence (AI) will augment the basic IT capabilities currently used within MDM.

PIM in 2022

As with MDM also PIM will go more interenterprise wide. As organizations get a grip on internal product data stores the focus will move to collaborating with external suppliers of product data and external consumers of product data through Product Data Syndication.

In some industries PIM will start extending from the handling the product model to also handling each instance of each product as examined in the post Product Model vs Product Instance.

There will also be a term called augmented PIM meaning using Machine Learning and Artificial Intelligence to improve product data quality. In fact, classification of products using AI has been an early use case of AI in data management. This use case will be utilized more and more besides other product information use cases for AI and ML.

DQM in 2022

Data quality management will also go wider as data quality requirements increasingly will be a topic in business partnerships. More and more contracts between trading partners will besides pricing and timing also emphasize on data quality.

Data quality improvement has for many years been focused on the quality of customer data. This is now extending to other business objects where we will see data quality tools will get better support for other data domains and the data quality dimensions that are essential here.

ML and AI data quality use cases will continue to be implemented and go beyond the current trial stage to be part of operational business processes though still at only a minority of organizations.  

Happy New Year.

Gartner MDM Quadrant vs Forrester MDM Wave, Q4 2021

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In Q4 2021 both Gartner and Forrester published their ranking of MDM solutions.

The two rankings are in some agreement. Both analyst firms have Informatica closest to the top-right corner. However, there are also these divergences:

Leader at Forrester, Challenger at Gartner:

·       Reltio has during the last couple of years been better rated at Forrester than by Gartner. According to Forrester, Reltio leverages modern capabilities such as Artificial Intelligence / Machine Learning (AI/ML), knowledge graph, and embedded analytics to support large and complex MDM deployments.

Leaders at Gartner. Strong Performers at Forrester:

·       Semarchy has, according to Gartner, a strong understanding of the MDM market, and its longer-term strategy of positioning MDM as a core capability within a data management platform is in sync with the MDM market direction.
·       Riversand received, according to Gartner, relatively high scores across the breadth of categories representative of the customer engagement life cycle from presales through delivery and support.

Included by Forrester, in Honorable Mentions by Gartner:

·       Prospecta Master Data Online provides, according to Forrester, a cloud-based MDM platform, leveraging predefined integration and data models, AI/ML capabilities for an intelligent rules-based approach to enriching data, and integration of data quality and governance.
·       Magnitude Kalido MDM helps, according to Forrester, define and model critical business information from many domains, including customer, product, financial, and vendor/supplier, to create and manage accurate, integrated, and governed data.

Included by Gartner, but not by Forrester:

·       Contentserv had, according to the Gartner survey, the highest levels of satisfaction for overall product capabilities and had the jointhighest satisfaction for ease of deployment.
·       Tamr utilizes, according to Gartner, augmented data management as a foundational element to its solution, with particular focus on the use of human-trained ML processes to augment entity resolution, data profiling, data mapping and data modelling.
·       PiLog reviewers highlight, according to Gartner, its strength in duplicate material identification and prevention. Reviewers repeatedly credited their MRDM solution for asset optimization, inventory and procurement cost savings.

You can find a link to a free copy of the Gartner report in the post The Gartner MDM MQ of December 2021 and Augmented MDM.

The November 2021 Forrester MDM Wave is downloadable from Reltio here.

The Disruptive MDM/PIM/DQM List 2022: Informatica

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The next vendor to be included on The Disruptive MDM / PIM / DQM List 2022 is Informatica.

Informatica has been a leader on the Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) space for many years latest as seen in their 6th time front position on the Gartner MDM quadrant.

Their success is also apparent in the recent Earnings Report and their return to the public markets.

You can learn more about Informatica here.

Welcome Viamedici on The Disruptive MDM/PIM/DQM List

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I am pleased to welcome Viamedici on The Disruptive MDM/PIM/DQM List and thus also one of the innovative solutions to be on the 2022 version.

During the recent years I have followed Viamedici as a very interesting solution among those Product Information Management (PIM) vendors who are developing into multidomain Master Data Management (MDM) vendors.

Their PIM solution has some unique capabilities around managing complex products and real-time handling of large numbers of products, attributes, relations, and digital assets. These capabilities can be utilized to cover extended MDM where multidomain MDM goes beyond traditional customer, supplier, and product MDM.

You can learn more about Viamedici here.

How MDM Inflates

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Since the emerge of Master Data Management (MDM) back in 00’s this discipline has taken on more and more parts of the also evolving data management space.

The past

It started with Customer Data Integration (CDI) being addressing the common problem among many enterprises of having multiple data stores for customer master data leading to providing an inconsistent face to the customer and lack of oversight of customer interactions and insights.

In parallel a similar topic for product master data was addressed by Product Information Management (PIM). Along with the pains of having multiple data stores for product data the rise of ecommerce lead to a demand for handling much more detailed product data in structural way than before.

While PIM still exist as an adjacent discipline to MDM, CDI mutated into customer MDM covering more aspects than the pure integration and consolidation of customer master data as for example data enrichment, data stewardship and workflows. PIM has thrived either within, besides – or without – product MDM while supplier MDM also emerged as the third main master data domain.   

The present

Today many organizations – and the solution providers – either grow their MDM capabilities into a multidomain MDM concept or start the MDM journey with a multidomain MDM approach. Multidomain Master Data Management is usually perceived as the union of Customer MDM, Supplier MDM and Product MDM. It is. And it is much more than that as explained in the post What is Multidomain MDM?

As part of a cross-domain thinking some organizations – and solution providers – are already preparing for the inevitable business partner domain as pondered in the post The Intersection of Supplier MDM and Customer MDM.

The PIM discipline has got a subdiscipline called Product Data Syndication (PDS). While PIM basically is about how to collect, enrich, store, and publish product information within a given organization, PDS is about how to share product information between manufacturers, merchants, and marketplaces.

The future

Interenterprise MDM will be the inflated next stage of the business partner MDM and Product Data Syndication (PDS) theme. This is about how organizations can collaborate by sharing master data with business partners in order to optimize own master data and create new data driven revenue models together with business partners.

It is in my eyes one of the most promising trends in the MDM world. However, it is not going to happen tomorrow. The quest of breaking down internal data and knowledge silos within organizations around is still not completed in most enterprises. Nevertheless, there is a huge business opportunity to pursue for the enterprises who will be in the first wave of interenterprise data sharing through interenterprise MDM.

Extended MDM is the inflated next scope of taking other data domains than customer, supplier, and product under the MDM umbrella.

Reference Data Management (RDM) is increasingly covered by or adjacent to MDM solutions.

Also, we will see handling of locations, assets, business essential objects and other digital twins being much more intensive within the MDM discipline. Which entities that will be is industry specific. Examples from retail are warehouses, stores, and the equipment within those. Examples from pharma are own and affiliated plants, hospitals and other served medical facilities. Examples from manufacturing are plants, warehouses as well as the products, equipment and facilities where their produced products are used within.

The handling of all these kind of master data on the radar of a given organization will require interenterprise MDM collaboration with the involved business partners.

Organizations who succeed in extending the coverage of MDM approaches will be on the forefront in digital transformation.

Augmented MDM is the inflated next level of capabilities utilized in MDM as touched in the post The Gartner MDM MQ of December 2021 and Augmented MDM. It is a compilation of utilizing several trending technologies as Machine Learning (ML), Artificial Intelligence (AI), graph approaches as knowledge graph with the aim of automating MDM related processes.

Metadata management will play a wider and more essential role here not at least when augmented MDM and extended MDM is combined.    

Mastering this will play a crucial role in the future ability to launch competitive new digital services.

The Disruptive MDM/PIM/DQM List 2022: Reltio

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Today it is time to present the fourth vendor to be on The Disruptive MDM / PIM / DQM List in 2022. That is Reltio.

I have been following Reltio here on the blog since 2013 as this MDM vendor has grown from an entrepreneur to a recognized solution provider recently manifested as being a leader in the Forrester MDM Wave and receiving 120 M USD in funding last month.

Reltio is a multi-domain cloud-native MDM solution covering a broad range of MDM capabilities.

You can learn more about Reltio here.