MDM of Material and Parts Data

The Often-overlooked MDM Scenario

Most presentations of Master Data Management (MDM) solutions revolve around the scenario of having multiple data stores holding customer master data and the needed capabilities of federation and deduplication in the quest for getting a 360 degree of customers.

Another common scenario is the Product Information Management (PIM) theme, where the quest is to get a 360 degree of the products that is sold to the customers.

However, in for example the manufacturing sector there is a frequent and complex scenario around governing product master data before the products become sellable to customers.

In that scenario we usually use the term material as an alternative to product and we use the term parts for the products bought from suppliers as either components of a finished product, as materials used in Maintenance, Repair and Operation (MRO) and as other supplies.

MDM Capabilities for Material and Parts Data

Some of the key MDM capabilities needed in the material and parts data scenario are:

  • Auto-generated material descriptions which helps with identification and distinction between materials which leads to better utilization of the inventory. This capability can by the way also be used by merchants in reselling PIM scenarios as pondered in the post What’s in a Product Name?.
  • Advanced mass maintenance of material attributes which leads to improved accuracy and consistency and thereby better operational efficiency. Again, this capability is also useful in other MDM scenarios.
  • User friendly maintenance of complex material relationship structures as for example Bill of Material (BOM). This leads to less scrap and rework and improved compliance reporting. This capability can successfully be extended to other internally defined relationship structures in PIM and MDM.

Workflow Management and Data Governance

Handling material and parts master data is collaboration intensive with many business units involved as for example:

  • Procurement
  • Supply Chain / Logistics
  • Production
  • Research & Development
  • Finance

This means that operational efficiency can only be obtained through cross business unit workflows tailored to the data requirements and compliance obligations held by each business unit.

With the degree of enterprise data sharing needed this must be encompassed by data governance framework elements as:

  • Roles and responsibilities for data
  • Data policies and data standards according to business rules
  • Data quality measurement
  • A commonly shared business glossary

Solution Example: Master Data Online (MDO)

In my experience MDM of material and parts data is often done utilizing the ERP application with the shortcomings around data overview, workflow management and data governance that entail.

Therefore, it is good to see when an MDM solution that has the material and parts master data management covered as well as touched in the post Welcome Master Data Online (MDO) from Prospecta on The Disruptive MDM / PIM / DQM List.

The Master Data Online (MDO) solution was born around material and parts master data which is an refreshing exception from the many MDM solutions that were born either from the Customer Data Integration (CDI) solution branch or the Product Information Management (PIM) branch.

You can learn more about material and parts MDM at Prospecta and MDO here.

MDO Material and Parts

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.

MDM / PIM / DQM Resources

Last week The Resource List went live on The Disruptive MDM / PIM / DQM List.

The rationale behind the scaling up of this site is explained in the article Preaching Beyond the Choir in the MDM / PIM / DQM Space.

These months are in general a yearly peak for conferences, written content and webinars from solution and service providers in the Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) space. With the covid-19 crises conferences are postponed and therefore the content providers are now ramping up the online channel.

As one who would like to read, listen to and/or watch relevant content, it is hard to follow the stream of content being pushed from the individual providers every day.

The Resource List on this site is a compilation of white papers, ebooks, reports, podcasts, webinars and other content from potentially all the registered tool vendors and service providers on The Solution List and coming service list. The Resource List is divided into sections of topics. Here you can get a quick overview of the content available within the themes that matters to you right now.

The list of content and topics is growing.

Check out The Resource List here.

MDM PIM DQM resourcesPS: The next feature on the site is planned to be The Case Study List. Stay tuned.

How the Covid-19 Outbreak Can Change Data Management

From sitting at home these are my thoughts about how data management can be changed due to the current outbreak of the Covid-19 (Corona) virus and the longer-term behaviour impact after the pandemic hopefully will be over.

Ecommerce Will Grow Faster

Both households and organizations are buying more online and this trend is increasing due to the urge of keeping a distance between humans. The data management discipline that underpins well executed ecommerce is Product Information Management (PIM). We will see more organizations implementing PIM solutions and we must see more effective and less time-consuming ways of implementing PIM solutions.

Data Governance Should Mature Faster

The data governance discipline has until now been quite immature and data governance activities have been characterized by an endless row of offline meetings. As data governance is an imperative in PIM and any other data management quest, we must shape data governance frameworks that are more ready to use, and we must have online learning resources available for both professionals and participating knowledge workers with various roles.

Data Sharing Could Develop Faster

People, organizations and countries initially act in a selfish manner during a crisis, but we must realize that collaboration including data sharing is the only way forward. Hopefully we will see more widespread data sharing enterprise wide as this will ease remote working. Also, we could see increasing interenterprise (business ecosystem wide) data sharing which in particular will ease PIM implementations through automated Product Data Syndication (PDS).

Covid Data Management

Take Part in State of Data 2020

KDR Recruitment is a data management recruitment company and one of those rare recruitment agencies that genuinely express an interest in the disciplines covered.

This is manifested in among other things a yearly survey and report about the state of data that also was touched on this blog five years ago in the post Integration Matters.

This year the surveyed topics include for example how to use data analysis, new skills needed and the most effective ways to improve data quality. You can participate with your experience and observations here at State of Data 2020.

KDR state of data 2020

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.

Maturing RDM, MDM and ADM With Collaborative Data Governance

Data Governance and Master Data Management (MDM) are overlapping disciplines. When embarking on a data governance initiative you may encounter some difficulties in what belongs to the data governance side and what belongs to the master data side. One of the challenges is that data governance should also encompass other data than master data. The most common examples are reference data and other critical application data than master data.

So, while you may get coverage for setting up data stewardship, processes and the data platform for master data in a traditional MDM tool, other important aspects as the data governance related to Reference Data Management (RDM) and Application Data Management (ADM) may have to be implemented separately.

This calls for taking the MDM solution to the next level by encompassing reference data and application data as well. In that way essential data governance definition components as a business glossary, data policies and data standards as well as the enforcement components through data stewardship can be implemented in a collaborative way:

RDM MDM ADM

In this case the MDM platform will be extended to be an intelligent data hub. In collaboration with FX Nicolas I will be presenting such a solution in a webinar hosted by Semarchy. The webinar goes live Wednesday 13th November at 5pm CET / 11am ET. Register here on Intelligent Data Hub: MDM and Beyond.

How Data Discovery Makes a Data Hub More Valuable

Data discovery is emerging as an essential discipline in the data management space as explained in the post The Role of Data Discovery in Data Management.

In a data hub encompassing master data, reference data, critical application data and more, data discovery can play a significant role in the continuous improvement of data quality and how data is governed, managed and measured along with an ever evolving business model and new data driven services.

Data discovery serves as the weapon used when exploring the as-is data landscape at your organization with the aim of building a data hub that reflects your data model and data portfolio. As the data maturity is continuously improved reflected in step-by-step maturing to-be states, data discovery can be used when increasing the data hub scope by encompassing more data sources, when new data driven services are introduced and the business model is enhanced as part of a digital transformation.

Data Discovery Outcome

In that way data discovery is an indispensable node in maturing the data supply chain and the continuously data quality improvement cycle that must underpin your digital transformation course.

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

Unifying Data Quality Management, MDM and Data Governance

During the end of last century data quality management started to gain traction as organizations realized that the many different applications and related data stores in operation needed some form of hygiene. Data cleansing and data matching (aka deduplication) tools were introduced.

In the 00’s Master Data Management (MDM) arised as a discipline encompassing the required processes and the technology platforms you need to have to ensure a sustainable level of data quality in the master data used across many applications and data stores. The first MDM implementations were focused on a single master data domain – typically customer or product. Then multidomain MDM (embracing customer and other party master data, location, product and assets) has become mainstream and we see multienterprise MDM in the horizon, where master data will be shared in business ecosystems.

MDM also have some side disciplines as Product Information Management (PIM), Digital Asset Management (DAM) and Reference Data Management (RDM). Sharing of product information and related digital assets in business ecosystems is here supported by Product Data Syndication.

Lately data governance has become a household term. We see multiple varying data governance frameworks addressing data stewardship, data policies, standards and business glossaries. In my eyes data governance and data governance frameworks is very much about adding the people side to the processes and technology we have matured in MDM and Data Quality Management (DQM). And we need to combine those themes, because It is not all about People or Processes or Technology. It is about unifying all this.

In my daily work I help both tool providers and end user organisations with all this as shown on the page Popular Offerings.

DG DQ and MDM

 

Top 15 MDM / PIM Requirements in RFPs

A Request for Proposal (RFP) process for a Master Data Management (MDM) and/or Product Information Management (PIM) solution has a hard fact side as well as there are The Soft Sides of MDM and PIM RFPs.

The hard fact side is the detailed requirements a potential vendor has to answer to in what in most cases is the excel sheet the buying organization has prepared – often with the extensive help from a consultancy.

Here are what I have seen as the most frequently included topics for the hard facts in such RFPs:

  • MDM and PIM: Does the solution have functionality for hierarchy management?
  • MDM and PIM: Does the solution have workflow management included?
  • MDM and PIM: Does the solution support versioning of master data / product information?
  • MDM and PIM: Does the solution allow to tailor the data model in a flexible way?
  • MDM and PIM: Does the solution handle master data / product information in multiple languages / character sets / script systems?
  • MDM and PIM: Does the solution have capabilities for (high speed) batch import / export and real-time integration (APIs)?
  • MDM and PIM: Does the solution have capabilities within data governance / data stewardship?
  • MDM and PIM: Does the solution integrate with “a specific application”? – most commonly SAP, MS CRM/ERPs, SalesForce?
  • MDM: Does the solution handle multiple domains, for example customer, vendor/supplier, employee, product and asset?
  • MDM: Does the solution provide data matching / deduplication functionality and formation of golden records?
  • MDM: Does the solution have integration with third-party data providers for example business directories (Dun & Bradstreet / National registries) and address verification services?
  • MDM: Does the solution underpin compliance rules as for example data privacy and data protection regulations as in GDPR / other regimes?
  • PIM: Does the solution support product classification and attribution standards as eClass, ETIM (or other industry specific / national standards)?
  • PIM: Does the solution support publishing to popular marketplaces (form of outgoing Product Data Syndication)?
  • PIM: Does the solution have a functionality to ease collection of product information from suppliers (incoming Product Data Syndication)?

Learn more about how I can help in the blog page about MDM / PIM Tool Selection Consultancy.

MDM PIM RFP Wordle