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.

Three Not So Easy Steps to a 360-Degree Customer View

Getting a 360-degree view (or single view) of your customers has been a quest in data management as long as I can remember.

This has been the (unfulfilled) promise of CRM applications since they emerged 25 years ago. Data quality tools has been very much about deduplication of customer records. Customer Data Integration (CDI) and the first Master Data Management (MDM) platforms were aimed at that conundrum. Now we see the notion of a Customer Data Platform (CDP) getting traction.

There are three basic steps in getting a 360-degree view of those parties that have a customer role within your organization – and these steps are not at all easy ones:

360 Degree Customer View

  • Step 1 is identifying those customer records that typically are scattered around in the multiple systems that make up your system landscape. You can do that (endlessly) by hand, using the very different deduplication functionality that comes with ERP, CRM and other applications, using a best-of-breed data quality tool or the data matching capabilities built into MDM platforms. Doing this with adequate results takes a lot as pondered in the post Data Matching and Real-World Alignment.
  • Step 2 is finding out which data records and data elements that survives as the single source of truth. This is something a data quality tool can help with but best done within an MDM platform. The three main options for that are examined in the post Three Master Data Survivorship Approaches.
  • Step 3 is gathering all data besides the master data and relate those data to the master data entity that identifies and describes the real-world entity with a customer role. Today we see both CRM solution vendors and MDM solution vendors offering the technology to enable that as told in the post CDP: Is that part of CRM or MDM?

Product Data in ERP, MDM and PIM

Organizations typically holds product data in three different kind of applications:

  • In an ERP application together with all other kinds of master data and transaction data.
  • In an MDM (Master Data Management) application either as a Product MDM implementation or a multidomain MDM implementation together with other master data domains.
  • In a PIM (Product Information Management) application.

Each of these applications have their pros and cons and where an organization utilizes several of these applications we often see that there is no single source of truth for all product data, but that some product attributes are controlled by one application and some other attributes are controlled by another application. Recently I wrote a post on the Pimnews think forum with a walk through of different kinds of product attributes and if they typically are controlled in PIM or ERP / MDM. The post had the title Six Product Attribute Levels.

The overwhelming part of organizations still use ERP as the place for product data – often supplemented by satellite spreadsheets with product data.

However, more and more organizations, not at least larger global ones, are implementing MDM solutions and, also midsize organisations, are implementing PIM solutions. The solution market was before split between MDM and PIM solutions, but we now do see some of the PIM solution providers also encompassing MDM capabilities. On the Disruptive MDM/PIM List there is a selection of solutions either being more MDM-ish or more PIM-ish as examined in the post MDM, PIM or Both.

MDM ish and PIM ish vendors

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

CDP: Is that part of CRM or MDM?

The notion of a data centred application type called a Customer Data Platform (CDP) seems to be trending these days. A CDP solution is a centralized registry of all data related to parties regarded as (prospective) customers at an enterprise.

This kind of solution comes from two solution markets:

  • Customer Relationship Management (CRM)
  • Master Data Management (MDM)

The CRM track was recently covered in a Venture Beat article telling that Salesforce announces a Customer Data Platform to unify all marketing data. In this article it is also stated that Oracle just announced a similar solution named CX Unity and Adobe announced triggered journeys based on a rich pool of centralized data.

Add to that last year´s announcement from Microsoft, Adobe and SAP on their Open Data Initiative as told in the LinkedIn article Using a Data Lake for Data Sharing.

Some MDM solution providers are also on that track. Reltio Cloud embraces all customer data and Informatica Customer 360 Insights, formerly known as Allsight, is also going there as reported in the post Extended MDM Platforms.

Will be interesting to follow how CDP solutions evolve and if it is CRM or MDM vendors who will do best in this discipline. One guess could be that MDM vendors will provide “the best” solutions but CRM vendors will sell most licenses. We will see.

CDP CRM MDM

10 Years

This blog has now been online for 10 years.

pont_du_gard
Pont du Gard

Looking back at the first blog posts I think the themes touched are still valid.

The first post from June 2009 was about data architecture. 2000 years ago, the roman writer, architect and engineer Marcus Vitruvius Pollio wrote that a structure must exhibit the three qualities of firmitas, utilitas, venustas — that is, it must be strong or durable, useful, and beautiful. This is true today – both in architecture and data architecture – as told in the post Qualities in Data Architecture.

A recurring topic on this blog has been a discussion around the common definition of data quality as being that the data is fit for the intended purpose of use. The opening of this topic as made in the post Fit for what purpose?

brueghel-tower-of-babel
Tower of Babel by Brueghel

Diversity in data quality has been another repeating topic. Several old tales including in the Genesis and the Qur’an have stories about a great tower built by mankind at a time with a single language of all people. Since then mankind was confused by having multiple languages. And indeed, we still are as pondered in the post The Tower of Babel.

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.

greatbeltbridge
Great Belt Bridge

RDM: A Small but Important Extension to MDM

Reference Data Management (RDM) is a small but important extension to Master Data Management (MDM). Together with a large extension, being big data and data lakes, mastering reference data is increasingly being part of the offerings from MDM solution vendors as told in the post Extended MDM Platforms.

RDM

Reference Data

Reference data are these smaller lists of values that gives context to master data and ensures that we use the same (or linkable) codes for describing master data entities. Examples are:

Reference data tend to be externally defined and maintained typically by international standardization bodies or industry organizations, but reference data can also be internally defined to meet your specific business model.

3 RDM Solutions from MDM Vendors

Informatica has recently released their first version of a new RDM solution: MDM – Reference 360. This is by the way the first true Software as a Service (SaaS) solution from Informatica in the MDM space. This solution emphasizes on building a hierarchy of reference data lists, the ability to make crosswalks between the lists, workflow (approval) around updates and audit trails.

Reltio has embraced RDM has an integral part of their Reltio Cloud solution where the “RDM capabilities improves data governance and operational excellence with an easy to use application that creates, manages and provisions reference data for better reporting and analytics.

Semarchy has a solution called Semarchy xDM. The x indicates that this solution encompasses all kinds of enterprise grade data and thus both Master data and Reference data while “xDM extends the agile development concept to its implementation paradigm”.

Data Modelling and Data Quality

There are intersections between data modelling and data quality. In examining those we can use a data quality mind map published recently on this blog:

Data modelling and data quality

Data Modelling and Data Quality Dimensions:

Some data quality dimensions are closely related to data modelling and a given data model can impact these data quality dimensions. This is the case for:

  • Data integrity, as the relationship rules in a traditional entity-relation based data model fosters the integrity of the data controlled in databases. The weak sides are, that sometimes these rules are too rigid to describe actual real-world entities and that the integrity across several databases is not covered. To discover the latter one, we may use data profiling methods.
  • Data validity, as field definitions and relationship rules controls that only data that is considered valid can enter the database.

Some other data quality dimensions must be solved with either extended data models and/or alternative methodologies. This is the case for:

  • Data completeness:
    • A common scenario is that for example a data model born in the United States will set the state field within an address as mandatory and probably to accept only a value from a reference list of 50 states. This will not work in the rest of world. So, in order to not getting crap or not getting data at all, you will either need to extend the model or loosening the model and control completeness otherwise.
    • With data about products the big pain is that different groups of products require different data elements. This can be solved with a very granular data model – with possible performance issues, or a very customized data model – with scalability and other issues as a result.
  • Data uniqueness: A common scenario here is that names and addresses can be spelled in many ways despite that they reflect the same real-world entity. We can use identity resolution (and data matching) to detect this and then model how we link data records with real world duplicates together in a looser or tighter way.

Emerging technologies:

Some of the emerging technologies in the data storing realm are presenting new ways of solving the challenges we have with data quality and traditional entity-relationship based data models.

Graph databases and document databases allows for describing and operating data models better aligned with the real world. This topic was examined in the post Encompassing Relational, Document and Graph the Best Way.

In the Product Data Lake venture I am working with right now we are also aiming to solve the data integrity, data validity and data completeness issues with product data (or product information if you like) using these emerging technologies. This includes solving issues with geographical diversity and varying completeness requirements through a granular data model that is scalable, not only seen within a given company but also across a whole business ecosystem encompassing many enterprises belonging to the same (data) supply chain.