Disciplines come and go in the data management world. Here is a mind map of the disciplines on top of my mind today. Some of the disciplines goes back to the emerge of IT in the previous millennium and some have risen during the latest years.
I am sad to hear that Larry English has passed away as I learned from this LinkedIn update by C. Lwanga Yonke.
As said in here: “When the story of Information Quality Management is written, the first sentence of the first paragraph will include the name Larry English”.
Larry pioneered the data quality – or information quality as he preferred to coin it – discipline.
He was an inspiration to many data and information quality practitioners back in the 90’s and 00’s, including me, and he paved the way for bringing this topic to the level of awareness that it has today.
In his teaching Larry emphasized on the simple but powerful concepts which are the foundation of data quality and information quality methodologies:
Let us roll up our sleeves and continue what Larry started.
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.
In 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.
Any implementation of a Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) solution will need a business case to tell if the intended solution has a positive business outcome.
Prior to the solution selection you will typically have:
The solution selection (jump-starting with the Disruptive MDM / PIM / DQM Select Your Solution service) will then inform you about the Total Cost of Ownership (TCO) of the best fit solution(s).
From here you can, put very simple, calculate the Return of Investment (ROI) by withdrawing the TCO from the estimated financial results.
You can check out more inspiration about ROI and other business case considerations on The Disruptive MDM / PIM /DQM Resource List.
One of the major players on the data quality market, Experian, do a yearly survey of the current data management trends. This year is no exception and I just had the chance to read through the 2020 report.
This year’s report revolves around trusted data, data debt and the skills gap in the light of data literacy. As always, the report holds some good percentage take away you can use in your data quality evangelism.
My favourite this year is a bit tricky:
I think this one shows a challenging side of data quality evangelism. While operational efficiency is a bit ahead of other reasons to improve data quality, there are many good reasons to improve data quality. And advocating for every kind of goodness is often harder than being able to pinpoint one absolutely good reason.
Well, see for yourself. Get the 2020 Global data management research from Experian Data Quality here.
The Disruptive MDM / PIM / DQM List was launched in the late 2017.
Here the first innovative Master Data Management (MDM) and Product Information Management (PIM) tool vendors joined the list with a presentation page showcasing the unique capabilities offered to the market.
The blog was launched at the same time. Since then, a lot of blog posts – including guest blog posts – have been posted. The topics covered have been about the list, the analysts and their market reports as well as the capabilities that are essential in solutions and their implementation.
In 2019 the MDM and PIM tool vendors were joined by some of the forward-looking best-of-breed Data Quality Management (DQM) tool vendors.
The Select Your Solution service was launched at the same time. Here organizations – and their consultants – who are on the look for a MDM / PIM / DQM solution can jumpstart the selection process by getting a list of the best solutions based on their individual context, scope and requirements. More than 100 hundred end user organizations or their consultants have received such a list.
Going into the 20es the list is ready to be scaled up. The new sections being launched are:
If you have questions and/or suggestions for valuable online content on the list, make a comment or get in contact here:
Analyst firms occasionally publish market reports with solution overview for Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM).
The publication schedule from the analyst firms can be unpredictable.
Information Difference is an exception. There have during the years every year been a Data Quality landscape named Q1 and published shortly after that quarter and an MDM landscape named Q2 and published shortly after that quarter. However, these reports are relying on participation from relevant vendors and not all vendors prioritize this scheme.
Forrester is quite unpredictable both with timing and which market segments (MDM, PIM, DQM) to be covered.
Gartner is a bit steadier. However, for example the MDM solution reports have been coming in varying intervals during the latest years.
Here is an overview of the latest major reports:
Stay tuned on this blog to get the latest on analyst reports and news on market movements.
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.
If you search on Google for “data quality” you will find the ever-recurring discussion on how we can define data quality.
This is also true for the top ranked none sponsored articles as the Wikipedia page on data quality and an article from Profisee called Data Quality – What, Why, How, 10 Best Practices & More!
The two predominant definitions are that data is of high quality if the data:
Personally, I think it is a balance.
In theory I am on the right side. This is probably because I most often work with master data, where the same data have multiple purposes.
However, as a consultant helping organizations with getting the funding in place and getting the data quality improvement done within time and budget I do end up on the other side.
What about you? Where do you stand in this question?
TLA stands for Three Letter Acronym. The world is full of TLAs. The IT world is full of TLAs. The Data Management world is full of TLAs. Here are 10 TLAs from the data management world that have been mentioned a lot of times on this blog and the sister blog over at The Disruptive MDM / PIM / DQM List:
MDM = Master Data Management can be defined as a comprehensive method of enabling an enterprise to link all of its critical data to a common point of reference. When properly done, MDM improves data quality, while streamlining data sharing across personnel and departments. In addition, MDM can facilitate computing in multiple system architectures, platforms and applications. You can find the source of this definition and 3 other – somewhat similar – definitions in the post 4 MDM Definitions: Which One is the Best?
PIM = Product Information Management is a discipline that overlaps MDM. In PIM you focus on product master data and a long tail of specific product information related to each given classification of products. This data is used in omni-channel scenarios to ensure that the products you sell are presented with consistent, complete and accurate data. Learn more in the post Five Product Information Management Core Aspects.
DAM = Digital Asset Management is about handling rich media files often related to master data and especially product information. The digital assets can be photos of people and places, product images, line drawings, brochures, videos and much more. You can learn more about how these first 3 mentioned TLAs are connected in the post How MDM, PIM and DAM Stick Together.
DQM = Data Quality Management is dealing with assessing and improving the quality of data in order to make your business more competitive. It is about making data fit for the intended (multiple) purpose(s) of use which most often is best to achieved by real-world alignment. It is about people, processes and technology. When it comes to technology there are different implementations as told in the post DQM Tools In and Around MDM Tools.
RDM = Reference Data Management encompass those typically smaller lists of data records that are referenced by master data and transaction data. These lists do not change often. They tend to be externally defined but can also be internally defined within each organization. Learn more in the post What is Reference Data Management (RDM)?
CDI = Customer Data Integration, which is considered as the predecessor to MDM, as the first MDMish solutions focussed on federating customer master data handled in multiple applications across the IT landscape within an enterprise. You may ask: What Happened to CDI?
CDP = Customer Data Platform is an emerging kind of solution that provides a centralized registry of all data related to parties regarded as (prospective) customers at an enterprise. Right now, we see such solutions coming both from MDM solution vendors and CRM vendors as reported in the post CDP: Is that part of CRM or MDM?
ADM = Application Data Management, which is about not just master data, but all critical data however limited to a single (suite of) application(s) at the time. ADM is an emerging term and we still do not have a well-defined market as examined in the post Who are the ADM Solution Providers?
PXM = Product eXperience Management is another emerging term that describes a trend to distance some PIM solutions from the MDM flavour and more towards digital experience / customer experience themes. Read more about it in the post What is PxM?
PDS = Product Data Syndication, which connects MDM, PIM (and other) solutions at each trading partner with each other within business ecosystems. As this is an area where we can expect future growth along with the digital transformation theme, you can get the details in the post What is Product Data Syndication (PDS)?
Liliendahl on Data Quality 