The solution selection service on this site started 3 months ago as told here.
Since then Master Data Management (MDM) and Product Information Management (PIM) solutions have been joined by Data Quality Management (DQM) solutions, where some of the most innovative DQM solutions have joined the listing on this site.
More than 50 requesters have provided information about the context, scope and requirements of their intended solution and based on that received a report telling:
- Which solution that is the best fit for a direct proof of concept
- Which 3 solutions that are the best fit for a shortlist of solutions
- Which 7 solutions that are the best fit for a longlist of solutions
Depending on your organization’s rules and the circumstances of your solution selection this report is aimed to jumpstart your selection process using one of the above selections.
The requesters of this report that have given feedback have provided positive responses as told in the post about the First Experiences with the MDM / PIM Solution Selection Service.
The service is still free. Start here.
The upcoming Black Friday and Cyber Monday are synonymous with good deals.
At the Disruptive MDM / PIM / DQM List there are good deals all the days.
As a potential buyer on the look for a solution covering your Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) needs you can use the free service that based on your context, scope and requirement selects the best fit solution(s). You can start here.
As a solution provider you can against a very modest fee register your solution here.
Happy Black Friday and Cyber Monday.
One of the hottest trends in the Master Data Management (MDM) world today is how to exploit Artificial Intelligence (AI) and ignite that with Machine Learning (ML).
This aspiration is not new. It has been something that have been going on for years and you may argue about when computerized decision support and automation goes from being applying advanced algorithms to being AI. However, the AI and ML theme is getting traction today as part of digital transformation and whatever we call it, there are substantial business outcomes to pursue.
As told in the post Machine Learning, Artificial Intelligence and Data Quality perhaps all use cases for applying AI is dependent on data quality and MDM is playing a crucial role in sustaining data quality efforts.
Here are six use cases that are commonly being addressed by AI and ML capabilities:
- Translating between taxonomies: As reported in the post Artificial Intelligence (AI) and Multienterprise MDM emerging technologies can help in translating between the taxonomies in use when digital transformation sets a new bar for utilizing master data in business ecosystems.
- Transforming unstructured to structured: A lot of data is kept in an unstructured way and to in order to systematically exploit these data in AI supported business process we need make data more structured. AI and ML can help with that too.
- Data quality issue prevention: Simple rules for checking integrity and validating data is good – but unfortunately not good enough for ensuring data quality. AI is a way to exploit statistical methods and complex relationships.
- Categorizing data: Digital transformation, spiced up with increasing compliance requirements, has made data categorization a must and AI and ML can be an effective way to solve this task that usually is not possible for humans to cover across an enterprise.
- Data matching: Establishing a link between multiple descriptions of the same real-world entity across an enterprise and out to third party reference data has always been a pain. AI and ML can help as examined in the post The Art in Data Matching.
- Improving insight: The scope of MDM can be enlarged to Extended MDM Platforms where other data as transactions and big data is used to build a 360-degree view of the master data entities. AI and ML is a prerequisite to do that.
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 the 10 TLAs from the data management world that covers the solutions encompassed on this 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)?
New terms are constantly emerging in the data management space. One of these are “Data Fabric”.
According to Gartner, the analyst firm, data fabric “enables frictionless access and sharing of data in a distributed network environment.” Usually, one would associate data fabric with big data and edge computing. However, data fabric does embrace all kind of data and computing from the ones mentioned over multi-cloud to traditional on-premise computing and the data stores within.
Data fabric and Master Data Management (MDM) have the same aim, which is that all (master) data must be shared across the enterprise – and eventually also in business ecosystems. This is a prerequisite for successful digital transformation.
Lately, there has also been a development in the conception of MDM, where other data than master data are encompassed in some of the platforms offered as examined in the post Master Data, Product Information, Reference Data and Other Data.
So, there is clearly a union and an intersection of data fabric and MDM.