2022 Data Management Predictions

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

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

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

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

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 Gartner MDM MQ of December 2021 and Augmented MDM

The latest Gartner Magic Quadrant for Master Data Management Solutions is out.

If we look at the vendor positioning not much has happened since the January 2021 quadrant. Tamr is a new solution in the quadrant while Cluedin and Pimcore are now listed under honorable mentions. Informatica is as always closest to the top-right corner.

Augmented data management and augmented MDM are the new main buzzwords as seen in the strategic planning assumptions. Also, the vendors capabilities for doing the augmented stuff are stated as strengths and cautions.

So, what is this augmented data management and augmented MDM by the way? It is in short a compilation of utilizing several trending technologies as Machine Learning (ML), Artificial Intelligence (AI), graph approaches (as examined in the post MDM and Knowledge Graph) with the aim of automating and scaling data management including MDM.

If this will be the final shape of whatever augmented MDM is though in my eyes a bit unsure and even Gartner has doomed augmented MDM to be obsolete before reaching the plateau of productivity in their latest hype cycle for Data and Analytics Governance and Master Data Management. Anyway, I am sure we will see some form of extended MDM covering more domains than customer, supplier and product and utilizing the emerging technologies.

If you want to read the full Gartner report for free, you can get it from Informatica here.

The Disruptive MDM/PIM/DQM List 2022: Reltio

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.

MDM and Knowledge Graph

As examined in a previous post with the title Data Fabric and Master Data Management, the use of the knowledge graph approach is on the rise.

Utilizing a knowledge graph has an overlap with Master Data Management (MDM).

If we go back 10 years MDM and Data Quality Management had a small niche discipline that was called (among other things) entity resolution as explored in the post Non-Obvious Entity Relationship Awareness. The aim of this was the same that today can be delivered in a much larger scale using knowledge graph technology.

During the past decade there have been examples of using graph technology for MDM as for example mentioned in the post Takeaways from MDM Summit Europe 2016. However, most attempts to combine MDM and graph have been to visualize the relationships in MDM using a graph presentation.

When utilizing knowledge graph approaches you will be able to detect many more relationships than those that are currently managed in MDM. This fact is the foundation for a successful co-existence between MDM and knowledge graph with these synergies:

  • MDM hubs can enrich knowledge graph with proven descriptions of the entities that are the nodes (vertices) in the knowledge graph.
  • Additional detected relationships (edges) and entities (nodes) from the knowledge graph that are of operational and/or general analytic interest enterprise wide can be proven and managed in MDM.

In this way you can create new business benefits from both MDM and knowledge graph.

The Disruptive MDM/PIM/DQM List 2022: Magnitude Software

In the round of presenting the solutions for The Disruptive MDM / PIM / DQM List 2022 the next vendor is Magnitude Software.

Magnitude Software has two solutions on the list:

  • Kalido MDM where you can define and model critical business information from any domain – customer, product, financial, vendor, supplier, location and more – to create and manage accurate, integrated, and governed data that business users trust.
  • Agility Multichannel PIM which has the capabilities to get products to market faster with a simple-to-use, comprehensive Product Information Management solution that makes it easy to support commerce across digital and traditional channels.

Learn more about Kalido MDM here and Agility Multichannel PIM here.

Data Fabric and Master Data Management

Data fabric has been named a key strategic technology trend in 2022 by Gartner, the analyst firm.

According to Gartner, “by 2024, data fabric deployments will quadruple efficiency in data utilization while cutting human-driven data management tasks in half”.

Master Data Management (MDM) and data fabric are overlapping disciplines as examined in the post Data Fabric vs MDM. I have seen data strategies where MDM is put as a subset to data fabric and data strategies where they are separate tracks.

In my head, there is a common theme being data sharing.

Then there is a different focus, where data fabric seems to be focusing on data integration. MDM is also about data integration, but more about data quality. Data fabric takes care of all data while MDM obviously is about master data, though the coverage of business entities within MDM seems to be broadening.

Another term closely tied to data fabric – and increasingly with MDM as well – is knowledge graph. Knowledge graph is usually considered a mean to achieve a good state of data fabric. In the same way you can use a knowledge graph approach to achieve a good state of MDM when it comes to managing relationships – if you include a data quality facet.

What is your take on data fabric and MDM?