What You Should Know About Master Data Management

Today’s guest blog post is from Benjamin Cutler of Winpure. In here Benjamin goes through a few things that you in a nutshell should know about master data management.


People have multiple phone numbers and multiple email addresses and in 2022 there must be several decades of historic contact information available for any one person. Most of us move at least once, every few years. Sometimes we go by different nicknames in different situations, some people even change their names. We hold different titles throughout the course of our careers and we change companies every few years. Only a few people in our lives know exactly how to get a hold of us, at any given time. Many of us change vehicles just as often as we change our hair color. Many of us are employees, most of us are also customers, many of us are spouses and sometimes we are grandparents, parents, aunts, uncles, and children at the same time. Sometimes we’re out enjoying ourselves and sometimes we just want to be left alone. We each have unique interests and desires, but we also have many things in common with other groups of people.


Products have many different descriptions, they come in many different variations, different sizes, different colors, and different packaging materials. Similar products are often manufactured by different manufacturers, and they can be purchased from many different commercial outlets, at different price points. Any one product on the market at any one time will likely be available in several variations, but that product will also likely change over time as the manufacturer makes improvements. Products can be purchased therefore they can also be sold. They can also be returned or resold to other buyers, so there are different conditions and ways to determine product value. There are SKU and UPC numbers and other official product identification and categorization systems including UNSPSC and others, but none of them speak the same language.


Companies are made up of many different people who come and go over time. The company may change names or change ownership. It may have multiple locations which means multiple addresses and phone numbers, and they probably offer many different ways to contact them. Depending on where you look, there are probably more than a dozen different ways to find their contact information, but only some of those company listings will be correct. Companies have tax IDs and Employer IDs and DUNS IDs in the US, and there are many different systems worldwide.


Addresses are the systems we use to identify locations. Each country and territory has its own system so each system is different. In the US we use premise numbers, street names with and without street prefixes and suffixes, we use unit numbers, states, counties, cities, towns and 5 and 9 digital numerical postal codes. Addresses and address systems can change over time, and they are inherently one of the most inconsistent forms of identification. Addresses are usually riddled with errors, misspellings, different structures and formatting, and they can be very difficult to work with. What makes this even more difficult is that the same address represented in multiple internal business systems will often be represented differently, and will rarely match the way the same address is represented externally.


Data is a digital description of all of these things. Data usually comes in columns and rows and all shapes and sizes. Data about these things is captured, stored in business systems and it’s used to get work done. Need to call a contact? Check your contact data. Need to know a company’s billing address? Check your company data. Need to know something about a product? Check your product information. Need to know something about where your customers live and work or where to deliver the product? Check your address information. But here’s the thing: the information rarely matches from system to system and it’s very hard to keep up to date. This is especially difficult for a few reasons. Internally your company probably has many different business systems and many different ways of storing and representing these things, so it rarely matches internally, plus, the way that your company stores and represents this information will almost never match external information. How can you know the best way to contact your customer who has multiple phone numbers and multiple email addresses? If you’re searching some external system for updated information about some product or contact and the information doesn’t match, how do you find the new information? How can you know if your own information is correct and up to date? How can you scale your efforts to communicate with hundreds or thousands of customers at a time, communicating information that is specifically relevant for each of them? If the information doesn’t match or is not correct, how can you know who is who?


The relationships across people, other groups of people, products, other groups of products, companies, other groups of companies, addresses, and other addresses, is where the rubber hits the road. Business value comes from connecting companies and products or services with other people and companies, and other products and services, at scale. Customers purchasing products might be interested in purchasing related products. Customers often buy things based on location. Companies selling to customers might be able to sell more, if they target similar customers in similar locations. Products and services also sell well based on location, and companies can optimize sales territories and delivery routes based on the relative proximity to other locations.

People and Technology

The people and technology between all of this, finds it difficult to keep up. People do things one by one and we’re good with ambiguity. We program computers and business systems to do things faster. Computers do things programmatically and very quickly but they’re not good with ambiguity. People can see similarity between things that are similar, but computers and business systems cannot. People might be good with troubleshooting and critical thinking, but computers and business systems are not. A computer program might be able to find the same customer in multiple systems and might be able to update that customer’s information all at once, but how can you know if the new information is the best information? Knowing that your customer probably has multiple phone numbers and multiple addresses and multiple nicknames, how can you know which information is correct? Doing this at scale can be very, very difficult.

In Conclusion

Master Data Management is very difficult but it’s fundamental in scaling your business. People can sell products door-to-door, but data and technology allow us to market, sell, deliver, and service our products and services, to tens and hundreds of thousands of people in milliseconds, regardless of the distance. Most organizations still view data as a cost of doing business but with the right investments in people, process, technology, and in data management, we can scale as worldwide organizations.

Popular Entries on The Resource List

This site has a list of white papers, ebooks, reports and webinars from solution and service providers.

The aim is to give inspiration for organizations having the quest to implement or upgrade their Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) capability.

The list has now been online in a month and it is time to look at which entries that until now have been the most popular in terms of click through. These are:


Exploring The ROI of PIM and MDMHave you ever wondered how to effectively evaluate the return on investment (ROI) of a Product Information Management (PIM) and Master Data Management (MDM) implementation? Then, take a look at some real-life examples. Download the Enterworks ebook on Exploring The ROI of PIM and MDM.

MDM, PIM and DQM market overview

The State of Product Information Management 2020Get an overview of why PIM solutions are implemented in more and more organizations, which capabilities a 2020 PIM solution needs to cover, where the market is heading and who the PIM vendors in the market are and how this affect your purchase of PIM. Download the Dynamicweb PIM white paper The State of Product Information Management 2020. 

MDM, PIM and DQM implementation

virtual-conference-webcast-revConferences cancelled? Stuck working from home? Bring the conferences to you with an virtual MDM conference. Don’t miss this must see 6 week live webcast series and hear what other companies are doing in the world of MDM along with best practices and workshops by industry experts.. Register for this Enterworks webcast series at the Everything Master Data Management (MDM) Virtual Conference.

Extended MDM

Intelligent Data Hub - Taking MDM to the Next LevelMDM solutions have been instrumental in solving core data quality issues in a traditional way, focusing primarily on simple master data entities such as customer or product. Organizations now face new challenges with broader and deeper data requirements to succeed in their digital transformation. Help your organization through a successful digital transformation while taking your MDM initiative to the next level. Download the Semarchy white paper Intelligent Data Hub – Taking MDM to the Next Level.

Data Quality

4 Keys to Unlocking Data Quality with MDMBusinesses today face a rapidly growing mountain of content and data. Mastering this content can unlock a whole new level of Business Intelligence for your organization and impact a range of data analytics. It’s also crucial for operational excellence and digital transformation. Download the 1WorldSync and Enterworks ebook on 4 Keys to Unlocking Data Quality with MDM.

Next To Come

More resources from solution and service vendors are on the way. Additionally, there will also be a Case Story List with success stories from various industries. Stay tuned.

If you have comments, suggestions and/or entries to be posted (yes, there is a very modest fee), then get in touch here:


Interview with FX Nicolas, VP of Products at Semarchy

This site is a presentation of the best available Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) solutions. However, behind the technology there are people who is working hard to bring the best tools to live, break into the market and cover new land.

Semarchy was one of the first disruptive MDM solutions to join the list and FX was one of the first employees to join Semarchy.

FX NicolasFX, what was your path into Master Data Management?

It started with Data Integration. Back in 2000, we designed a data integration product called “Sunopsis”. It was the first solution to use an E-LT architecture. In fact, we created that acronym as a “smart data geek” joke, and it is now an established marketing buzzword. The product was acquired by Oracle and is still on the market under the Oracle Data Integrator name. I was Product Manager for Sunopsis and Oracle Data Integrator. As such I was exposed to the challenges of integration (real-time vs. batch, EAI vs. ETL, data quality, performance, etc.). Governance and MDM were not yet trendy terms, but we had the idea of a technical “Active Data Hub”, managed at the integration layer, to share high quality data between systems.

Semarchy was founded in 2011. How did the MDM market look like then?

A vast plain with two large circles of massive monoliths, namely the Customer Data Hub (CDH) and Product Information Management (PIM) verticals. Some of them were ironically sold by the same vendors who had failed to properly manage customer and product data in operational systems (CRMs, ERPs, etc.). When we looked at the market, we realized that there was room for domain-agnostic platforms to support customer, product, and every other domain.

The market was also a graveyard of failed projects. The reasons why so many projects ended up there were suspected (gigantic project scopes with insanely large timeframes, lack of agility and business involvement, etc.) but never clearly stated.

You have been in the forefront of introducing Semarchy. What have been the most difficult challenges in breaking into the market?

Building the platform was a challenge, but education was the hardest part. When you tell people that you know why they have failed or will fail, and you have a solution for a better outcome, they are not really willing to listen. We had to educate people with sound messages: “Yes sir, data quality is part of master data management”, “Start small and grow your initiative”, “Involve the business all along”.

Another challenge was the multiple number of shiny new trends and buzzwords popping up in the data space. Big data, cloud, graph, digital transformation, and now AI? Pick your favorite! Data management, governance, quality or workflows look very dull in comparison. The good thing is good practitioners know that these are prerequisites to get things done correctly.

Now that Semarchy has become an established player on the MDM market, what is the next move?

Since our inception, we’ve always believed in a single platform to solve all master data management issues. This all-in-one solution is still a dream for most companies, who struggle with four or five tools to manage their data. We are now ahead of that with our next move: extending our platform to be the end-to-end Intelligent Data Hub™. This includes new capabilities such as:

  • Data Discovery: Profiling data sources and learning about existing critical data assets.
  • Integration of any applications and leveraging any data source or service to enhance the enterprise data.
  • Governing the data hub by defining and enforcing business terms, processes, rules, policies, etc.
  • Managing data using apps designed for data champions and business users, with built-in data quality, match and merge, workflows, generated from the governance definitions and decisions.
  • Measuring the efficiency of the operations and the relevance of the governance choices using dashboards, KPIs and metrics based on data from the data hub or from external data sources.

Can you tell something more about how the Intelligent Data Hub is extending the MDM concept?

MDM is mainly about managing the domains core data assets (reference, customer, product, etc..) with data quality, match/merge and stewardship workflows. The data hub extends this idea in multiple directions:

  • It extends the scope of data available via the data hub beyond core master data, for example by eventually including transactional data and interactions to provide 360° views.
  • It takes an end-to-end approach for the data management initiative: from data governance, data onboarding with data discovery, profiling and cataloguing, down to the assessment of the value delivered with dashboards and KPIs.
  • It transparently opens the initiative to the whole enterprise. All business users become full members of the initiative via the data governance, data management and measurement channels. In short, the Intelligent Data Hub transforms every stakeholder in the organization into a data champion.

If you should have done something differently in Semarchy’s route to where you are now, what would that have been?

Go to the cloud from the beginning! Our platform is now available on major cloud platforms. If I had to do it again, I would have shipped the first version on premises *and* in the cloud.

Master Data Management Definitions: The A-Z of MDM. Part 3

This guest blog post is written by Justine Aa. Rodian of Stibo SystemsThe post is part 3 in a series of 3. Please find part 1 here and part 2 here.


Party data. In relation to Master Data Management, party data is understood in two different ways. First of all, party data can mean data defined by its source. You will typically hear about first, second and third-party data. First-party data being your own data, second-party data being someone else’s first-party data handed over to you, while third-party data is collected by someone with no relation to you—and probably sold to you. However, when talking about party data management, party data refers to master data typically about individuals and organizations with relation to, for example, customer master data. A party can in this context be understood as an attorney or husband of a customer that plays a role in a customer transaction, and party data is then data referring to these parties. Party data management can be part of an MDM setup, and these relations can be organized using hierarchy management.

Learn more about party data here.

PII. Personally Identifiable Information. In Europe often just referred to as personal information. PII is sensitive information that identifies a person, directly (on its own) or indirectly (in combination). Examples of direct PII include name, address, phone number, email address and passport number, while examples of indirect PII include a combination (e.g., workplace and job title or maiden name in combination with date and place of birth).

Product Information Management (PIM). Today sometimes also referred to as Product MDM, Product Data Management (PDM) or Master Data Management for products. No matter the naming, PIM refers to a set of processes used to centrally manage and evaluate, identify, store, share and distribute product data or information about products. PIM is enabled with the implementation of PIM or Product Master Data Management software.

Learn more here.

Product Lifecycle Management (PLM). The process of managing the entire lifecycle of a product from ideation, through design, product development, sourcing and selling. The backbone of PLM is a business system that can efficiently handle the product information full-circle, and significantly increase time to market through streamlined processes and collaboration. That can be a standalone PLM tool or part of a comprehensive MDM platform.

Learn more here.

Pool. A data pool is a centralized repository of data where trading partners (e.g., retailers, distributors or suppliers) can obtain, maintain and exchange information about products in a standard format. Suppliers can, for instance, upload data to a data pool that cooperating retailers can then receive through their data pool.

Platform. A comprehensive technology used as a base upon which other applications, processes or technologies are developed. An example of a software platform is an MDM platform.

Profiling. Data profiling is a technique used to examine data from an existing information source, such as a database, to determine its accuracy and completeness and share those findings through statistics or informative summaries. Conducting a thorough data profiling assessment in the beginning of a Master Data Management implementation is recognized as a vital first step toward gaining control over organizational data as it helps identify and address potential data issues, enabling architects to design a better solution and reduce project risk.


Quality. As in data quality, also sometimes just shortened into DQ. An undeniable part of any MDM vendor’s vocabulary as a high level of data quality is what a Master Data Management solution is constantly seeking to achieve and maintain. Data quality can be defined as a given data set’s ability to serve its intended purpose. In other words, if you have data quality, your data is capable of delivering the insight you require. Data quality is characterized by, for example, data accuracy, validity, reliability, completeness, granularity, consistency and availability.


Reference data. Data that define values relevant to cross-functional organizational transactions. Reference data management aims to effectively define data fields, such as units of measurements, fixed conversion rates and calendar structures, to “translate” these values into a common language in order to categorize data in a consistent way and secure data quality. Reference Data Management (RDM) systems can be the solution for some organizations, while others manage reference data as part of a comprehensive Master Data Management setup.


SaaS. Software as a Service. A software licensing and delivery model in which software is licensed on a subscription basis and is centrally hosted. SaaS is on the rise, due to change in consumer behavior and based on the higher demand for a more flat-rate pricing model, since these solutions are typically paid on a monthly or quarterly basis. SaaS is typically used in cloud MDM, for instance.

Supply Chain Management (SCM). The management of material and information flow in an organization—everything from product development, sourcing, production and logistics, as well as the information systems—to provide the highest degree of customer satisfaction, on time and at the lowest possible cost. A PLM solution or PLM MDM solution can be a critical factor for driving effective supply chain management.

Silos. When navigating the MDM landscape you will often come across the term data silos. A term describing when crucial data or information, such as master data, is held separately whether by individuals, departments, regions or systems. MDMs’ finest purpose is to “break down data silos.”

Stock Keeping Unit (SKU). A SKU represents an individual item, product or service manifested in a code, uniquely identifying that item, product or service. SKU codes are used in business to track inventory. It’s often a machine-readable bar code, providing an additional layer of uniqueness and identification.

Stack. The collection of software or technology that forms an organization’s operational infrastructure. The term stack is used in reference to software (software stack), technology (technology stack) or simply solution (solution stack) and refers to the underlying systems that make your business run smoothly. For instance, an MDM solution can—in combination with other solutions—be a crucial part of your software stack.

Stewardship. Data stewardship is the management and oversight of an organization’s data assets to help provide business users with high-quality data that is easily accessible in a consistent manner. Data stewards will often be the ones in an organization responsible for the day-to-day data governance.

Strategy. As with all major business initiatives, MDM needs a thorough, coherent, well-communicated business strategy in order to be as successful as possible.

Supplier data. Data about suppliers. One of the domains on which MDM can be beneficial. May be included in an MDM setup in combination with other domains, such as product data.

Learn more about supplier data here.

Synchronization. The operation or activity of two or more things at the same time or rate. Applied to data management, data synchronization is the process of establishing data consistency from one endpoint to another and continuously harmonizing the data over time. MDM can be the key enabler for global or local data synchronization.

Syndication. Data syndication is basically the onboarding of data provided from external sources, such as suppliers. An MDM solution will typically automate the process of receiving external data while making sure that high-quality criteria are met.

Swamp. A data swamp is a deteriorated data lake, that is inaccessible to its intended users and provides little value.


Training. No, not the type that goes on in a gym. Employee training, that is. MDM is not just about software. It’s about the people using the software, hence they need to know how to use it best in order to maximize the Return on Investment (ROI). MDM users will have to receive training from either the MDM vendor, consultants or from your employees who already have experience with the solution.


User Interface (UI). The part of the machine that handles the human–machine interaction. In an MDM solution—and in all other software solutions—users have an “entrance,” an interface from where they are interacting with and operating the solution. As is the case for all UIs, the UI in an MDM solution needs to be user-friendly and intuitive.


Vendor. There are many Master Data Management vendors on the market. How do you choose the right one? It all depends on your business needs, as each vendor is often specialized in some areas of MDM more than others. However, there are some things you generally should be aware of, such as scalability (Is the system expandable in order to grow with your business?), proven success (Does the vendor have solid references confirming the business value?) and integration (Does the solution integrate with the systems you need it to?).


Warehouse. A data warehouse—or EDW (Enterprise Data Warehouse)—is a central repository for corporate information and data derived from operational systems and external data sources, used to generate analytics and insight. In contrast to the data lake, a data warehouse stores vast amounts of typically structured data that is predefined before entering the data warehouse. The data warehouse is not a replacement for Master Data Management, as MDM can support the EDW by feeding reliable, high-quality data into the system. Once the data leaves the warehouse, it is often used to fuel Business Intelligence.

Workflow automation. An essential functionality in an MDM solution is the ability to set up workflows, a series of automated actions for steps in a business process. Preconfigured workflows in an MDM solution generate tasks, which are presented to the relevant business users. For instance, a workflow automation is able to notify the data steward of data errors and guide him through fixing the problem.


Yottabyte. Largest data storage unit (i.e., 1,000,000,000,000,000,000,000,000 bytes). No Master Data Management solution, or any other data storage solution, can handle this amount yet. But, scalability should be a considerable factor for which MDM solution you choose.


ZZZZZ… With a Master Data Management solution placed at the heart of your organization you get to sleep well at night, knowing your data processes are supported and your information can be trusted.

If you’d like the whole A-Z e-book in a downloadable format, please find it here.

Justine Aagaard Rodian is a marketing specialist at Stibo Systems with a background as a journalist. Five years in the data management industry has armed Justine with unique insights and she is now using her storytelling and digital skills to spread valuable business knowledge about Master Data Management and related topics.


Cloud multi-domain MDM as the foundation for Digital Transformation


Upen Varanasi

This guest blog post is an interview by Katie Fabiszak with CEO & Founder Upen Varanasi on the new MDM release from Riversand Technologies.

Riversand is pleased to announce the newest release of our cloud-native suite of Master Data Management solutions. Helping organizations create a data and analytics foundation to drive successful digital transformation was the driving factor behind Riversand’s decision to completely rethink and rebuild a master data platform that would meet the future needs of the digital era. According to Riversand CEO & Founder Upen Varanasi, “We knew we had to make an aggressive move to envision a new kind of data platform that was capable of handling the forces shaping our industry.” Katie Fabiszak, Riversand Global Vice President of Marketing, sat down with Upen to talk about Riversand’s vision of the future and how the bold decisions he had made several years ago led to the company’s own transformational journey and a new MDM solution:

KF:        Where do you think the market is heading?

UV:       Well, we are truly in the digital era. The markets are rewarding companies that are leaders in this digital landscape. This is forcing all companies to truly embrace digital transformation. A key aspect of this transformation is the role of data. Data is the new “fuel” of digital transformation. What matters is how enterprises can leverage data and unlock its power to create better and faster business outcomes. Turning data into insights is a key factor. Another important aspect is to understand the complexities of the information supply chain. Simplifying the flow of data is critical to drive better and faster business outcomes. Let’s take an example from the retail/CPG space: Enterprises want to better understand their customers (utilizing customer insights through AI), create/sell products and services that meet and exceed customer expectations (product positioning through AI), meet their customers across all touch points (channel management), secure their customer’s information (privacy), and interact with trading partners to creating cutting edge product differentiation (cloud and AI).

KF:        Why did Riversand decide to shift gears and embark on what became a 2+ year transformational journey for the company?

UV:       At Riversand, we understood the need to envision a new kind of platform to handle the trends occurring in our industry. We focused on creating a data-driven multi-domain Master Data Management System that has the following properties:

  • A contextual master data modeling environment.
  • A platform that can handle scale, velocity and variety of data, built to handle master data as well as transaction and interaction data.
  • Embedded AI to drive better insights and outcomes.
  • Built on cloud, with the ability to integrate data and processes across hybrid clouds.
  • A highly business-friendly user experience.
  • Apps built on the platform that can solve the “final mile” problem for business users. PIM and customer domain solutions are each apps built on this same platform.

Our goal was to create a platform and solutions that can be quick to implement, provide insights and recommended actions to business users, and automates many of the more mundane data management and stewardship functions. We believe we can provide Enterprises with the critical capabilities from the Master Data Management and Product Information Management space to differentiate themselves in the marketplace.

KF:        What are the core values of Riversand?

UV:       Our core values are innovation, commitment & integrity. We have consistently strived to bring leading edge innovation to the market. We have dared to step back at times in order to take a bigger leap forward. What helps is that we have had a long-term view of our company and the industry we play in. We have been in business for 18 years and have been committed to this space and to our customers all along the way. Integrity in everything we do is key in dealing with our stakeholders – employees, customers and partners – and has been our focus since inception. We might not get things right all the time to begin with, but we will always make it right with our stakeholders over time.

KF:        What do you see as Riversand’s biggest strength?

UV:       Our people, our passion for this space and our long-term thinking give us an incredible edge against our competition. We are blessed to have a team who have shaped this company over many years and who are invested deeply in the company and this market space.

Riversand picKF:        What would you say sets Riversand apart?  How is our technology and approach different than the other MDM and PIM vendors in the market?

UV:       The path we have chosen provides clear benefits for our customers with respect to our competition. Some key points of differentiation are:

  • We provide a single platform to help implement MDM and PIM initiatives. No need to create further silos of data and processes.
  • We future-proof both business users and IT users with a platform that is flexible and future enabled.
  • We are built for the cloud and SaaS eras: Upgrades are easy and done by us, and customers can scale with pay as you go models.
  • Our AI engine is built on a big data stack to drive insights and actions for better business outcomes.
  • With our big data technology stack, enterprises have the ability to scale with data and handle both its variety and velocity.
  • A completely new and extremely business friendly user interface and experience that people love to use!
  • The app building toolkit (SDK) to help customers and partners build their own apps so that they can solve their final mile problems: This is in addition to the core apps we are actively building.

KF:        Where is Riversand today along this journey that we began over 2 years ago?

UV:       Our new solutions were introduced as a kind of soft launch with a select few customers over the past year. The feedback and insights we received during this year were extremely useful in becoming enterprise-ready. We are now in a position to launch our platform to the broader market. Over the next two quarters, we will be further enhancing our offerings including: the analytics/AI platform, app SDK for partners and customers, launching additional SaaS appsnd entering additional vertical markets. We also look forward to creating robust partner ecosystems for these vertical markets.

KF:        What’s next for Riversand – what do you envision for the future?

UV:       We are really excited about the potential of our new master data platform and we look forward to working with our current customers as well as new customers to help them with their digital transformation journey. We are disruptors in our space and we will continue to establish ourselves as a larger, bolder and leading global brand.

We hope you enjoyed the interview!  You can check out additional information by reading the press announcement here.

Katie Fabiszak oversees and directs global marketing efforts at Riversand.  She is an accomplished executive with more than 20 years of success in global marketing for high tech companies. She is responsible for leading the strategic evolution of the company’s branding and marketing strategy.  With proven success developing effective marketing strategies to drive revenue, Katie’s extensive career includes marketing leadership positions at Informatica, StrikeIron and DataFlux (a SAS company).