4 MDM Definitions: Which One is the Best?

What is Master Data Management (MDM)? How can we define MDM?

Well, as with everything in life there are varying and competing definitions. Below you can find 4 different definitions:

Wikipedia: In business, Master data management (MDM) is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference. In computing, a master data management tool can be used to support master data management by removing duplicates, standardizing data (mass maintaining), and incorporating rules to eliminate incorrect data from entering the system in order to create an authoritative source of master data. Master data are the products, accounts and parties for which the business transactions are completed.

MDM Wordle

Gartner: Master data management (MDM) is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets. Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.

SearchDataManagement: Master data management (MDM) is 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.

Techopedia: Master data management (MDM) refers to the management of specific key data assets for a business or enterprise. MDM is part data management as a whole but is generally focused on the handling of higher level data elements, such as broader identity classifications of people, things, places and concepts.

Your definition: Which one of the four above-mentioned definitions do you prefer? Or is there a much better fifth one?

Multienterprise MDM

One of the terms on the move on the Gartner Hype Cycle for Information Governance and Master Data Management is Multienterprise MDM.

Doing Master Data Management (MDM) enterprise wide is hard enough. The ability to control master data across your organization is essential to enable digitalization initiatives and ensure the competitiveness of your organization in the future.

But it does not stop there. Increasingly every organization will be an integrated part of a business ecosystem where collaboration with business partners will be a part of digitalization and thus we will have a need for working on the same foundation around master data.

The different master data domains will have different roles to play in such endeavors. Party master will be shared in some degree but there are both competitive factors, data protection and privacy factors to be observed as well.

MDM Ecosystem

Product master data – or product information if you like – is an obvious master data domain where you can gain business benefits from extending master data management to be ecosystem wide. This includes:

  • Working with the same product classifications or being able to continuously map between different classifications used by trading partners
  • Utilizing the same attribute definitions (metadata around products) or being able to continuously map between different attribute taxonomies in use by trading partners
  • Sharing data on product relationships (available accessories, relevant spare parts, updated succession for products, cross-sell information and up-sell opportunities)
  • Having access to latest versions of digital assets (text, audio, video) associated with products

The concept of ecosystem wide Multi-Domain MDM is explored further is the article about Master Data Share.

Golden Records in Multidomain MDM

The term golden record is a core concept within Master Data Management (MDM). A golden record is a representation of a real world entity that may be compiled from multiple different representations of that entity in a single or in multiple different databases within the enterprise system landscape.

GoldIn Multidomain MDM we work with a range of different entity types as party (with customer, supplier, employee and other roles), location, product and asset. The golden record concept applies to all of these entity types, but in slightly different ways.

Party Golden Records

Having a golden record that facilitates a single view of customer is probably the most known example of using the golden record concept. Managing customer records and dealing with duplicates of those is the most frequent data quality issue around.

If you are not able to prevent duplicate records from entering your MDM world, which is the best approach, then you have to apply data matching capabilities. When identifying a duplicate you must be able to intelligently merge any conflicting views into a golden record.

In lesser degree we see the same challenges in getting a single view of suppliers and, which is one of my favourite subjects, you ultimately will want to have a single view on any business partner, also where the same real world entity have both customer, supplier and other roles to your organization.

Location Golden Records

Having the same location only represented once in a golden record and applying any party, product and asset record, and ultimately golden record, to that record may be seen as quite academic. Nevertheless, striving for that concept will solve many data quality conundrums.

GoldLocation management have different meanings and importance for different industries. One example is that a brewery makes business with the legal entity (party) that owns a bar, café, restaurant. However, even though the owner of that place changes, which happens a lot, the brewery is still interested in being the brand served at that place. Also, the brewery wants to keep records of logistics around that place and the historic volumes delivered to that place. Utility and insurance is other examples of industries where the location golden record (should) matter a lot.

Knowing the properties of a location also supports the party deduplication process. For example, if you have two records with the name “John Smith” on the same address, the probability of that being the same real world entity is dependent on whether that location is a single-family house or a nursing home.

Product Golden Record

Product Information Management (PIM) solutions became popular with the raise of multi-channel where having the same representation of a product in offline and online channels is essential. The self-service approach in online sales also drew the requirements of managing a lot more product attributes than seen before, which again points to a solution of handling the product entity centralized.

In large organizations that have many business units around the world you struggle with having a local view and a global view of products. A given product may be a finished product to one unit but a raw material to another unit. Even a global SAP rollout will usually not clarify this – rather the contrary.

GoldWhile third party reference data helps a lot with handling golden records for party and location, this is lesser the case for product master data. Classification systems and data pools do exist, but will certainly not take you all the way. With product master data we must, in my eyes, rely more on second party master data meaning sharing product master data within the business ecosystems where you are present.

Asset (or Thing) Golden Records

In asset master data management you also have different purposes where having a single view of a real world asset helps a lot. There are namely financial purposes and logistic purposes that have to aligned, but also a lot of others purposes depending on the industry and the type of asset.

With the raise of the Internet of Things (IoT) we will have to manage a lot more assets (or things) than we usually have considered. When a thing (a machine, a vehicle, an appliance) becomes intelligent and now produces big data, master data management and indeed multi-domain master data management becomes imperative.

You will want to know a lot about the product model of the thing in order to make sense of the produced big data. For that, you need the product (model) golden record. You will want to have deep knowledge of the location in time of the thing. You cannot do that without the location golden records. You will want to know the different party roles in time related to the thing. The owner, the operator, the maintainer. If you want to avoid chaos, you need party golden records.

Cloud multi-domain MDM as the foundation for Digital Transformation

Upen-200x300

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).

Five Steps to Guarantee a Successful Master Data Management Implementation

Today’s guest blog post is written by Nils Erik Pedersen of Stibo Systems. In here Nils Erik goes through five essential prerequisites for making your MDM implementation a success.

For any Master Data Management initiative to be a success, it’s important that initial preparations and considerations are in place. These are the steps you as a business cannot ignore when initiating any type of Master Data Management journey. The success of your initiatives depends on it.

#1. Establish your business vision and define Master Data Management’s role in it

Before you even decide which Master Data Management aspects and processes you need, you must start with focusing on something completely different than your data: Your business. What is your overall business vision? What is it that you specifically need to solve to fulfill that vision? I bet the answers to these questions to begin with won’t come down to “improving our data quality” or “create better workflows around our product on-boarding processes”.

But once you start drilling into your business vision and its components, you’ll probably find that processes around data quality and data workflows do in fact support your greater vision. But it’s a question of translating IT challenges into broader business problems, such as the challenge of creating a frictionless customer journey or how to empower your employees with the insights that they need.

If you view MDM as a business driver – a business problem solver instead of an IT problem solver – you’ll have an easier time getting buy-in from the rest of the business, as well as have a better foundation to measure the ROI of your investment.

#2. Identify what domain supports the first step toward your business vision

Let’s be honest. You cannot expect MDM to magically solve all your business issues overnight. The vast majority of successful multidomain MDM implementations are done with step-by-step approaches, where you gradually expand your solution from one to more data domains – e.g. your Product data domain, supplier data domain or customer data domain – and build on the experiences from the first domain(s). Step two is about zooming in on the areas of your business that will benefit most from better data quality and data processes and then focus on that. Is your vision to compete on ‘fastest with new products’? Then your product data may be the place to start. Or is it ‘providing the best customer service’? Then it’s probably customer data you need to focus on. Again, concentrate on what can be expected to bring you the greatest business value. And it is not necessarily the area that is the easiest or most obvious to fix.

#3. Create the foundation for MDM ROI

One of the challenges of Master Data Management and other data investments is that it can be hard to prove its business value in tangible quantities. But today, every ambitious enterprise wants to measure and analyze the Return on Investment (ROI) of every single investment. That’s why you need to pick your MDM related Key Performance Indicators (KPIs) in the very inaugural phase of implementing Master Data Management, so that you have a starting point to compare to later in the implementation.

But what KPIs can you measure? Although MDM in itself can be hard to measure, you can better quantify some of your business vision components. So, if one of the milestones in your overall business vision is to improve customer experience, you have to define: What components make up my CX? Those could be:

  1. Customer Service satisfaction
  2. Sales stats
  3. Direct marketing results
  4. Returned goods stats
  5. Shopping cart abandonment rates

Make sure you measure on those ROI metrics before, during and after implementing Master Data Management initiatives.

#4. Educate your business about the why

As soon as you’ve taken the very first steps down MDM lane, it’s time to launch the foundation for the cultural change that needs to happen in your business as the project progresses. Unfortunately, it is not enough that only a handful of employees – those who work closely with the project – know the value of good master data. The rest of the business must also understand this, more specifically why you as a business are doing it and what it means for them as employees. Everybody in the organization needs to be aware of data quality and data processes if the project is to succeed. This step is a rather educational task, and the way to do it is through lots of relevant communication – preferably communication from senior management, not the IT department.

#5. Make sure your strategy and system is scalable and integrational

The one mistake you don’t want to make is to let your early choices prohibit growth at a later stage. So now that you’ve reached the step where you want to start creating strategies and are looking for solutions, you should make sure that you prepare for the future by creating a strategy and bringing in a software solution with the ability to scale. A scalable solution enables you to later expand it into other domains and integrate it with data-driven applications and emerging technologies. That’s important to support future business growth as well as any future mergers or acquisitions.

If you can check these five steps before moving on to the next phase of your MDM initiative, then I believe you’re well off to a good start. What are your experiences with these steps? Are you looking to kick off a MDM project and have other considerations? Please share your thoughts in the comment field.

Nils Erik Pedersen is the Vice President of Product Strategy at Stibo Systems. Nils has a passion for process optimization and automation, in particular when it comes to handling data.  With a background and a career rooted in handling massive amounts of complex data at an enterprise level, he has been involved in many successful Master Data Management implementations. After 20+ years in the Master Data Management industry, and today holding a leading position within Stibo Systems, Nils has built a solid understanding of what makes or breaks Master Data Management implementations and what it takes to drive business value from enterprise data.

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