Tag Archives: IoT

Digital Twins in the MDM Product Domain / PIM

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When working with the product domain in Master Data Management (MDM) and with Product Information Management (PIM) we have traditionally been working with the product model meaning that we manage data about how a product that can be produced many times in exactly the same way and resulting in having exactly the same features. In other words, we are creating a digital twin of the product model.

As told in the post Spectre vs James Bond and the Unique Product Identifier the next level in product data management is working with each product instance meaning each produced thing that have a set of data attached that is unique to that thing. Such data can be:

  • Serial number or other identification as for example the Unique Device Identification (UDI) known in healthcare
  • Manufacturing date and time
  • Specific configuration
  • Current and historical position
  • Current and historical owner
  • Current and historical installer, maintainer and other caretaker
  • Produced sensor data if it is a smart device / machine

There is a substantial business potential in being better than your competitor in managing product instances. This boils down to that data is power – if you use the data.

When managing this data, we are building a digital twin of the product instance.

Maintaining that digital twin is a collaborative effort involving the manufacturer, the logistic service provider, the owner, the caretaker, and other roles. For that you need some degree of Interenterprise MDM.

Digital Transformation Success Rely on MDM / PIM Success

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It is hard to find an organization who do not want to be on the digital transformation wagon today. But how can you ensure that your digital transformation journey will be a success? One of the elements in making sure that this data driven process will be a success will be to have a solid foundation of Master Data Management (MDM) including Product Information Management (PIM).

The core concepts here are:

  • Providing a 360-degree view of master data entities: Engaging with your customers across a range of digital platforms is a core part of any digital transformation. Having a 360-degree view of your customer has never been more important, and that starts with well-organized and maintained customer master data. The same is true for supplier master data and other party master data. 360-degree view of locations is equally important. The same goes for products and assets as pondered in post Golden Records in Multidomain MDM.
  • Enabling happy self-service scenarios: Customer data are gathered from many sources and digital self-registration is becoming the most common used method. The self-service theme has also emerged in handling supplier master data as self-service based supplier portals have become common as the place where supplier/vendor master data is captured and maintained. Interacting with your trading partners on digital platforms and having the most complete product information in front of your customers in self-service online selling scenarios requires a solid foundation for product master data and Product experience Management (PxM).
  • Underpinning the best customer experience: Customer experience (CX) and MDM must go hand in hand. Both themes involve multiple business units and digital environments within your enterprise and in the wider business ecosystem, where your enterprise operates. Master data is the glue that brings the data you hold about your customers together as well as the glue that combines this with the data you share about your product offering.
  • Encompassing Internet of Things (IoT): Smart devices that produces big data can be used to gain much more insight about parties (in customer and other roles), products, locations and the things themselves. You can only do that effectively by relating IoT and MDM.

Digital Transformation Success

IoT and MDM

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With the rise of Internet of Things (IoT), asset – seen as a thing – is seriously entering the Master Data Management (MDM) world. These things are smart devices that produces big data we can use to gain much more insight about parties (in customer and other roles), products, locations and the things themselves.

In the old MDM world with party, product and location we had 3 types of relationships between entities in these domains. With the inclusion of asset/thing we have 3 more challenging relationship types.

IoT and MDM

The Old MDM World

1: Handling the relationship between a party at its location(s) as for example a postal address and possibly also a geocode is one of the core capabilities of a proper party MDM solution.

2: Managing the relationship between parties and products is essential in supplier master data management and tracking the relationship between customers and products is a common multidomain MDM use case.

3:  Products are often related to a location, product features and not at least the language(s) used has a relation to locations and digital assets as certificates are location dependent.

The New MDM World

4: We need to be aware of who owns, operates, maintains, manufactured and have other party roles with any smart device being a part of the Internet of Things.

5: In order to make sense of the big data coming from fixed or moving smart devices we need to know the location context.

6: Further, we must include the product information of the product model for the smart devices.

Golden Records in Multidomain MDM

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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.

Master Data Management Definitions: The A-Z of MDM. Part 2

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

img_A-Z_post2E

Enterprise Asset Management (EAM). The management of the assets of an organization (e.g., equipment and facilities).

Enterprise Resource Planning (ERP). Refers to enterprise systems and software used to manage day-to-day business activities, such as accounting, procurement, project management, inventory, sales, etc. Many businesses have several ERP systems, each managing data about products, locations or assets, for example. A comprehensive MDM solution complements an ERP by ensuring that the data from each of the data domains used by the ERP is accurate, up-to-date and synchronized across the multiple ERP instances.

Enrichment. Data enrichment refers to processes used to enhance, refine or otherwise improve raw data. In the world of MDM, enriching your master data can happen by including third-party data to get a more complete view, for example, such as adding social data to your customer master data. MDM eliminates manual product enrichment processes and replaces them with custom workflows, business rules and automation.

Entity. A classification of objects of interest to the enterprise (e.g., people, places, things, concepts and events).

ETL. Extract, Transform and Load. A process in data warehousing, responsible for pulling data out of source systems and placing it into a data warehouse.

G

Golden Record. In the MDM world, also sometimes referred to as “the single version of the truth.” This is the state you want your master data to be in and what every MDM solution is working toward creating: the most pure, complete, trustable data record possible.

Governance. Data Governance is a collection of practices and processes aiming to create and maintain a healthy organizational data framework, by establishing processes that ensure that data is formally managed throughout the enterprise. It can include creating policies and processes around version control, approvals, etc., to maintain the accuracy and accountability of the organizational information. Data governance is as such not a technical discipline but an indispensable discipline of a modern organization—and a fundamental supplement to any data management initiative.

GS1. Global Standards One. The GS1 standards are unique identification codes used by more than one million companies worldwide. The standards aim to create a common foundation for businesses when identifying and sharing vital information about products, locations, assets and more. The most recognizable GS1 standards are the barcode and the radio-frequency identification (RFID) tags. An MDM solution will support and integrate the GS1 standards across industries.

H

Hierarchy Management. An essential aspect of MDM that allows users to productively manage complex hierarchies spread over one or more domains and change them into a formal structure that can be used throughout the enterprise. Products, customers and organizational structures are all examples of domains where a hierarchy structure can be beneficial (e.g., in defining the hierarchical structure of a household in relation to a customer data record).

Hub. A data hub or an enterprise data hub (EDH) is a database that is populated with data from one or more sources and from which data is taken to one or more destinations. An MDM system is an example of a data hub, and therefore sometimes goes under the name Master Data Management hub.

I

Identity resolution. A data management process where an individual is identified from disparate data sets and databases to resolve their identity. This process relates to Customer Master Data Management.

Information. Information is the output of data that has been analyzed and/or processed in any manner.

Learn more about the difference between data and information here.

Integration. One of the biggest advantages of an MDM solution is its ability to integrate with various systems and link all of the data held in each of them to each other. A system integrator will often be brought on board to provide the implementation services.

Internet of Things (IoT). Internet of Things is the network of physical devices embedded with connectivity technology that enables these “things” to connect and exchange data. IoT technology represents a huge opportunity—and challenge—for organizations across industries as they can access new levels of data. A Master Data Management solution supports IoT initiatives by, for example, linking trusted master data to IoT-generated data as well as supporting a data governance framework for IoT data.

Learn more about the link between IoT and MDM here.

L

Lake. A data lake is a place to store your data, usually in its raw form without changing it. The idea of the data lake is to provide a place for the unaltered data in its native format until it’s needed. Why? Certain business disciplines such as advanced analytics depend on detailed source data. A data lake is the opposite of a data warehouse, but often the data lake will be an addition to a data warehouse.

Location data. Data about locations. Solutions that add location data management to the mix, such as Location Master Data Management, are on the rise. Effectively linking location data to other master data such as product data, supplier data, asset data or customer data can give you a more complete picture and enhance processes and customer experiences.

M

Maintenance. In order for any data management investment to continue delivering value, you need to maintain every aspect of a data record, including hierarchy, structure, validations, approvals and versioning, as well as master data attributes, descriptions, documentation and other related data components. Master data maintenance is often enabled by automated workflows, such as pushing out notifications to data stewards when there’s a need for a manual action. Maintenance is an unavoidable and ongoing process of any MDM implementation.

Modelling. Modelling in Master Data Management is a process in the beginning of an MDM implementation where you accurately map and define the relationship between the core enterprise entities (e.g., your products and their attributes). Based on that you create the optimal master data model that best fits your organizational setup.

Matching (and linking and merging). Key functionalities in a Customer Master Data Management solution with the purpose of identifying and handling duplicates to achieve a Golden Record. The matching algorithm constantly analyzes or matches the source records to determine which represent the same individual or organization. While the linking functionality persists all the source records and link them to the Golden Record, the merging functionality selects a survivor and non-survivor. The Golden Record is based only on the survivor. The non-survivor is deleted from the system.

Multidomain. A multidomain Master Data Management solution masters the data of several enterprise domains, such as product and supplier domain, or customer and product domain or any combination handling more than one domain.

Metadata management. The management of data about data. Metadata Management helps an organization understand the what, where, why, when and how of its data: where is it coming from and what meaning does it have? Key functionalities of Metadata Management solutions are metadata capture and storage, metadata integration and publication as well as metadata management and governance. While Metadata Management and Master Data Management systems intersect, they provide two different frameworks for solving data problems such as data quality and data governance.

N

New Product Development (NPD). A discipline in Product Lifecycle Management (PLM) that aims to support the management of introducing a new product line or assortment, from idea to launch, including its ideation, research, creation, testing, updating and marketing.

O

Omnichannel. A term mostly used in retail to describe the creation of integrated, seamless customer experiences across all customer touchpoints. If you offer an omnichannel customer experience, your customers will meet the same service, offers, product information and more, no matter where they interact with your brand (e.g., in-store, on social media, via email, customer service, etc.). The term stems from the Latin word omni, meaning everything or everywhere, and it has surpassed similar terms such as multi-channel and cross-channel that do not necessarily comprise all channels.

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.