IoT and Multi-Domain MDM

The Internet-of-Things (IoT) is a hot topic and many Master Data Management (MDM) practitioners as well as tool and service vendors are exploring what the rise of the Internet-of-Things and the related Industry 4.0 themes will mean for Master Data Management in the years to come.

globalIn my eyes, connecting these smart devices and exploiting the big data you can pull (or being pushed) from them will require a lot for all Master Data Management domains. Some main considerations will be:

  • Party Master Data Management is needed to know about the many roles you can apply to a given device. Who is the manufacturer, vendor, supplier, owner, maintainer and collector of data? Privacy and security matters on that basis will have to be taken very seriously.
  • Location Master Data Management is necessary at a much deeper and precise level than what we are used to when dealing with postal addresses. You will need to know a home location with a timespan and you will need to confirm and, for moving devices, supplement with observed locations with a timestamp.
  • Product and Asset Master Data Management is imperative in order to know about the product model of the smart device and individual characteristics of the given device.

It is also interesting to consider, if you will be able to manage this connectivity within a MDM platform (even multidomain and end-to-end) behind your corporate walls. I do not think so as told in the post The Intersections of 360 Degree MDM.

What is a Master Data Entity?

What is a customer? What is a product? You encounter these common questions when working with Master Data Management (MDM).

The overall question about what master data is has been discussed on this blog often as for example in the post A Master Data Mind Map.

Master Data

The two common questions posed as start of this blog post is said to be very dangerous. Well, here are my experiences and opinions:

What is a customer?

In my eyes, customer is a role you can assign to a party. Therefore, the party is the real master data entity. A party can have many other roles as employee, supplier and other kinds of business partner roles. More times than you usually imagine, the party can have several roles at the same time. Examples are customers also being employees and suppliers who are also customers.

From a data quality point of view, it does not have to matter if a party is a customer or not at a certain time. If your business rules requires you to register that party because the party has placed an order, got an invoice, paid an invoice or pre-paid an amount, you will need to take care of the quality of the information you have stored. You will also have to care about the privacy, not at least if the party is a natural person.

Uniqueness is the most frequent data quality issue when it comes to party master data. Again, it is essential to detect or better prevent if the same party is registered twice or more whether that party is a customer according to someone’s definition or not.

What is a product?

Also with products business rules dictates if you are going to register that product. If you are a reseller of products, you should register a product that you promote (being in your range). You could register a product, that you resell that product occasionally (sometimes called specials). If you are a manufacturer, you should register your finished products, your semi-finished products and the used raw materials. Most companies are actually both a reseller and a manufacturer in some degree. Despite of that degree practically all companies also deals with indirect goods as spare parts, office supplies and other stuff you could register as a product within your organisation in the same way your supplier probably have.

What we usually defines as a product is most often what rather should be called a product model. That means we register information about things that are made in the same way and up by the same ingredients and branded similarly. A thing, as each physical instance of a product model, will increasingly have business rules that requires it to be registered as told in the post Adding Things to Product Data Lake.

Big Data Fitness

A man with one watch knows what time it is, but a man with two watches is never quite sure. This old saying could be modernized to, that a person with one smart device knows the truth, but a person with two smart devices is never quite sure.

An example from my own life is measuring my daily steps in order to motivate me to be more fit. Currently I have two data streams coming in. One is managed by the app Google Fit and one is managed by the app S Health (from Samsung).

This morning a same time shot looked like this:

Google Fit:

google-fit

S Health:

s-health

So, how many steps did I take this morning? 2,047 or 2413?

The steps are presented on the same device. A smartphone. They are though measured on two different devices. Google Fit data are measured on the smartphone itself while S Health data are measured on a connected smartwatch. Therefore, I might not be wearing these devices in the exact same way. For example, I am the kind of Luddite that do not bring the phone to the loo.

With the rise of the Internet of Things (IoT) and the expected intensive use of the big data streams coming from all kinds of smart devices, we will face heaps of similar cases, where we have two or more sets of data telling the same story in a different way.

A key to utilize these data in the best fit way is to understand from what and where these data comes. Knowing that is achieved through modern Master Data Management (MDM).

At Product Data Lake we in all humbleness are supporting that by sharing data about the product models for smart devices and in the future by sharing data about each device as told in the post Adding Things to Product Data Lake.

Golden Records in Multi-Domain 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 Multi-domain 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.

Is blockchain technology useful within MDM?

This question was raised on this blog back in January this year in the post Tough Questions About MDM.

Since then the use of the term blockchain has been used more and more in general and related to Master Data Management (MDM). As you know, we love new fancy terms in our else boring industry.

blockchainHowever, there are good reasons to consider using the blockchain approach when it comes to master data. A blockchain approach can be coined as centralized consensus, which can be seen as opposite to centralized registry. After the MDM discipline has been around for more than a decade, most practitioners agree that the single source of truth is not practically achievable within a given organization of a certain size. Moreover, in the age of business ecosystems, it will be even harder to achieve that between trading partners.

This way of thinking is at the backbone of the MDM venture called Product Data Lake I’m working with right now. Yes, we love buzzwords. As if cloud computing, social network thinking, big data architecture and preparing for Internet of Things wasn’t enough, we can add blockchain approach as a predicate too.

In Product Data Lake this approach is used to establish consensus about the information and digital assets related to a given product and each instance of that product (physical asset or thing) where it makes sense. If you are interested in how that develops, why not follow Product Data Lake on LinkedIn.

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Adding Things to Product Data Lake

Product Data Lake went live last month. Nevertheless, we are already planning the next big things in this cloud service for sharing product data. One of them is exactly things. Let me explain.

Product data is usually data about a product model, for example a certain brand and model of a pair of jeans, a certain brand and model of a drilling machine or a certain brand and model of a refrigerator. Handling product data on the model level within business ecosystems is hard enough and the initial reason of being for Product Data Lake.

stepping_stones_oc

However, we are increasingly required to handle data about each instance of a product model. Some use cases I have come across are:

  • Serialization, which is numbering and tracking of each physical product. We know that from having a serial number on our laptops and another example is how medicine packs now will be required to be serialized to prevent fraud as described in the post Spectre vs James Bond and the Unique Product Identifier.
  • Asset management. Asset is kind of the fourth domain in Master Data Management (MDM) besides party, product and location as touched in the post Where is the Asset. Also Gartner, the analyst firm, usually in theory (and also soon in practice in their magic quadrants) classifies product and asset together as thing opposite to party. Anyway, in asset management you handle each physical instance of the product model.
  • Internet of Things (IoT) is, according to Wikipedia, the internetworking of physical devices, vehicles (also referred to as “connected devices” and “smart devices”), buildings and other items—embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data.

Fulfilling the promise of IoT, and the connected term Industry 4.0, certainly requires common understood master data from the product model over serialization and asset management as reported in the post Data Quality 3.0 as a stepping-stone on the path to Industry 4.0.

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