The Rise of Business Ecosystems in Data Management

There are many signs showing that we are entering the age of business ecosystems. A recent example is an article from Digital McKinsey. This read worthy article is called Adopting an ecosystem view of business technology.

In here, the authors emphasizes on the need to adapt traditional IT functions to the opportunities and challenges of emerging technologies that embraces business ecosystems. I fully support that sentiment.

In my eyes, some of the emerging technologies we see are in large misunderstood as something meant for being behind the corporate walls. My favorite example is the data lake concept. I do not think a data lake will be an often seen success solely within a single company as explained in the post Data Lakes in Business Ecosystems.

The raise of technology for business ecosystems will also affect the data management roles we know today. For example, a data steward will be a lot more focused towards external data than before as elaborated in the post The Future of Data Stewardship.

Encompassing business ecosystems in data management is of course a huge challenge we have to face while most enterprises still have not reached an acceptable maturity when it comes internal data and information governance. However, letting the outside in will also help in getting data and information right as told in the post Data Sharing Is The Answer To A Single Version Of The Truth.

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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.

Infonomics and Second Party Data

The term infonomics does not yet run unmarked through my English spellchecker, but there are some information available on Wikipedia about infonomics. Infonomics is closely related to the often-mentioned phrases in data management about seeing data / information as an asset.

Much of what I have read about infonomics and seeing data / information as an asset is related to what we call first party data. That is data that is stored and managed within your own company.

Some information is also available in relation to third party data. That is data we buy from external parties in order to validate, enrich or even replace our own first party data. An example is a recent paper from among others infonomic guru Doug Laney of Gartner (the analyst firm). This paper has a high value if you want to buy it as seen here.

Anyway, the relationship between data as an asset and the value of data is obvious when it comes to third party data, as we pay a given amount of money for data when acquiring third party data.

Second party data is data we exchange with our trading and other business partners. One example that has been close to me during the recent years is product information that follows exchange of goods in cross company supply chains. Here the value of the goods is increasingly depending on the quality (completeness and other data quality dimensions) of the product information that follows the goods.

In my eyes, we will see an increasing focus on infonomics when it comes to exchanging goods – and the related second party data – in the future. Two basic factors will be:

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We Need More Product Data Lake Ambassadors

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Product Data Lake is the new solution to sharing product information between trading partners. While we see many viable in-house solutions to Product Information Management (PIM), there is a need for a solution to exchange product information within cross company supply chains between manufacturers, distributors and retailers.

Completeness of product information is a huge issue for self-service sales approaches as seen in ecommerce. 81 % of e-shoppers will leave a webshop with lacking product information. The root cause of missing product information is often an ineffective cross company data supply chain, where exchange of product data is based on sending spreadsheets back and forth via email or based on biased solutions as PIM Supplier Portals.

However, due to the volume of product data, the velocity required to get data through and the variety of product data needed today, these solutions are in no way adequate or will work for everyone. Having a not working environment for cross company product data exchange is hindering true digital transformation at many organizations within trade.

As a Product Information Management professional or as a vendor company in this space, you can help manufacturers, distributors and retailers in being successful with product information completeness by becoming a Product Data Lake ambassador.

The Product Data Lake encompasses some of the most pressing issues in world-wide sharing of product data:

The first forward looking professionals and vendors in the Product Information Management realm have already joined. I would love to see you as well as our next ambassador.

Interested? Get in contact:

PIM Supplier Portals: Are They Good or Bad?

A recent discussion on the LinkedIn Multi-Domain MDM group is about vendor / supplier portals as a part of Product Information Management implementations.

A supplier portal (or vendor portal if you like) is usually an extension to a Product Information Management (PIM) solution. The idea is that the suppliers of products, and thus providers of product information, to you as a downstream participant (distributor or retailer) in a supply chain, can upload their product information into your PIM solution and thus relieving you of doing that. This process usually replace the work of receiving spreadsheets from suppliers in the many situations where data pools are not relevant.

In my opinion and experience, this is a flawed concept, because it is hostile to the supplier. The supplier will have hundreds of downstream receivers of products and thus product information. If all of them introduced their own supplier portal, they will have to learn and maintain hundreds of them. Only if you are bigger than your supplier is and is a substantial part of their business, they will go with you.

Broken data supply chainAnother concept, which is the opposite, is also emerging. This is manufacturers and upstream distributors establishing PIM customer portals, where suppliers can fetch product information. This concept is in my eyes flawed exactly the opposite way.

And then let us imagine that every provider of product information had their PIM customer portal and every receiver had their PIM supplier portal. Then no data would flow at all.

What is your opinion and experience?

Data Management, Never stop learning

Welcome in the class room to Rick Buijserd from The Netherlands as the next guest blog post author:

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As a child you were happy when the bell ranged and the school day ended.  It was time to play with your friends and don’t think about learning anymore, just play! Most of us look back at this time as the best time of our lives. A time without any worries and enjoying every moment of it. Even though it wasn’t the main focus as a child it was also the time that we learned new ideas and things every day. Are we still learning every day? Are you learning new things about data management every day? You should and here is why…

Gaining knowledge

Data is the new oil and many of us make a decent living by advising or consulting companies in this area of expertise. But when time goes by so are the developments and in the technology world this goes fast, very fast. In the last couple of years the data environment has become bigger and bigger. First there was just data in companies, now you have the combine sources of data to get a clear view about. And the sources keep on changing. Big data used to be a word that was undefined and unable to use. And for many it still is, but others use big data to enrich and enable growth for their companies. By just summing this up you see the changes that happened in the last couple of years and you have to keep up to stay relevant. Learn and gain knowledge is the only key to success in the long term. Artificial Intelligence and Machine Learning powered by optimal use of data and data management will take over many tasks but in the end human creativity and the ability to learn will provide success and the power to make the difference.

Data Management is never finished and neither is learning about it

As you have been in the world of data management you should know that data management is never finished and so is the possibility of gaining knowledge. New books about data management are published recently, research firms keep on researching and find new discoveries. And many companies use the evolution of the technology to grow. Also Communities are built around topics on many different platforms. The possibility to learn is everywhere! Use it in your benefit, data management is never finished…

data-management-expertsRick Buijserd is author and owner of the platform Data Management Experts and a young professional with experience in the world of data. He started his career at a well-known software vendor as channel manager where he learned the skills of indirect sales and managing partners. Financial, HR, Logistics, Warehousing and PSA were the main elements of his software sales. Building relationships with experts and other vendors are part of his DNA.

rickAfter a couple of years he decided to make a switch and landed in the world of accountancy firms. In this period he enabled himself to become a trusted advisor of many accountancy firms in The Netherlands. The area of finance, financial reporting, tax, auditing and other accountancy related activities are no secret to him. Together with his clients he developed many solutions to solve their challenges. In this period the love for data management came above. Accountancy firms are the ultimate example of being data driven. It is all they know.

In the most recent period of his career he stepped into the world of multinationals and as off today he is still active in this world advising around data management and selling software solutions to multinationals who have challenges in the area of data management. Also he is an expert in the area of social selling via LinkedIn and this knowledge has been brought into practice via a LinkedIn Group for Dutch Data Management Experts in which he gathers the top data management experts from the largest companies in The Netherlands to discuss all kind of data related topics.