The next step towards excellence in PIM is to handle product information in close collaboration with your trading partners. Product Data Lake is the solution for that. Here upstream providers of product information (manufacturers and upstream distributors) and downstream receivers of product information (downstream distributors and retailers) connect their choice of in-house PIM solution or other product master data solution as PLM (Product Lifecycle Management) or ERP.
The principle behind Product Data Lake is inspired by how a data lake differs from a traditional data warehouse. In a data lake the linking and transformation takes place late, when the data is consumed by the receiver.
Product Data Lake resembles a social network as you connect with your trading partners from the real world in order to collaborate on getting complete and accurate product data from the manufacturer to the point-of-sales:
As a downstream receiver, you can be on the winning side by utilizing our Product Data Pull service
As an upstream provider, you can be on the winning side by utilizing our Product Data Push service
In here Joe examines how data warehouses can be modernized to augment architectures supporting data lakes and Mater Data Management and the case for moving data warehouses to the cloud.
In my view, a lot of data management disciplines will eventually move to the cloud as one follows the other. By adding “beyond” I suggest, that cloud solutions will not only be something that is supported company by company. Eventually you will be able to get business outcome by sharing data management burdens within your business ecosystem.
My current venture called Product Data Lake is an example of such a solution. It modernizes the data warehouse thinking within product information sharing by using a data lake concept in the cloud ready-to-use by trading partners within business ecosystems:
If you are a provider of product information, typically as a manufacturer of goods, you can harvest your business outcome by using us for Product Data Push
If you are a receiver of product information, you can harvest your business outcome by using us for Product Data Pull
In his section around improving data James, among other things, suggests asking this question: “Do you have other data sources you can bring in to support the data you have?”
This is a key question to me and in my eyes a very important mean to make your data bring business outcome. Applying second party and third party data can increase the potential value of your first party data in these ways:
Utilizing third party data to compile complete, accurate and timely party data assets needed for understanding and connecting with customers.
Receive second party data to compile complete, accurate and timely product information.
Having a holistic view of internal and external data needed for decision making.
Hereby you will sell more, reduce costs and mitigate risks.
In his post Andrew connects the classic dots: How does technology lead to business outcome? Especially the use of cloud solutions and the multi-tenant aspect is in the focus. Andrew asks: What do you see “out there”?
My view is that multi-tenant is not just about offering the same subscription based cloud solutions to a range of clients. It is about making clients sharing the same business ecosystem work in the same MDM realm. This is the platform described in Master Data Share.
Oh, and what does that have to do with business outcome? A lot. Organizations will not win the future the race by optimizing there inhouse MDM capabilities alone. With the rise of digitalization, they need to connect with and understand their customers, which I believe is something Reltio is good at. Furthermore, organisations need to be much better at working with their business partners in a modern way, including at the master data level. The business outcome of this is:
Having complete, accurate and timely data assets needed for understanding and connecting with customers. You will sell more.
Having a fast and seamless flow of data assets, not at least product information, to and from your trading partners. You will reduce costs.
Having a holistic view of internal and external data needed for decision making. You will mitigate risks.
Some of the hot topics on the agenda today is the EU General Data Protection Regulation (GDPR) and the data lake concept. These are also hot topics for me, as GDPR is high on the agenda in doing MDM (and currently TDM – Test Data Management) consultancy and the data lake approach is the basic concept in my Product Data Lake venture.
In my eyes the data lake concept can be used for a lot of business challenges. One of the them was highlighted in a CIO article called Informatica brings AI to GDPR compliance, data governance. In here Informatica CEO Anil Chakravarthy tells how a new tool, Informatica’s Compliance Data Lake, can help organisations getting a grasp on where data elements relevant to be compliant with GDPR resides in the IT landscape. This is a task very close to me in a current engagement.
Right now, I am working with the Product Data Lake service, that is aimed at supporting business ecosystems when it comes to sharing product information. Our take at business ecosystems seems to fit quite nicely into the 8-dimension model.
Participants in supply chains must adapt a business ecosystem strategy when it comes to handling product information. An inhouse Product Information Management (PIM) system is only the beginning. This system must be an active part of a digital ecosystem as explained in the post Passive vs Active Product Information Exchange.
This digital ecosystem must be able to support different degrees of openness as public, private or hybrid and embrace engagement of diverse participants having various types of relationships. How this is achieved for product information sharing was touched in the post Product Data Management is Like an Ironman.
The rise of big data is very much driven by a craving for getting more insight on your (prospective) customers. However, as always, the coin has a flipside.
Looking at it from the other side
As a customer, we will strike back. We do not need to be told what to buy. But we do want to know what we are buying. This means we want to be able to see rich product information when making a self-service purchase. This subject was examined in the post You Must Supplement Customer Insight with Rich Product Data.
Many companies who are involved in selling to private and business customers are ramping up maintenance of product data by implementing inhouse Product Information Management (PIM) solutions as told in yesterday’s guest post on this blog. The article is called The Relation of PIM to Retail Success.
One further challenge is that you have to get product information from the source, usually being the manufacturers.
Big data approaches work for both
As data lakes are used to being the place to harvest customer insight, the data lake concept can be the approach to provide product insight to end customers as well.
The problem with having product data flowing from manufacturers to distributors and retailers is that everyone does not use the same standard, format, structure and taxonomy for product information.
The solution is a data lake shared by the business ecosystem. It is called Product Data Lake.