The term narcissism originates from Greek mythology, where the young Narcissus fell in love with his own image reflected in a pool of water. While this is about how a natural person may behave it can certainly also be applied to how a company behaves.
Not to show empathy to customers
I think we all know the classic sales presentation with endless slides about how big and wonderful the selling company is and how fantastic the products they sell are. This approach contradicts everything we know about selling, which is to start with the needs and pain points at the buying company and then how the selling company effectively can fulfill the needs and make the pain points go away.
Not to show empathy to trading partners
While business outcomes originate from selling to your customers it certainly also is affected by how you treat your trading partners and how you can put yourself in their place.
An example close to me is exchange of product information (product data syndication) between trading partners. We often see solutions which is made to make it easy for you but then being difficult for your trading partner. This includes requiring your spreadsheet format to filled out by your trading partner, may be a customer data portal set up by a manufacturer or opposite a supplier data portal set up by a merchant. These are narcissistic dead ends as told in the post The Death Trap in Product Information Management: Your Customer/Supplier Portal.
4 years ago, a post on this blog was called The Scary Data Lake. The post was about the fear about if the then new data lake concept would lead to data swamps with horrific data quality, data dumps no one would ever use, data cesspools with all the bad governed data and data sumps that would never be part of the business processes.
For sure, there have been mistakes with data lakes. But it seems that the data lake concept has matured and the understanding of what a data lake can do good is increasing. The data lake concept has even grown out of the analytic world and into more operational cases as told in the post Welcome to Another Data Lake for Data Sharing.
Some of the things we have learned is to apply well known data management principles to data lakes too. This encompasses metadata management, data lineage capabilities and data governance as reported in the post Three Must Haves for your Data Lake.
Building materials is a very diverse product group. As a wholesaler or dealer, you will have to manage many different attributes and digital assets depending on which product classification we are talking about.
Getting these data from a diverse crowd of suppliers is a hard job. You may have a spreadsheet for each product group where you require data from your suppliers, but this means a lot of follow up and work in putting the data into your system. You may have a supplier portal, but suppliers are probably reluctant to use it, because they cannot deal with hundreds of different supplier portals from you and all the other wholesalers and dealers possibly across many countries. In the same way that you are not happy about if you must fetch data from hundreds of different customer portals provided by manufacturers and other brand owners.
This also means that even if you can handle the logistics, you must limit your regular assortment of products and therefore often deal with special ad hoc products when they are needed to form a complete range of products asked for by your customers for a given building project. Handling of “specials” is a huge burden and the data gathering must usually be repeated if the product turns up again.
At Product Data Lake we have developed a solution to these challenges. It is a cloud service where your suppliers can provide product information in their way and you can pull the information in the way that fits your taxonomy, structure and format.
Our approach is not to reinvent the wheel, but to collaborate with partners in the industry. This include:
Experts within a type of product as building materials and sub-sectors in this industry, machinery, chemicals, automotive, furniture and home-ware, electronics, work clothes, fashion, books and other printed materials, food and beverage, pharmaceuticals and medical devices. You may be a specialist in certain standards for product data. As an ambassador you will link the taxonomy in use at two trading partners or within a larger business ecosystem.
Product data cleansing specialists who have proven track records in optimizing product master data and product information. As an ambassador you will prepare the product data portfolio at a trading partner and extend the service to other trading partners or within a larger business ecosystem.
System integrators who can integrate product data syndication flows into Product Information Management (PIM) and other solutions at trading partners and consult on the surrounding data quality and data governance issues. As an ambassador, you will enable the digital flow of product information between two trading partners or within a larger business ecosystem.
Tool vendors who can offer in-house Product Information Management (PIM) / Master Data Management (MDM) solutions or similar solutions in the ERP and Supply Chain Management (SCM) sphere. As an ambassador you will able to provide, supplement or replace customer data portals at manufacturers and supplier data portals at merchants and thus offer truly automated and interactive product data syndication functionality.
Technology providers with data governance solutions, data quality management solutions and Artificial Intelligence (AI) / machine learning capacities for classifying and linking product information to support the activities made by ambassadors and subscribers.
Reservoirs, as Product Data Lake is a unique opportunity for service providers with product data portfolios (data pools and data portals) for utilizing modern data management technology and offer a comprehensive way of collecting and distributing product data within the business processes used by subscribers.
Until now my venture called Product Data Lake has been a rather technical quest. As with most start-ups the first years have been around building the actual software (in our case facilitated by Larion Computing in Vietnam), adjusting the market fit and run numerous trials with interested parties.
Now it is time to go to market for real. I am happy that another Henrik has joined as CEO and will emphasize on leading the marketing, sales and financial activities.
While I will be concentrating on the product strategy and product management activities it is time to recap the business outcomes we want our subscribers and partners to achieve. Let me express those towards three kinds of business partners:
Manufacturers and brand owners:
On the upstream side of Product Data Lake our goal is to let you as a manufacturer and/or brand owner:
Sell more: Your re-sellers will have the most complete, accurate and timely product information in front of their customers.
Reduce costs: Push your product information in one uniform way and let your re-sellers pull it in their many ways.
Our concept, using emerging technologies within Product Data Lake, will free you from applying many different solutions to providing product information to your re-sellers. You will avoid errors. You will be able to automate the processes and you will be easy to do business with in the eyes of your trading partners.
The people who will use your products want to get complete product information when making the buying decision wherever they are in the supply chain.
You can follow the news stream for this on our LinkedIn showcase page called Product Data Push.
Merchants (dealers and retailers):
On the downstream side of Product Data Lake our goal is to let you as a merchant (dealer or retailer) gain substantial business outcome.
You will sell more by having the most complete, accurate and timely product information in front of your online customers when they make self-service buying decisions.
You will reduce costs as you can pull product information in one uniform way and let your suppliers push it in their many ways. Hereby you can automate the processes, avoid errors and reduce product returns.
Our solution, using emerging technologies within Product Data Lake, will make you be easy to do business with in the eyes of your suppliers and make your product information transform into a powerful weapon in the quest for winning more online market share.
The people who may buy your product range deserves to know all about it and wants to get that information when making the buying decision. Remember: 81 % of visitors will leave a web-shop with incomplete product information.
You can follow the news stream for this on our LinkedIn showcase page called Product Data Pull.
Technology and service partners:
Ambassadors at Product Data Lake can sign up subscribers, assisting these subscribers in uploading their relevant product information portfolio to Product Data Lake and assisting these subscribers in linking their product information with the product information at their trading partners. As an ambassador, you will:
Have the opportunity to work with a big data solution within Product Information Management.
Have the opportunity to make data mapping and/or data integration services and cross-sell of other services for subscribers in a whole supply ecosystem.
Get 25 % kickback on new subscriptions in a potentially exponentially growing subscriber base in supply ecosystems
As Reservoir you can bring new life into product data portals and pools. Product Data Lake is a unique opportunity for service providers with product data portfolios for utilizing modern data management technology and offer a comprehensive way of linking, collecting and distributing product data within the business processes used by subscribers. Signing up as reservoir is free.
The linking theme also related to applying artificial intelligence / machine learning to mapping between the different product information taxonomies in use at trading partners, where we collaborate with business partners who provide such capabilities.
You can follow the news stream for this on our LinkedIn showcase page called Product Data Link.
A couple of weeks ago Microsoft, Adobe and SAP announced their Open Data Initiative. While this, as far as we know, is only a statement for now, it of course has attracted some interest based on that it is three giants in the IT industry who have agreed on something – mostly interpreted as agreed to oppose Salesforce.com.
Forming a business ecosystem among players in the market is not new. However, what we usually see is that a group of companies agrees on a standard and then each one of them puts a product or service, that adheres to that standard, on the market. The standard then caters for the interoperability between the products and services.
In this case its seems to be something different. The product or service is operated by Microsoft based on their Azure platform. There will be some form of a common data model. But it is a data lake, meaning that we should expect that data can be provided in any structure and format and that data can be consumed into any structure and format.
In all humbleness, this concept is the same as the one that is behind Product Data Lake.
The Open Data Initiative from Microsoft, Adobe and SAP focuses at customer data and seems to be about enterprise wide customer data. While it technically also could support ecosystem wide customer data, privacy concerns and compliance issues will restrict that scope in many cases.
At Product Data Lake, we do the same for product data. Only here, the scope is business ecosystem wide as the big pain with product data is the flow between trading partners as examined here.
20 years ago, when I started working as a contractor and entrepreneur in the data management space, data was not on the top agenda at many enterprises. Fortunately, that has changed.
An example is displayed by Schneider Electric CEO Jean-Pascal Tricoire in his recent blog post on how digitization and data can enable companies to be more sustainable. You can read it on the Schneider Electric Blog in the post 3 Myths About Sustainability and Business.
Manufacturers in the building material sector naturally emphasizes on sustainability. In his post Jean-Pascal Tricoire says: “The digital revolution helps answering several of the major sustainability challenges, dispelling some of the lingering myths regarding sustainability and business growth”.
One of three myths dispelled is: Sustainability data is still too costly and time-consuming to manage.
From my work with Master Data Management (MDM) and Product Information Management (PIM) at manufacturers and merchants in the building material sector I know that managing the basic product data, trading data and customer self-service ready product data is hard enough. Taking on sustainability data will only make that harder. So, we need to be smarter in our product data management. Smart and sustainable homes and smart sustainable cities need smart product data management.
The intersection between Artificial Intelligence (AI) and Master Data Management (MDM) – and the associated discipline Product Information Management (PIM) – is an emerging topic.
A use case close to me
In my work at setting up a service called Product Data Lake the inclusion of AI has become an important topic. The aim of this service is to translate between the different taxonomies in use at trading partners for example when a manufacturer shares his product information with a merchant.
In some cases the manufacturer, the provider of product information, may use the same standard for product information as the merchant. This may be deep standards as eCl@ss and ETIM or pure product classification standards as UNSPSC. In this case we can apply deterministic matching of the classifications and the attributes (also called properties or features).
However, most often there are uncovered areas even when two trading partners share the same standard. And then again, the most frequent situation is that the two trading partners are using different standards.
As always, applying too much human interaction is costly, time consuming and error prone. Therefore, we are very eagerly training our machines to be able to do this work in a cost-effective way, within a much shorter time frame and with a repeatable and consistent outcome to the benefit of the participating manufacturers, merchants and other enterprises involved in exchanging products and the related product information.
Learning from others
This week I participated in a workshop around exchanging experiences and proofing use cases for AI and MDM. The above-mentioned use case was one of several use cases examined here. And for sure, there is a basis for applying AI with substantial benefits for the enterprises who gets this. The workshop was arranged by Camelot Management Consultants within their Global Community for Artificial Intelligence in MDM.
Enterprises are increasingly going to be part of business ecosystems where collaboration between legal entities not belonging to the same company family tree will be the norm.
This trend is driven by digital transformation as no enterprise possibly can master all the disciplines needed in applying a digital platform to traditional ways of doing business.
Enterprises are basically selfish. This is also true when it comes to Master Data Management (MDM). Most master data initiatives today revolve around aligning internal silos of master data and surrounding processes to fit he business objectives within an enterprise as a whole. And that is hard enough.
However, in the future that is not enough. You must also be able share master data in the business ecosystems where your enterprise will belong. The enterprises that, in a broad sense, gets this first will survive. Those who will be laggards are in danger of being left out of business.
There is a tendency when deploying Product Information Management (PIM) solutions, that you may want to add a portal for your trading partners:
If you are a manufacturer, you could have a customer portal where your downstream re-sellers can fetch the nicely arranged product information that is the result of your PIM implementation.
If you are a merchant, you could have a supplier portal where your upstream suppliers can deliver their information nicely arranged according to your product information standards in your PIM implementation.
This is a death trap for both manufacturers and merchants, because:
As a trading manufacturer and merchant, you probably follow different standards, so one must obey to the other. The result is that one side will have a lot of manual and costly work to do to obey the strongest trading partner. Only a few will be the strongest all time.
If all manufacturers have a customer portal and all merchants have a supplier portal everyone will be waiting for the other and no product information will flow in the supply chains.