How Wholesalers and Dealers of Building Materials can Improve Product Information Efficiency

MaterialsBuilding 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.

Learn about and follow the solution on our Product Data Pull LinkedIn page.

If you are interested, please ask for more information here:

 

Wrapping Data Around Tangible Products

There are three kinds of data monetization: Selling data, wrapping data around products and utilizing advanced analytics leading to fast operational decision making. These options were examined in the post Three Flavors of Data Monetization.

If we look at the middle option, wrapping data around products, and narrow it down to wrapping data around tangible products, there are some ways to execute that for supply change delegates, not at least if the participating business entities embraces the business ecosystem where goods are moved through:

  • Manufacturers need to streamline the handling of product information internally. This includes disciplines as PLM (Product Lifecycle Management) and PIM (Product Information Management). On top of that, manufacturers need to be effective in the way the product information is forwarded to direct customers and distributors/wholesalers and merchants as exemplified in the post How Manufacturers of Building Materials Can Improve Product Information Efficiency.
  • Merchants need to utilize the best way of getting data into inhouse PIM (Product Information Management) solutions or other kind of solutions where data flows in from trading partners. Many merchants have a huge variety in product information needs as told in the post Work Clothes versus Fashion: A Product Information Perspective. On top of that a merchant will have supplying manufacturers and distributors with varying formats and capabilities to offer product information as discussed in the post PIM Supplier Portals: Are They Good or Bad?.
  • Shippers may extend their offerings from moving the goods between manufacturers and merchants (or directly to end users) to also moving the information about the goods as suggested in the post New Routes for Products. New Routes for Product Information.

The end goal is that the buyer personas in self-service scenarios will be able to make a fact based and full informed decision as pondered in the post Where to Buy a Magic Wand?

Magic wand 3

New Routes for Products. New Routes for Product Information

One of the news this week was that Maersk for the first time is taking a large container ship from East Asia to Europe using a Northern Route through the Arctic waters as told in this Financial Times article.

Arctic route

The purpose of this trip is to explore the possibility of avoiding the longer Southern Route including shoehorning the sea traffic through the narrow Suez Canal. A similar opportunity exists around North America as an alternative to going through The Panama Canal.

Similar to moving products and finding new routes for that we may also explore new routes when it comes to moving information about products. Until now the possibilities, besides cumbersome exchange of spreadsheets, have been to shoehorn product information from the manufacturer into a consensus-based data portal or data pool from where the merchant can fetch the information in accurate the same shape as his competitors does.

At Product Data Lake we have explored shorter, more agile and diverse new routes for that. We call it Product Data Syndication Freedom.

Product Data Syndication Freedom

When working with product data syndication in supply chains the big pain is that data standards in use and the preferred exchange methods differ between supply chain participants.

As a manufacturer you will have hundreds of re-sellers who probably have data standards different from you and most likely wants to exchange data in a different way than you do.

As a merchant you will have hundreds of suppliers who probably have data standards different from you and most likely wants to exchange data in a different way than you do.

The aim of Product Data Lake is to take that pain away from both the manufacturer side and the merchant side. We offer product data syndication freedom by letting you as manufacturer push product information using your data standards and your preferred exchange method and letting you as a merchant pull product information using your data standards and your preferred exchange method.

Product Data SyndicationIf you want to know more. Get in contact here:

Product Information on Demand

Video on demand has become a popular way to watch television series, films and other entertainment and Netflix is probably the most known brand for delivering that.

The great thing about watching video on demand is that you do not have to enjoy the service at the exact same time as everyone else, as it was the case back in the days when watching TV or going to the movies were the options available.

At Product Data Lake we will bring that convenience to business ecosystems, as the situation today with broadcasting product information in supply chains very much resembles the situation we had before video on demand came around in the TV/Movie world.

As a provider of product information (being a manufacturer or upstream distributor), you will push your product information into Product Data lake, when you have the information available. Moreover, you will only do that once for each product and piece of information. No more coming to each theatre near your audience and extensive reruns of old stuff.

As a receiver of product information (being a downstream distributor, reseller or large end user), you will pull product information when you need it. That will be when you take a new product into your range or do a special product sale as well as when you start to deal with a new piece of information. No more having to be home at a certain time when your supplier does the show or waiting in ages for a rerun when you missed it.

Learn more about how Product Data Lake makes your life in Product Information Management (PIM) easier by following us here on LinkedIn.

Product Data Lake

 

6 Decades of the LEGO® Brick and the 2nd Decade of MDM

28th January 2018 marks the 60th anniversary of the iconic LEGO® brick.

As I was raised close to the LEGO headquarter in Billund, Denmark, I also remember having a considerable amount of LEGO® bricks to play with as a child back in the 60’s in the first decade of the current LEGO® brick design. At that time the brick was a brick, where you had to combine a few sizes and colours of bricks into resembling a usable thing from the real world. Since then the range of shapes and colours of the pieces from the Lego factory have grown considerably.

MDM BlocksMaster Data Management (MDM) went into the 2nd decade some years ago as reported in the post Happy 10 Years Birthday MDM Solutions. MDM has some basic building blocks, as proposed by former Gartner analyst John Radcliffe  back in 00’s and touched in the post The Need for a MDM Vision.

These blocks indeed look like the original LEGO® bricks.

Through the 2nd decade of MDM and in coming decades we will probably see a lot of specialised blocks in many shapes describing and covering the people, process and technology parts of MDM. Let us hope that they will all stick well together as the LEGO® bricks have done for the past 60 years.

PS: Some if the sticking together is described in the post How MDM, PIM and DAM Stick Together.

Sell more. Reduce costs.

Business outcome is the end goal of any data management activity may that be data governance, data quality management, Master Data Management (MDM) and Product Information Management (PIM).

Business outcome comes from selling more and reducing costs.

At Product Data Lake we have a simple scheme for achieving business outcome through selling more goods and reducing costs of sharing product information between trading partners in business ecosystems:

Sell more Reduce costs

Interested? Get in touch:

Using Pull or Push to Get to the Next Level in Product Information Management

The importance of having a viable Product Information Management (PIM) solution has become well understood for companies who participates in supply chains.

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.

Read more about that in the post What a PIM-2-PIM Solution Looks Like.

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.

pdl-diagram-new

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:

  • Pull-PushAs 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

Data Born Companies and the Rest of Us

harriThis post is a new feature here on this blog, being guest blogging by data management professionals from all over the world. First up is Harri Juntunen, Partner at Twinspark Consulting in Finland:

Data and clever use of data in business has had and will have significant impact on value creation in the next decade. That is beyond reasonable doubt. What is less clear is, how this is going to happen? Before we answer the question, I think it is meaningful to make a conceptual distinction between data born companies and the rest of us.

Data born born companies are companies that were conceived from data. Their business models are based  on monetising clever use of data. They have organised everything from their customer service to operations to be capable of maximally harness data. Data and capabilities to use data to create value is their core competency. These companies are the giants of data business: Google, Facebook, Amazon, Über, AirBnB. The standard small talk topics in data professionals’ discussions.

However, most of the companies are not data born. Most of the companies were originally established to serve a different purpose. They were founded to serve some physical needs and actually maintaining them physically, be it food, spare parts or factories. Obviously, all of these companies in  e.g. manufacturing and maintenance of physical things need data to operate. Yet, these companies are not organised around the principles of data born companies and capabilities to harness data as the driving force of their businesses.

We hear a lot of stories and successful examples about how data born companies apply augmented intelligence and other latest technology achievements. Surely, technologies build around of data are important. The key question to me is: what, in practice, is our capability to harness all of these opportunities in companies that are not data born?

In my daily practice I see excels floating around and between companies. A lot of manual work caused by unstandardised data, poor governance and bad data quality. Manual data work simply prevents companies to harness the capabilities created by data born companies. Yet, most of the companies follow the data born track without sufficient reflection. They adopt the latest technologies used by the data born companies. They rephrase same slogans: automation, advanced analytics, cognitive computing etc. And yet, they are not addressing the fundamental and mundane issues in their own capabilities to be able to make business and create value with data. Humans are doing machine’s job.

Why? Many things relate to this, but data quality and standardization are still pressing problems in every day practice in many companies. Let alone between companies. We can change this. The rest of us can reborn from data just by taking a good look of our mundane data practices instead of aspiring to go for the next big thing.

P.S. The Google Brain team had reddit a while ago and they were asked “what do you think is underrated?

The answer:

“Focus on getting high-quality data. “Quality” can translate to many things, e.g. thoughtfully chosen variables or reducing noise in measurements. Simple algorithms using higher-quality data will generally outperform the latest and greatest algorithms using lower-quality data.”

https://www.reddit.com/r/MachineLearning/comments/4w6tsv/ama_we_are_the_google_brain_team_wed_love_to/

About Harri Juntunen:

Harri is seasoned data provocateur and ardent advocate of getting the basics right. Harri says: People and data first, technology will follow.

You can contact Harri here:

+358 50 306 9296

harri.juntunen@twinspark.fi

www.twinspark.fi

 

Everyday Digital Transformation

Ben Rund of Informatica has a Youtube video running these days with the title/question: Enough Heard on Digital Transformation by Uber & AirBnB?

I share this sentiment with Ben. You don’t have to disrupt the whole world to take part in digital transformation and you don’t have to start something completely new. As an established enterprise you can transform your current business and combine the good things from the past with the new opportunities aroused from the digital evolution.

Forrester, the other analyst firm, some years ago devided digital transformation into a loop of:

  • Digital Customer Experience
  • Digital Operational Excellence

The below figure visualizes this landscape:

digital

What I would like to elaborate on related to this picture is the business ecosystem of your enterprise, which must be included in the everyday digital transformation.

Let’s take the example of product information management:

However, connect is better than collect. If you are dependent on receiving spreadsheets with product information from your trading partners or you let them put their spreadsheets into your supplier product data portal, you have an everyday digital transformation in front of you.

The solution for that is Product Data Lake.

digital2