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

Shipping Product Information

When looking out of the windows from Product maersk-seen-from-pdl-in-sunshineData Lake global headquarters (well, that is also our home office) we see our neighbour, which is the global headquarters of Maersk, a major worldwide operating shipping company.

In all humbleness we do very parallel business. Maersk is good at moving goods. We are going to move data about the goods. Product data or product information if you like.

The reason of being for a shipping company is that it would be very ineffective for each manufacturer of goods, if they should arrange and carry out the transportation of their manufactured goods to each distributor around the world. Furthermore, it would be equally ineffective, if each distributor should arrange and carry out the transportation of their range of goods to each reseller or large end buyer.

Until now, this ineffectiveness has unfortunately been the case when it comes to exchanging data about the goods. Manufacturers are asked by their distributors to provide product information in a different way for each – most often meaning in a different spreadsheet. And the same craziness repeats itself when it comes to exchanging data between distributors, resellers and large end users of product information.

At Product Data Lake we have set sail to end this insanity and bring digitalization to shipping of product information. Learn more about how exactly we will arrange that journey on Product Data Lake Documentation and Data Governance.

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Ways of Sharing Product Data in Business Ecosystems

Sharing product data within business ecosystems of manufacturers, distributors, retailers and end users has grown dramatically during the last years driven by the increased use of e-commerce and other customer self-service sales approaches.

At Product Data Lake we recently had a survey about how companies shares product data today. The figures were as seen below:

our survey

The result shows that there are different approaches out there. Spreadsheets still rules the world though closely, in this survey, followed by external data portals. Direct system to system approaches are also present while supplier portals seems to be not that common.

At the Product Data Lake we aim to embrace those different approaches. Well, regarding use of spreadsheets and digital asset files via eMail our embracement is meant to be that of a constrictor snake. The Product Data Lake is the solution to end the hailstorms of spreadsheets with product data within cross company supply chains.

For external data portals, the Product Data Lake offers the concept of a data reservoir. A data reservoir in the Product Data Lake can be with an industry focus or with a special focus on certain data elements as for example sustainability data as described in the post Sustainability Data in PIM.

Direct systems to system exchange can be orchestrated through the Product Data Lake and supplier portals can served by the Product Data Lake. In that way existing investments in those approaches, that typically are implemented to serve basic data elements shared with your top trading partners, can be supplemented by a method that caters for exchange with all your trading partners and covering all data elements and digital assets.

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Take an Ultra Short Survey on Product Data Exchange

How do you exchange product data with your trading partners today? At the Product Data Lake we would like to know some more about that. We do expect that many still send eMails with spreadsheets and digital assets. But please tell us how it is with you. Take the survey by clicking here.

Survey

Also please comment on this blog post on your plans or if you work with Product Information Management (PIM) as a service provider and have experiences to share.

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Data Management for Business Ecosystems

Business ecosystems is an important concept of the digital age. The father of business ecosystems, James F. Moore, defined business ecosystems as:

“An economic community supported by a foundation of interacting organizations and individuals—the organisms of the business world. The economic community produces goods and services of value to customers, who are themselves members of the ecosystem. The member organisms also include suppliers, lead producers, competitors, and other stakeholders”.

The problem with data management methodologies and tools today, as I see it, is that they emphasizes on the needs inside the corporate walls of a single company without much attention to, that every single company is a member of one or several business ecosystems as examined in the post called MDM and SCM: Inside and outside the corporate walls.

Opening your data management, including your Master Data Management (MDM), up to the outside is scary business, as the ecosystems often will include your competitors as well as mentioned in the post Toilet Seats and Data Quality.

Nevertheless, if you want your company to survive in the digital age by building up your company’s digitilazation effort you have to extend your data management strategy to encompass the business ecosystems where you are a member.

And now some promotion:

Helene light 03
The Product Data Lake: A tool for business ecosystems

Take A Quick Tour around the Product Data Lake

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