We Need More Product Data Lake Ambassadors

ambassador

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:

IT is not the opposite of the business, but a part of it

Yin and yangDuring my professional work and not at least when following the data management talk on social media I often stumble upon sayings as:

  • IT should not drive a CRM / MDM / PIM /  XXX project. The business should do that.
  • IT should not be responsible for data quality. The business should be that.

I disagree with that. Not that the business should not do and be those things. But because IT should be a part of the business.

I have personally always disliked the concept of dividing a company into IT and the business. It is a concept practically only used by the IT (and IT focused consulting) side. In my eyes, IT is part of the business just as much as marketing, sales, accounting and all the other departmental units.

With the raise of digitalization the distinction between IT and the business becomes absolutely ridiculous – not to say dangerous.

We need business minded IT people and IT savvy business people to drive digitilization and take responsibility of data quality.

Used abbreviations:

  • IT = Information Technology
  • CRM = Customer Relationship Management
  • MDM = Master Data Management
  • PIM = Product Information Management

Who will become Future Leaders in the Gartner Multidomain MDM Magic Quadrant?

Gartner emphasizes that the new Magic Quadrant for Master Data Management Solutions Published 19 January 2017 is not solely about multidomain MDM or a consolidation of the two retired MDM quadrants for customer and product master data. However, a long way down the report it still is.

If you want a free copy both Informatica here and Riversand here offers that.

The Current Pole Position and the Pack

The possible positioning was the subject in a post here on the blog some while ago. This post was called The Gartner Magic Quadrant for MDM 2016. The term 2016 has though been omitted in the title of the final quadrant probably because it took into 2017 to finalize the report as reported in the post Gartner MDM Magic Quadrant in Overtime.

Below is my look at the positioning in the current quadrant:

mdm-mq

Starting with the multidomain MDM point the two current leaders, Informatica and Orchestra, have made their way to multidomain in two different ways. Pole position vendor Informatica has used mergers and acquisitions with the old Siperian MDM solution and the Heiler PIM (Product Information Management) solution to build the multidomain MDM leadership. Orchestra Networks has built a multidomain MDM solution from the gound.

The visionary Riversand is coming in from the Product MDM / PIM world as a multidomain MDM wannabe and so is the challenger Stibo. I think SAP is in their right place: Enormous ability to execute with not so much vision.

If you go through the strengths and cautions of the various vendors, you will find a lot of multidomain MDM views from Gartner.

The Future Race

While the edges of the challengers and visionaries’ quadrants are usually empty in a Gartner magic quadrant, the top right in this first multidomain MDM quadrant from Gartner is noticeably empty too. So who will we see there in the future?

Gartner mentions some interesting upcoming vendors earning too little yet. Examples are Agility Multichannel (a Product Data Lake ambassador by the way), Semarchy and Reltio.

The future race track will according to Gartner go through:

  • MDM and the Cloud
  • MDM and the Internet of Things
  • MDM and Big Data

PS: At Product Data Lake we are heading there in full speed too. Therefore, it will be a win-win to see more MDM vendors joining as ambassadors or even being more involved.

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

 

MDM: The Technology Trends

There are certainly many things going on in the Master Data Management (MDM) realm when it comes to technologies applied.

The move from on premise based solutions to cloud based solutions has been visible for some years. It is not a rush yet, but we see more and more master data services being offered as cloud services as well as many vendors of full stack MDM platforms offers both on premise, cloud and even hybrid solutions.

As reported in the post Emerging Database Technologies for Master Data new underlying database technologies are put in place instead of the relational database solutions that until now have ruled the MDM world. As mentioned graph databases as Neo4J and document databases as MongoDB (which now also support graph) are examples of new popular choices.

blockchainAs examined by Gartner (the analyst Firm) there are Two Ways of Exploiting Big Data with MDM, either doing it directly or by linking. Anyway, the ties between big data and master data management is in my eyes going to be a main focus for the technology trends in the years to come. Other important ties includes the raise of Industry 4.0 / Internet of Things and blockchain approaches.

We are still waiting for The Gartner Magic Quadrant for Master Data Management Solutions 2016 and the related Critical Capabilities document, so it will be very exciting, in fact more exciting that the vendor positioning, to learn about how Gartner sees the technology trends affecting the MDM landscape.

What are your expectations about Master Data Management and new emerging technologies?

Gartner MDM Magic Quadrant in Overtime

The Gartner Master Data Management Solutions Magic Quadrant 2016 did not go live in 2016. Estimated release date was 19th November 2016, but still there is no sign of the quadrant either on the Gartner site or at vendor bragging on social media.

We can only guess about why the quadrant is delayed, but a possible explanation is that vendor feedback on the suggested positioning has been harsh. I am not among the ones who believes Gartner actually takes money from vendors for inclusion and positioning in the quadrant. Still, Gartner has a substantial business relationship with those vendors. If a vendor feels they are really wrongly misplaced, they may question the judgement in the other payable services from Gartner.

While waiting, there is still time to have your guess on who has persuaded Gartner to be where in the quadrant as already many have done in the post The Gartner Magic Quadrant for MDM 2016.

And yes, the prize for best guess is still a genuine Product Data Lake t-shirt.

t-shirt