Tibco, Orchestra and Netrics

Today’s Master Data Management (MDM) news is that Tibco Software has bought Orchestra Networks. So, now the 11 vendors in last year’s Gartner Magic Quadrant for Master Data Management Solutions is down to 10.

If Gartner is still postponing this year’s MDM quadrant, they may even manage to reflect this change. We are of course also waiting to see if newcomers will make it to the quadrant and make the crowd of vendors in there go back to an above 10 number. Some of the candidates will be likes of Reltio and Semarchy.

Else, back to the takeover of Orchestra by Tibco, this is not the first time Tibco buys something in the MDM and Data Quality realm. Back in 2010 Tibco bought the data quality tool and data matching front runner Netrics as reported in the post What is a best-in-class match engine?

Then Tibco didn’t defend Netrics’ position in the Gartner Magic Quadrant for Data Quality Tools. The latest Data Quality Tool quadrant is also as the MDM quadrant from 2017 and was touched on this blog here.

So, will be exciting to see how Tibco will defend the joint Tibco MDM solution, which in 2017 was a sliding niche player at Gartner, and the Orchestra MDM solution, which in 2017 was a leader at the Gartner MDM quadrant.

Tibco Orchestra Netrics

MDM Hype Cycle, GDSN, Data Quality, Multienterprise MDM and Product Data Syndication

Gartner, the analyst firm, has a hype cycle for Information Governance and Master Data Management.

Back in 2012 there was a hype cycle for just Master Data Management. It looked like this:

Hype cycle MDM 2012
Source: Gartner

I have made a red circle around the two rightmost terms: “Data Quality Tools” and “Information Exchange and Global Data Synchronization”.

Now, 6 years later, the terms included in the cycle are the below:

Hype Cycle MDM 2018
Source: Gartner

The two terms “Data Quality Tools” and “Information Exchange and Global Data Synchronization” are not mentioned here. I do not think it is because the they ever fulfilled their purpose. I think they are being supplemented by something new. One of these terms that have emerged since 2012 is, in red circle, Multienterprise MDM.

As touched in the post Product Data Quality we have seen data quality tools in action for years when it comes to customer (or party) master data, but not that much when it comes to product master data.

Global Data Synchronization has been around the GS1 concept of GDSN (Global Data Synchronization Network) and exchange of product data between trading partners. However, after 40 years in play this concept only covers a fraction of the products traded worldwide and only for very basic product master data. Product data syndication between trading partners for a lot of product information and related digital assets must still be handled otherwise today.

In my eyes Multienterprise MDM comes to the rescue. This concept was examined in the post Ecosystem Wide MDM. You can gain business benefits from extending enterprise wide product master data management to be multienterprise wide. This includes:

  • Working with the same product classifications or being able to continuously map between different classifications used by trading partners
  • Utilizing the same attribute definitions (metadata around products) or being able to continuously map between different attribute taxonomies in use by trading partners
  • Sharing data on product relationships (available accessories, relevant spare parts, updated succession for products, cross-sell information and up-sell opportunities)
  • Having shared access to latest versions of digital assets (text, audio, video) associated with products.

This is what we work for at Product Data Lake – including Machine Learning Enabled Data Quality, Data Classification, Cloud MDM Hub Service and Multienterprise Metadata Management.

What is Interenterprise Data Sharing?

The term “Interenterprise Data Sharing” has been used a couple of times by Gartner, the analyst firm, during the last two decades.

Lately it has been part of the picturing in conjunction with a recent research document with the title Fundamentals for Data Integration Initiatives.

Data Integration.png
Source: Gartner Inc with red ovals added

The term was also used back in 2001 in the piece about that Data Ownership Extends Outside the Enterprise. Here on the blog it was included in the title of the post about Interenterprise Data Sharing and the 2016 Data Quality Magic Quadrant.

In my eyes interenterprise data sharing is closely related to how you can achieve business benefits from taking part in the ecosystem flavor of a digital business platform. Some of the data types where we will see such business ecosystem platform flourish will be around sharing product model master data and data about and coming from things related to the Internet of Things (IoT) theme. This is further explained in the blog page about Master Data Share.

How MDM Solutions are Changing

When Gartner, the analyst firm, today evaluates MDM solutions they measure their strengths within these use cases:

  • MDM of B2C Customer Data, which is about handling master data related to individuals within households acting as buyers (and users) of the products offered by an organisation
  • MDM of B2B Customer Data, which is about handling master data related to other organizations acting as buyers (and users) of the products offered by an organisation.
  • MDM of Buy-Side Product Data, which is about handling product master data as they are received from other organisations.
  • MDM of Sell-Side Product Data, which is about handling product master data as they are provided to other organisations and individuals.
  • Multidomain MDM, where all the above master data are handled in conjunction with other party roles than customer (eg supplier) and further core objects as locations, assets and more.
  • Multivector MDM, where Gartner adds industries, scenarios, structures and styles to the lingo.

QuadrantThe core party and product picture could look like examined in the post An Alternative Multi-Domain MDM Quadrant. Compared to the Gartner Magic Quadrant lingo (and the underlying critical capabilities) this picture is different because:

  • The distinction between B2B and B2C in customer MDM is diminishing and does not today make any significant differentiation between the solutions on the market.
  • Handling customer as one of several party roles will be the norm as told in the post Gravitational Waves in the MDM World.
  • We need (at least) one good MDMish solution to connect the buy-sides and the sell-sides in business ecosystems as pondered in the post Gravitational Collapse in the PIM Space.

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.

Providing a Digital Technology Platform

Gartner, the analyst firm, defines five different types of digital technology platforms:

  • Information system platform — Supports the back office operations such as ERP, CRM, PIM and other core systems with associated middleware and development capabilities.
  • Customer experience platform — Contains the main customer-facing elements, such as customer and citizen portals, multichannel commerce, and customer apps.
  • Analytics and intelligence platform — Contains information management and analytical capabilities. Data management programs and analytical applications fuel data-driven decision making, and algorithms automate discovery and action.
  • IoT platform — Connects physical assets for monitoring, optimization, control and monetization.
  • Business ecosystem platform — Supports the creation of, and connection to, external ecosystems, marketplaces and communities.
Gartner Digital Platforms 2
Source: Gartner

As a vendor of a modern data management platform, you will probably identify yourself primarily within one of these five types.

At Product Data Lake we are first and foremost a business ecosystem platform, being a cloud service for sharing product data in the business ecosystems of manufacturers, distributors, merchants and the end users of product information. As such, we are proud to be a part of the The Rise of Business Ecosystems in Data Management.

Of course, there are ties to the other types of digital technology platforms as well. As explained in the post Adding Things to Product Data Lake, the ecosystem approach is necessary to identify and track physical assets. Analytics will encompass data, as for example product data, in the business ecosystem. Customer experience in multichannel commerce when it comes to completeness of product information will require an effective cross company digital technology platform.

An external focused business ecosystem platform will have to be easily connected to the various internal focused information system platforms at trading partners. In our case, this is What a PIM-2-PIM Solution Looks Like.

 

Can You Keep Track of MDM and PIM Vendors?

If you have the job to shortlist a range of MDM and/or PIM vendors to help you getting a grip on product master data (MDM) and detailed product features (PIM), or have the job to assist a client in doing so, you may have a hard time.

As mentioned in the post Disruptive Forces in MDM Land now Gartner (the analyst firm) only mentions the 11 most expensive MDM vendors. This leaves very little room for taking into account the differences in product specific offerings, geographic presence, industry focus and other parameters.

As a consequence, PIM and Product MDM Consultant Nadim Georges WARDÉ, who runs his business from Geneva in Switzerland, is keeping track of the vendors in his own comprehensive list and have kindly provided the list to be shared here on the blog:

Nadim Warde List

You can access the full spreadsheet here.

The list also has a small section on professional service vendors, vendors that have achieved substantial funding and finally a list of vendors supporting product serialization exchange within the pharma industry – a topic covered on this blog in the post Spectre vs James Bond and the Unique Product Identifier.

 

Why IBM Declined to Participate in The Gartner MDM Magic Quadrant

The latest Gartner Magic Quadrant for Master Data Management Solutions was published a month ago as touched in the post Disruptive Forces in MDM Land.

In the section about IBM, there were this note: “IBM declined to participate in this research and did not supply supplemental information. Gartner’s analysis is therefore based on other credible sources, including previous research input from IBM, customer inquiries, Peer Insights reviews submitted during the period covered by this research and other publicly available information.”

My guess is that Gartner and IBM already had a bad relation around the previous report which led to that this report was delayed a couple of months as told in the post Gartner MDM Magic Quadrant in Overtime.

old-schoolToday Nancy Hensley of IBM published a post called Understanding the new Gartner MDM Magic Quadrant and the IBM position. In here Nancy explains that IBM chose not to participate because IBM has a different point of view on where the MDM marketplace is going. In other words: The Gartner MDM market view is old school.

Perhaps magic quadrants, and analyst reports in general, are old school then. Perhaps the new school is that IBM and all the other vendors explain themselves – and can be reviewed by the (professional) crowd. Well, this is the idea behind The Disruptive MDM List.

Disruptive Forces in MDM Land

MDM 2017 disruptionFor the second time this year there is a Gartner Magic Quadrant for Master Data Management Solutions out. The two leaders, Orchestra Networks and Informatica, have released their free copies here and here.

Now Gartner have stopped having a list of vendors on the market too small to be in the actual quadrant. So, if you are looking for new thinking, you will have to read the section about disruptive forces in the MDM market.

Gartner says that every market experiences disruptive forces that influence its overall shape and trajectory over time, and that inspire innovation, both transformational and incremental. According to Gartner, those most prominent in the MDM market appear diametrically opposed.

The current market is dominated by vendors who have predominantly taken a platform-centric approach involving robust technology stacks categorized as application-neutral hub-based solutions. Thus, the business value of the resulting master data is realized through utilization of that data within business applications or suites, or analytics platforms, external to the MDM solution — such as CRM, ERP and e-commerce systems, and data warehouses.

One disruptive force against that is an increase in business applications or suites with embedded ADM (Application Data Management) capabilities that address organizational needs for data management, including MDM (to varying degrees), while also managing nonmaster data for the pertinent application. Gartner states that application-centric approaches for some organizations can return greater value than platform-centric approaches in the short term and do so at reduced cost.

The opposing disruptive force stems from the emergence of more generalized data management solutions. These provide for unified execution logic on top of what is effectively an integrated technology stack. Vendors envision the primary consumption model to be cloud-based subscription. As such, these solutions will also provide a means for midmarket organizations and SMBs to procure advanced data management capabilities (such as MDM) using this model of consumption. Executed crisply, cloud-based subscriptions to these solutions may even moderate the rise of cloud-based MDM offerings.

Regular readers of this blog may guess, that I see a coming third disruptive force in MDM land, being specialized data management services for business ecosystems as explored in the post Ecosystems are The Future of Digital and MDM.

The Product Data Domain and the 2017 Gartner Data Quality Magic Quadrant

data-quality-magic-quadrant-2017The Gartner Magic Quadrant for Data Quality Tools 2017 is out. One place to get it for free is at the Informatica site.

As data quality for product data is high on my agenda right now, I did a search for the word product in the report. There are 123 occurrences of the word product, but the far majority is about the data quality tool as a product with a strategy and a roadmap.

The right context saying about the product domain is, as I could distil it based on word mentioning, as follows:

Product data is part of multidomain

Gartner says that the product domain is a part of multidomain support, being packaged capabilities for specific data subject areas, such as customer, product, asset and location.

Some vendors were given thumbs up for including product data in the offering. These were:

  • BackOffice Associates has this strength: Multidomain support across a wide range of use cases: BackOffice Associates’ data quality tools provide good support for all data domains, with particular depth in the product data domain.
  • Information Builders has this strength: Multidomain support and diverse use cases: Deployments by Information Builders’ reference customers indicate a diversity of usage scenarios and data domains, such as customer, product and financial data.
  • SAS (Institute, not the airline) has this strength: Strong knowledge base for the contact and product data domains.

One should of course be aware, that other vendors also may have support for product data, but this is overshadowed by other strengths.

Effect on positioning

Multidomain brings vendors to the top right. Gartner’s metrics means that leaders address all industries, geographies, data domains and use cases. Their products support multidomain and alternative deployment options such as SaaS.

Product data focus can make a vendor a challenger. Gartner tells that challengers may not have the same breadth of offering as Leaders, and/or in some areas they may not demonstrate as much thought-leadership and innovation. For example, they may focus on a limited number of data domains (customer, product and location data, for example). This also means, that missing product data focus keeps vendors away from the top right positioning, which seems to be hitting Pitney Bowes and Experian Data Quality.

Product data will become more important, but is currently behind other domains

Gartner emphasizes that data and analytics leaders including Chief Data Officers and CIOs must, to achieve CEOs’ business priorities, ensure that the quality of their data about customers, employees, products, suppliers and assets is “fit for purpose” and trusted by users.

Organizations are increasingly curating external data to enrich and augment their internal data. Finally, they are expanding their data quality domains from traditional party domains (such as customer and organization data) to other domains (such as product, location and financial data).

According to Gartner, data quality initiatives address a wide variety of data domains. However, party data (for existing customers, prospective customers, citizens or patients, for example) remains the No. 1 priority: 80% of reference customers considered it the top priority among their three most important domains. Transactional data came second highest, with 45% of reference customers naming it among their top three. Financial/quantitative data was third, with 39% of reference customers naming it. The figure for product data was 34%.

In my view, the 34% figure is because not all organizations have high numbers of product data and have major business pains related to product data. But those who have are looking at data quality tool and service vendors for suitable solutions.