Data Quality Tools are Vital for Digital Transformation

The Gartner Magic Quadrant for Data Quality Tools 2019 is out. It will take you 43 minutes to read through, so let me provide a short overview.

Gartner says that “data quality tools are vital for digital business transformation, especially now that many have emerging features like automation, machine learning, business-centric workflows and cloud deployment models.”

The data quality software tools market was at 1.61 billion USD in 2017 which was an increase of 11.6% compared to 2016.

Gartner sees that end-user demand is shifting toward having broader capabilities spanning data management and information governance. Therefore, the data quality tool market continues to interact closely with the markets for data integration tools and for Master Data Management (MDM) products.

Among the capabilities mentioned is multidomain support meaning capabilities covering all the specific data subject areas, such as customer, product, asset and location. Interestingly Gartner continues to focus on customer as the one of several party data domains out there. In my experience, there are the same data quality challenges with vendor and other business partner data as well as with employee data.

According to Gartner, data quality tool vendors are competing to address shifting market requirements by introducing an array of new technologies, such as machine learning, interactive visualization and predictive/prescriptive analytics, all of which they are embedding in data quality tools. They are, according to Gartner, also offering new pricing models, based on open source and subscriptions.

The vendors included in the quadrant are positioned as seen below:

Gartner DQ 2019

If you want a full copy of the report you can, against providing your personal data, get it from Information Builders here.

Data Quality Dimensions in Motion

For the fifth year Dan Myers of DQMatters is making an Annual Dimensions of Data Quality Survey.

There are some very interesting findings when looking at the trend in the previous years surveys as seen in the figure below.

Data Quality Dimensions 2015 to 2018

Among the data quality dimensions included in this survey we see that the use of consistency, validity and not at least completeness has increased significantly over these years.

The possible use of consistency and completeness was examined here on the blog in the post Multi-Domain MDM and Data Quality Dimensions. Another dimension included in this post was uniqueness, which is a frequently addressed data quality dimension for customer master data in the quest of fighting duplicates in databases around.

You can now be part of the 2019 Annual Dimensions of Data Quality Survey here.

Several Sources of Truth about MDM / PIM Solutions

The previous post on this blog was about Forrester vs Gartner on MDM/PIM. This post was about who is recognized as a major Master Data Management (MDM) / Product Information Management (PIM) solution vendor by the analyst firm Forrester versus who is recognized as a major MDM solution provider by the analyst firm Gartner.

MDM Truths

Now, let us have a look into how the individual solution providers are ranked in either the same way or differently by these major analyst firms spiced with my humble take on where this will be going. In the cause of brevity, I will focus on vendors positioned by Forrester as an MDM /PIM leader or strong performer or by Gartner as an MDM leader, visionary or challenger.

Informatica is an MDM leader both with Forrester and Gartner. When it comes to PIM Forrester has Informatica a little behind the leaders and back in the days when Gartner had specific customer MDM and product MDM quadrants, Informatica did better in customer MDM versus product MDM. Informatica has strengthened their grip on customer MDM with the recent AllSight acquisition. Will be interesting to see what moves Informatica will take to catching up on the product MDM / PIM battle ground and thus consolidating their multidomain MDM leadership.

Orchestra Networks who was recently acquired by Tibco is a leader in the eyes of Gartner but a bit less prominent positioned as a strong performer in the eyes of Forrester. The question asked on the market is if Tibco, against how earlier acquisitions turned out, will be able uphold Orchestra’s position as examined in the post Tibco, Orchestra and Netrics.

Reltio is a leader in the Forrester wave but still a niche player in the Gartner quadrant. This may say more about Forrester versus Gartner than about Reltio. Forrester seems to focus more on where the market is going while Gartner emphasizes on where the market has gone.

Riversand is a strong MDM and PIM performer at Forrester and a visionary in the Gartner quadrant. Perhaps Gartner sees a bit more on the vision side and Forrester a bit more on the offering side, but all in all the two analyst firms seems to be in agreement about Riversand. I think Riversand is on a good track.

SAP is a strong performer in the Forrester wave and a strong challenger according to Gartner. A lot of SAP ECC clients have and will choose the SAP MDG offering based on IT landscape simplification considerations. The Forrester PIM wave has SAP trailing the other solutions, which corresponds with my impression, which is that the SAP Hybris offering is struggling with really being a PIM solution.

Semarchy just made a high jump into the Gartner MDM quadrant challenger zone and according to Forrester they have the strongest MDM strategy possible. No doubt about that Semarchy is going in the fast track.

Profisee just moved up from niche player to challenger in the latest Gartner MDM quadrant. However, they were not included in the Forrester MDM wave. In my eyes, Profisee belongs among the major MDM solution providers.

Stibo Sytems is a challenger in the Gartner MDM quadrant. Forrester has Stibo Systems as a PIM leader but less prominent as an MDM contender. Stibo Systems has been on the same track as Riversand going from being a PIM vendor to become a multidomain MDM vendor. Perhaps because they are self-funded, versus Riversand being funded from outside, their tracks seem different.

IBM hangs on as a challenger in the Gartner MDM quadrant. Forrester only have IBM as a contender both for MDM and PIM. Nevertheless, large companies, not at least in the financial sector, will continue to rely on IBM also when it comes to MDM.

EnterWorks is a PIM leader and also an MDM leader according to Forrester. According to Gartner they are still a niche player in MDM. Recently EnterWorks joined forces with WinShuttle as told in the post The Recent Coupling on the MDM Market. It is not unlikely, that the Forrester view and the Gartner view will be aligned in the future.

Pitney Bowes is a strong performer in the latest Forrester wave sliding a bit from being a leader two years ago. They are not included in the Gartner quadrant. Pitney Bowes need to promote themselves as an MDM vendor and come up with new stuff to remain a major player on the MDM market.

Magnitude Software, who’s MDM solution was formerly known as Kalido, has moved up from contender to be a strong performer in the Forrester Wave. They are not included in the Gartner quadrant. Will be exciting to see if Magnitude Software can reignite the momentum Kalido had back in the first MDM years. Agility Multichannel is a part of Magnitude Software and a strong PIM performer at Forrester – and in my eyes too.

Contentserv is a PIM leader on the Forrester wave. Contentserv is also an MDM niche player on the Gartner quadrant. inRiver and Salsify are strong PIM performers on the Forrester wave but not big enough (and perhaps not MDM focussed enough) to be on the Gartner MDM quadrant.

PS: You can learn more about many of solutions mentioned here – and some more – on The Disruptive Master Data Management Solutions List.

Governing Product Information

The title of this blog post is also the title of a presentation I will do at the 2019 Data Governance and Information Quality Conference in San Diego, US in June.

There is a little difference between how we can exercise data governance and information quality management when we are handling data about products versus handling the most common data domain being party data (customer, vendor/supplier, employee and other roles).

Multi-Domain MDM and Data Quality DimensionsThis topic was touched here on the blog in the post called Data Quality for the Product Domain vs the Party Domain.

The conference session will go through these topics:

  • Product master data vs. product information
  • How Master Data Management (MDM), Product Information Management (PIM) and Digital Asset Management (DAM) stick together
  • The roles of 1st party data, 2nd party data and 3rd party data in MDM, PIM and DAM
  • Business ecosystem wide product data management
  • Cross company data governance and information quality alignment

You can have a look at the full agenda for the DGIQ 2019 Conference here.

dgiq 2019

The relation between CX and MDM

The title of this blog post is also the title of a webinar I will be presenting on the 28th February 2019. The webinar is hosted by the visionary Multidomain MDM and PIM solution provider Riversand.

Customer experience (CX) and Master Data Management (MDM) must go hand in hand. Both themes involve multiple business units and digital environments within your enterprise and in the wider business ecosystem, where your enterprise operates. Master data is the glue that brings the data you hold about your customers together as well as the glue that combines the data you share about your product offering together.

To be successful within customer experience in the digital era you need classic master data outcomes as a 360-degree view of customers as well as complete and consistent product information. In other words, you need to maintain Golden Records in Multidomain MDM.

You also need to combine your customer data and your product data to get to the right level of personalization. Knowing about your customer, what he/she wants, and their buying behaviour is one side personalization. The other side is being able to match these data with relevant products that is described to a level that can provide reasonable logic against the behavioural data.

Furthermore, you need to be able to make sense of internal and external big data sources and relate those to your prospective and existing customers and the products they have an interest in. This quest stretches the boundaries of traditional MDM towards being a more generic data platform.

You can register to join and replay the webinar here.

webinar data lake

Get Your Hands Dirty with Data

When working with data management – and not at least listening to and reading stuff about data management – there is in my experience too little work with the actual data going around out there.

I know this from my own work. Most often presentations, studies and other decision support in the data management realm is based on random anecdotes about the data rather than looking at the data. And don’t get me wrong. I know that data must be seen as information in context, that the processes around data is crucial, that the people working with data is key to achieving better data quality and much more cleverness not about the data as is.

data management wordsBut time and again I always realize that you get the best understanding about the data when getting your hands dirty with working with the data from various organizations. For me that have been when doing a deduplication of party master data, when calibrating a data matching engine for party master data against third party reference data, when grouping and linking product information held by trading partners, when relating other master data to location reference data and all these activities we do in order to raise data quality and get a grip on Master Data Management (MDM) and Product Information Management (PIM).

Well, perhaps it is just me and because I never liked real dirt and gardening.

B2C vs B2B in Product Information Management

The difference between doing Business-to-Consumer (B2C) or Business-to-Business (B2B) reflects itself in many IT enabled disciplines.

Yin and yangWhen it comes to Product Information Management (PIM) this is true as well. As PIM has become essential with the rise of eCommerce, some of the differences are inherited from the eCommerce discipline. There is a discussion on this in a post on the Shopify blog by Ross Simmonds. The post is called B2B vs B2C Ecommerce: What’s The Difference?

Some significant observations to go into the PIM realm is that for B2B, compared to B2C:

  • The audience is (on average) narrower
  • The price is (on average) higher
  • The decision process is (on average) more thoughtful

How these circumstances affect the difference for PIM was exemplified here on the blog in the post Work Clothes versus Fashion: A Product Information Perspective.

To sum up the differences I would say that some of the technology you need, for example PIM solutions, is basically the same but the data to go into these solutions must be more elaborate and stringent for B2B. This means that for B2B, compared to B2C, you (on average) need:

  • More complete and more consistent attributes (specifications, features, properties) for each product and these should be more tailored to each product group.
  • More complete and consistent product relations (accessories, replacements, spare parts) for each product.
  • More complete and consistent digital assets (images, line drawings, certificates) for each product.

How to achieve that involves deep collaboration in the supply chains of manufacturers, distributors and merchants. The solutions for that was examined in the post The Long Tail of Product Data Synchronization.

Flying by Ultima Thule and Data Management

Ultima Thule is a name for a distant place beyond the known world and the nickname of the most distant object in the solar system closely observed by a man-made object today the 1st January 2019. Before the flyby scientists were unsure if it was two objects, a peanut formed object or another shape. The images probing what it is will be downloaded during the next couple of months.

You can make many analogies between exploring space and data management. On this blog the journey has passed the similarity between Neutron Star Collision and Data Quality. The Gravitational Waves in the MDM World has been observed and so has the Gravitational Collapse in the PIM Space. The notion of A Product Information Management (PIM) Solar System has also been suggested.

Happy New Year and wishing you all well in the data management journey beyond Ultima Thule.

Ultima Thule
Source: Nasa via BBC

Linked Product Data Quality

Some years ago the theme of Linked Data Quality was examined here on the blog.

As stated in the post a lot of product data is already out there waiting to be found, categorized, matched and linked.

Doing this is at the core of the Product Data Lake venture I am involved with. What we aim to do is linking product information stored using different taxonomies at trading partners, preferable by referencing international and industry standards as eCl@ss, ETIM, UNSPSC, Harmonized System, GPC and more.

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.

See more on the Product Data Link site, on the Product Data Link showcase page on LinkedIn or get in contact right away:

 

Become a Product Data lake ambassador!

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Work Clothes versus Fashion: A Product Information Perspective

Work clothes and clothes for private (and white collar) use are as products quite similar. You have the same product groups as shoes, trousers, belts, shirts, jackets, hats and so on.

However, the sales channels have different structures and the product information needed in sales, not at least self-service sales as in ecommerce, are as Venus and Mars.

Online fashion sales are driven by nice images – nice clothes on nice models. The information communicated is often fluffy with only sparse hard facts on data like fabrics, composition, certificates, origin. Many sales channel nodes only deal with fashion.

Selling work clothes, including doing it on the emerging online channels, does include images. But they should be strict to presenting the product as is. There is a huge demand for complete and stringent product information.

Work clothes are often sold in conjunction with very different products as for example building materials, where the requirements for product information attributes are not the same. Work clothes comes, as fashion, in variants in sizes and colors. This is not so often used, or used quite differently, when selling for example building materials.

At Product Data Lake we offer a product information sharing environments for manufacturers of work clothes and their merchants who may have a lot of other products in range with different product information requirements. We call it Product Data Syndication Freedom.

Work Clothes versus Fashion