Which MDM and/or PIM Solution to Choose?

More and more organizations are implementing Master Data Management (MDM) and Product Information Management (PIM) solutions.

When the implementation comes to the phase where you must choose one or more solutions and you go for the buy option (which is recommended), it can be hard to get a view on the available solutions. You can turn to the Gartner option, but their Quadrant only shows the more expensive options and Gartner is a bit old school as reported here.

An additional option will be to see how the vendors themselves present their solutions in a crisp way. This is what is going on at The Disruptive Master Data Management Solutions List.

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As a solution provider you can register your solution on this site in order to be a solution considered by organizations looking for a:

  • Master Data Management (MDM) solution
  • Customer Data Integration (CDI) solution
  • Product Information Management (PIM) solution
  • Digital Asset Management (DAM) solution

Registration takes place here.

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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.

How to Improve Completeness of Data

Completeness is one of the most frequently mentioned data quality dimensions. The different data quality dimensions (as completeness, timeliness, consistency, conformity, accuracy and uniqueness) sticks together, and not at least completeness is an aim in itself as well as something that helps improving the other data quality dimensions.

“You can’t control what you can’t measure” is a famous saying. That also applies to data quality dimensions. As pondered in the post Hierarchical Completeness, measuring completeness is usually not something you can apply on the data model level, but something you need to drill down in hierarchies and other segmentation of data.

Party Master Data

A common example is a form where you have to fill a name and address. You may have a field called state/province. The problem is that for some countries (like USA, Canada, Australia and India) this field should be mandatory (and conform to a value list), but for most other countries it does not make sense. If you keep the field mandatory for everyone, you will not get data quality but rubbish instead.

Multi-Domain MDM and Data Quality DimensionsCustomer and other party master data have plenty of other completeness challenges. In my experience the best approach to control completeness is involving third party reference data wherever possible and as early in the data capture as feasible. There is no reason to type something in probably in a wrong and incomplete way, if it is already digitally available in a righter and more complete way.

Product Master Data

With product master data the variations are even more challenging than with party master data. Which product information attributes that is needed for a product varies across different types of products.

There is some help available in some of the product information standards available as told in the post Five Product Classification Standards. A few of these standards actually sets requirements for which attributes (also called features and properties) that are needed for a product of certain classification within that standard. The problem is then that not everyone uses the same standard (to say in the same version) at the same time. But it is a good starting point.

Product data flows between trading partners. In my experience the key to getting more complete product data within the whole supply chain is to improve the flow of product data between trading partners supported by those who delivers solutions and services for Product Information Management (PIM).

Making that happen is the vision and mission for 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.

5 Product Data Levels to Consider

When talking about Product Master Data Management (Product MDM) Product Information Management (PIM) I like to divide the different kinds of product data into the schema below:

Five levels

Level 1, Basic Data

At the first level, we find the basic product data that typically is the minimum required for creating a product in any system of record.

Here we find the primary product identification number or code that is the internal key to all other product data structures and transactions related to the product within an organization.

Then there usually is a short product description. This description helps internal employees identifying a product and distinguishing that product from other products. Most often the product is named in the official language of the company.

If an upstream trading partner produces the product, we may find the identification of that supplier here too. If the product is part of internal production, we may have a material type telling about if it is a raw material, semi-finished product, finished good or packing material.

Level 2, Trading Data

The second level has product data related to trading the product. We may have a unique Global Trade Item Number (GTIN) that may be in the form of an International – former European – Article Number (EAN) or a Universal Product Code (UPC). Here we have commodity codes and a lot of other product data that supports buying, receiving, selling and delivering the product.

Level 3, Recognition Data

On the third level, we find the two basic pieces of product information that came to existence when we started producing product catalogues and had the first ecommerce solutions online.

The extended product description is needed because the usual short product description used internally have no meaning to an outsider as told in the post Customer Friendly Product Master Data. Some good best practices for governing the extended product description is to have a common structure of how the description is written, not to use abbreviations and to have a strict vocabulary as reported in the post Toilet Seats and Data Quality.

We often see that the extended product descriptions need to be present in the range of languages covering the locations where business is done either if the business is international or done in a country with multiple countries. The trend of increased user customization (or should I say customisation) drives this point further.

Having a product image is pivotal if you want to sell something without showing the real product face-to-face with the customer or other end user. A missing product image is a sign of a broken business process for collecting product data as pondered in the post Image Coming Soon.

Level 4, Self-service Data

At the fourth level, we have three main sorts of product information: Product attributes, basic product relations and standard digital assets. These data supports when customers makes buying decisions within eCommerce and other self-service scenarios.

Product attributes are also sometimes called product properties or product features. These are up to thousands of different data elements that describes a product. Some are very common for most products like height, length, weight and colour. Some are very specific to the product category. This challenge is the reason of being for dedicated Product Information Management (PIM) solutions as told in the post MDM Tools Revealed.

Basic product relations are the links between a product and other products like a product that have several different accessories that goes with the product or a product being a successor of another now decommissioned product. Product relations is described further in the post Related Products: The Often Overlooked Facet of PIM.

Standard digital assets are documents like installation guides, line drawings and data sheets as examined in the post Digital Assets and Product MDM.

Level 5, Competitive Data

As the fifth level we find elements like on the fourth level, but usually these are elements that you won’t necessarily apply to all products but only to your top products where you want to stand out from the crowd and distance yourself from your competitors. If you are a reseller, you typically make these data yourself, where level 4 hard facts are delivered from the manufacturer, as examined in the post Using Internal and External Product Information to Win.

Special content are descriptions of and stories about the product above the hard features. Here you tell about why the product is better than other products and in which circumstances the product can to be used. A common aim with these descriptions is also Search Engine Optimization.

X-sell (cross-sell) and up-sell product relations applies to your particular mix of products and may be made subjective as for example to look at up-sell from a profit margin point of view. X-sell and up-sell relations may be defined from upstream by you or your upstream trading partners but also dripping down on the roof from the behaviour of your downstream trading partners / customers as manifested in the classic webshop message: “Those who bought product A also bought / looked at product B”.

Advanced digital assets are broader and more lively material than the hard fact line drawings and other documents. Increasingly newer digital media types as video are used for this purpose.

Product Classification, Product Pricing and Product Lifecycle Status

All of the above-mentioned levels of product information is supported by product classification. Usually we see product classification handled as a reference data type across Product Information Management (PIM), ERP and Product Lifecycle Management (PLM) where applicable.

Product pricing is usually also a subject mainly belonging to the ERP side of things.

Product Lifecycle Status again goes across Product Information Management (PIM), ERP and not at least Product Lifecycle Management (PLM) where applicable.

Master Data Management (MDM) is the discipline that connects the dots between these topics.

Take the processes to next level:

You can take your Product Information Management (PIM) and Product Master Data Management (Product MDM) to a higher level by following the processes as described in the post Using Pull or Push to Get to the Next Level in Product Information Management.

The 360 Ways to Improving Customer Experiences

In today’s blog post over on The Disruptive Master Data Management Solutions List the CEO of AllSight, David Corrigan, examines 3 Reasons MDM No Longer Delivers a Customer 360.

In here David explores the topics in the new era of the customer 360 degree view being encompassing all customer data, covering analytical and operational usages and improving customer experience.

The post includes this testimonial from Deotis Harris, Senior Director, MDM at Dell EMC: “We saw an opportunity to leverage AllSight’s modern technology (Customer Intelligence), coupled with our legacy systems such as Master Data Management (MDM), to provide the insight required to enable our sellers, marketers and customer service reps to create better experiences for our customers.”

By the way: Being a MDM practitioner who have spent many years with customer 360 and now spending equal chunks of time with product 360, I find the forward-looking topics being very similar between customer 360 and product 360. In short:

  • The span of product data to handle has increased dramatically in recent years as told in the post Self-service Ready Product Data.
  • We can use the same data architecture for analytical and operational purposes as mentioned in the post The Intersection of MDM and Big Data.
  • It is all about creating better experiences for your customers.

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Welcome AllSight on the Disruptive MDM List

I am thrilled to welcome AllSight as the next disruptive MDM solution on The Disruptive Master Data Management Solutions list.

AllSight2I resonate very well with the AllSight Advantage that is: “The hardest part about understanding the customer is representing them within archaic systems designed to manage ‘customer records’.  AllSight manages all customer data in its original format.  It creates a realistic and accurate likeness of who your customer actually is.  Really knowing your customer is the first step to being intelligent about your customers.”

A true disruptive approach in my eyes.

Check out the full Disruptive Master Data Management Solutions list here.

The Intersection of MDM and Big Data

Back in 2015 Gartner, within a Magic Quadrant for MDM, described two different ways observed in how you may connect big data and master data management as reported in the post Two Ways of Exploiting Big Data with MDM.

In short, the two ways observed were:

  • Capabilities to perform MDM functions directly against copies of big data sources such as social network data copied into a Hadoop environment. Gartner then found that there have been very few successful attempts (from a business value perspective) to implement this use case, mostly as a result of an inability to perform governance on the big datasets in question.
  • Capabilities to link traditionally structured master data against those sources. Gartner then found that this use case is also sparse, but more common and more readily able to prove value. This use case is also gaining some traction with other types of unstructured data, such as content, audio and video.

In my eyes the ability to perform governance on big datasets is key. In fact, master data will tend to be more externally generated and maintained, just like big data usually is. This will change our ways of doing information governance as for example discussed in the post MDM and SCM: Inside and outside the corporate walls.

Eventually, we will see use cases of intersections of MDM and big data. The one I am working with right now is about how you can improve sharing of product master data (product information) between trading partners. While this quest may be used for analytical purposes, which is the said aim with big data, this service will fundamentally serve operational purposes, which is the predominant aim with master data management.

This big data, or rather data lake, approach is about how we by linking metadata connects different perceptions of product information that exists in cross company supply chains. While everyone being on the same standard at the same time would be optimal, this is quite utopic. Therefore, we must encourage pushing product information (including rich textual content, audio and video) with the provider’s standard and do the “schema-on-read” stuff when each of the receivers pulls the product information for their purposes.

If you want to learn more about how that goes, you can follow Product Data Lake here.

MDM and Big Data

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.

 

Welcome Riversand on The Disruptive MDM List

I am delighted to welcome Riversand as the next disruptive MDM / PIM / DAM solution on The Disruptive Master Data Management Solutions list.

As a European MDM and PIM (Product Information Management) practitioner I have followed Riversand’s growing presence on the European scene both through being on the customer side in solution selection activities, on conferences and on the social media community and I have watched that MDM / PIM peers as Mark Thorpe and Ben Rund have joined the company.

Riversand is in a very exciting development with a recent funding that is used to accelerate Riversand’s global expansion and for further investment in its disruptive MDM platform. See how Riversand presents themselves on The Disruptive MDM List here.

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