MDM Alternative Facts

When searching for information about Master Data Management (MDM) solutions you will stumble on a lot of alternative facts.

Here are three more or less grave examples:

The MDM news is filled with yet a new market research report at sale for a few thousand US dollars. These reports look at first hand to be very thorough and information rich. But usually with a closer look you will become suspicious. It may be the mention of key players where often some is missing and a few actually mentioned will be companies more known from other trades. And the structure and content, as in the below example, seems to be a copy paste from other trades. Hmmm… “Production”, “Gross Margin” …. Seems to be more about the global cement market.

Market Research MDM.png

The next example is from an article called The 4 Best Master Data Management (MDM) Software Tools to Consider. Oracle, Profisee, Talend and SAP are all viable candidates. But the below justification for Oracle seems to be very little about MDM.

Oracle MDM

Finally, on the pedantic side, even the recognized analyst firms can make a mistake (or a copy paste from earlier years). Forrester places Informatica as a German company. Well, it is the Product Information Management (PIM) wave and Informatica got into PIM (now Product 360 MDM) by buying the German PIM vendor Heiler.

Infa as German.pngNope, there is no such thing as a single version of the truth.

Data Monetization and Data Quality

Traditionally data quality management has revolved around making data fit for purpose in various business processes and thus data quality has contributed indirectly to business outcomes, as the business benefits were measured and harvested by results created in these business processes.

This situation has also made it very hard to create distinct business cases for data quality improvement. Most often data quality improvement and related disciplines and data governance, Master Data Management (MDM) and Product Information Management (PIM) has been part of wider business cases concerning for example Customer Relationship Management (CRM) and eCommerce perhaps under an even wider specific business objective.

In today’s data driven business world and drastic rising top-level appetite for digital transformation we see more and more examples of how data can be used much more directly to create business outcome through new or fundamentally reshaped business services and business models.

WebinarsOne example close to me is how data quality via completeness of product information can lead directly to selling more online as told in the post Where to Buy a Magic Wand?

On the 7th August I will elaborate on these themes in a webinar together with Rado Kotorov. The webinar is hosted by Information Builders and you can learn more and register on the Data Monetization webinar here.

 

The Emperor´s New Term

Emperor_Clothes“No one dared to admit that he couldn’t see anything, for who would want it to be known that he was either stupid or unfit for his post?”

This is a quote from the story called The Emperors New Clothes by Hans Christian Andersen.

Having been in and around the IT business for nearly 40 years I have seen, and admittedly not seen, a lot. Inflated hype has always been there, and a lot of technologies, companies and gurus did not make it, but came out naked.

What will you say are the emperor’s new clothes within data management today. Here are some suggestions:

  • Social MDM (Social Master Data Management): The idea that master data management will embrace social profiles and social data streams. If not anything else, did GDPR kill that one?
  • Big Data: This term has been killed so many times. But were those always a staged murder?
  • Single source of truth: The vision that we can have one single source that encompasses everything we need to know about a business entity. This has been a long time running question. Will it ever be answered?

What is your suggestion?

Product Data Completeness

Completeness is one of the most frequently mentioned data quality dimensions as touched in the post How to Improve Completeness of Data.

ChecklistWhile every data quality dimension applies to all domains of Master Data Management (MDM), some different dimensions apply a bit more to one of the domains or the intersections of the domains as explained in the post Multi-Domain MDM and Data Quality Dimensions.

With product master data (or product information if you like) completeness is often a big pain. One reason is that completeness means different requirements for different categories of products as pondered in the post Hierarchical Completeness within Product Information Management.

At Product Data Lake we develop a range of cloud service offerings that will help you improve completeness of product data. These are namely:

  • Measuring completeness against these industry standards that have attribute requirements such as eClass and ETIM
  • For manufacturers measuring completeness against downstream trading partner requirements (if not fully governed by an industry standard).
  • For merchants measuring incoming completeness when pulling from merchants.
  • Measuring against completeness required by marketplaces.
  • Transforming product information to meet conformity and thereby ability to populate according to requirements
  • Translating product information in order to populate attributes in more languages
  • Transferring product information by letting manufacturers push it in their way and letting merchants pull it their way as described in the post Using Pull or Push to Get to the Next Level in Product Information Management.

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.

5 Vital Product Data Quality Dimensions

Data quality when it comes to product master data has traditionally been lesser addressed than data quality related to customer – or rather party – master data.

However, organizations are increasingly addressing the quality of product master data in the light of digitalization efforts, as high quality product information is a key enabler for improved customer experience not at least in self-service scenarios.

We can though still use most of the known data quality dimensions from the party master data management realm, but with the below mentioned nuances of data quality management for product information.

Completeness of product information is essential for self-service sales approaches. A study revealed that 81 % of e-shoppers would leave a web-shop with incomplete product information. The root cause of lacking product information is often a not working cross company data supply chain as reported in the post The Cure against Dysfunctional Product Data Sharing.

Timeliness, or currency if you like, of product information is again a challenge often related to issues in cross company supply chains. You can learn more about this subject in the post How to avoid Stale Product Data.

Conformity of product information is first and foremost achieved by adhering to a public standard for product information. However, there are different international, national and industry standards to choose from. These standards also comes in versions that changes over time. Also your variety of product groups may be best served by different standards. Learn more about Five Product Classification Standards here.

Consistency of product information has to be solved in two scopes. First consistency has to be solved internally within your organisation by consolidating diverse silos of product master data. This is often done using a Product Information Management (PIM) solution. Secondly you have to share your consistent product information with your flock of trading partners as explained in the post What a PIM-2-PIM Solution Looks Like.

Accuracy is usually best at the root, meaning where the product is manufactured. Then accuracy may be challenged when passed along in the cross company supply chain as examined in the post Chinese Whispers and Data Quality. Again, the remedy is about creating transparency in business ecosystems by using a modern data management approach as proposed in the post Data Lakes in Business Ecosystems.

Product DQ Dimension

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.

 

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.

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:

Neutron Star Collision and Data Quality

The scientific news of the day is the observed collision of two neutron stars resulting in gravitational waves, an extremely bright flash – and gold.

The connection between gravitational waves and Master Data Management (MDM) was celebrated here on the blog when those waves were detected for the first time as told in the post Gravitational Waves in the MDM World.

The ties to Product Information Management (PIM) was examined in the post Gravitational Collapse in the PIM Space.

Now we have seen a bright flash resembling what happens when two trading partners collide, as in makes business together encompassing sharing master data and product information. Seen from my telescope this improves data quality and thereby business outcome (gold, you know) as explained in the post Data Quality and Business Outcome.

Neutron Star Collide