Growth Series BLOG

Its a Premium BLOG template and it contains Instagram Feed, Twitter Feed, Subscription Form, Blog Search, Image CTA, Topic filter and Recent Post.

All Posts

Why Taxonomy is Critical to Master Data Management (MDM)

Organizations are paying more and more attention to Master Data Management (MDM). MDM comprises a set of processes and tools that consistently defines and manages the non-transactional data entities of an organization, such as product data or customer data. 

According to a study by Aberdeen, companies using MDM are more than twice as likely to be satisfied with data quality and speed of delivery, compared to those not using it. 

MDM promises not just greater control over consistent reference data; but an ability to manage the relations between data entities in order to generate more effective business knowledge. From this perspective, MDM requires an understanding and agreement about the meaning of terminology.   Hence, the natural role of taxonomy. Taxonomy is about "semantic architecture" - it is about naming things and making decisions about how to map different concepts and terms to a consistent  structure.  

MDM challenges and the argument for taxonomy

Ambiguity.  The same term can have different meanings.  Taxonomy provides a hierarchy that helps remove ambiguity. It includes mechanisms for understanding context and making meaning precise.    

Consistency.  It can be difficult to get complete agreement on what terms to use.  Also, people will use terms inconsistently if given a choice. Sometimes, in legacy situations, different terms were used in the past and for various reasons the data can't be re-tagged to provide consistent metadata.  A thesaurus can map terms together to account for these inconsistencies. 

Connections. Taxonomies can also represent related concepts (technically also part of a thesaurus) that can be used to connect processes, business logic, or dynamic/related content to support specific tasks.

An MDM strategy defines the process for cleansing the data, harmonizing the attributes, and ensuring that all required information is present. 

But, master data management programs also need to leverage taxonomy, and taxonomy should make use of MDM initiatives. 

  • Although taxonomy is typically applied to unstructured content, it is increasingly supporting structured and transactional content. 
  • Similarly, master data plays an essential role in making unstructured information consistent, findable, and valuable. 

The following provides a brief example of key concepts and the role of taxonomy.  Note that the transactional data is on the left, the non-transactional persistent reference data on the right. 


transactional-vs-master-data

Let's look at the product master. 

We have two different manufacturers who both offer mechanical pencils.  In our product master, they are called the same thing.  However the original product manufacturers do not necessarily use the same terms to describe their products.  The original bills of lading might have contained the following: 
mdm-taxonomy

There are a couple of observations:

  1. The description uses abbreviations that are not user friendly. 
  2. The attributes are not consistent. 

One manufacturer classifies their product as Stationary and other calls it Home Office. Further, one abbreviates the attribute of Color as Bl and the other uses Blk. With these inconsistencies, it is impossible to deliver an excellent user experience where this data may need to be displayed. 

Bringing it all together with taxonomy and master data management 

Master Data Management fixes these inconsistencies by improving data quality.  Each supplier has a way or organizing and describing their products that may or may not be aligned and consistent. However, the retailer needs to drive a consistent user interface and experience to achieve the best business outcomes. 

  • A centralized repository where "the source of truth" exists 

  • Governance processes for fixing inconsistencies or providing feedback to suppliers 

  • Rules for automating remediation of predictable inconsistencies 

  • Tools for cleansing and normalizing the data (running scripts and converting the data) 

The role of taxonomy is even more important in multi-domain MDM, which is the direction in which the industry is heading. 

According to Gartner, 58% of the reference customers in its 2018 Magic Quadrant Report on Master Data Management Solutions are facing the requirement for multi-domain MDM.  

Whereas in the past, most MDM systems were focused on a single area such as product data or customer data, more organizations now want to bring data together from multiple domains, to allow for a broader range of business use cases and greater use of analytics. 

In order to conduct analytics across domains and develop effective governance programs, organizations need to set up consistent taxonomies and standard metadata, especially on their critical data. The data models will need to reflect a consistent taxonomy. Ultimately, the relationships among different taxonomies should be captured and documented through an ontology, but having an MDM with appropriate taxonomies is a foundational step to take.

Nothing about this is easy (or sexy) but it needs to be done if your initiatives are going to make headway.  Our team of information science experts can help.  Give us a shout if you'd like to talk.

Click to register for the upcoming webinar

Seth Earley
Seth Earley
Seth Earley is the Founder & CEO of Earley Information Science and the author of the award winning book The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable. An expert with 20+ years experience in Knowledge Strategy, Data and Information Architecture, Search-based Applications and Information Findability solutions. He has worked with a diverse roster of Fortune 1000 companies helping them to achieve higher levels of operating performance.

Recent Posts

Large Scale Digital Transformations — What to Do When the CEO Says “Just Make It Work”

A few years back, I was attending an executive presentation of project findings and recommendations for an outdoor products manufacturing company. The project remit was defining the future state digital architecture and a high-level plan to support the organization’s transformation of the customer experience. Our engagement lead had vetted the findings and recommendations with the client’s core team. The core team was excited about the level of detail, the plan’s comprehensiveness, and the consistent architecture across multiple tools that would enable an outstanding customer experience.

How Personalized Customer Experience Leads to Competitive Advantage

When you consider how customers interact with organizations these days, it quickly becomes apparent that much of that interaction is through digital channels. “CX” suggests a customer experience via laptops or mobile devices, and that digital experience is driven entirely by data. The question is, how do we make it the most relevant and seamless experience possible, given the needs and objectives of the user, and what data can we leverage to do so? In addition to voice of the customer feedback through surveys and social media monitoring (which provide high-level themes), three principal ways of leveraging data can be used in order to create an excellent customer experience:

Personalization - 3 Ways to Use Data to Guide Decisions

Personalization comes in multiple shapes and forms, many of which businesses can put to effective use. But they shouldn't make the mistake of launching all of them at once. An incremental approach works well here. And a good place to start is product hierarchies.