In the burgeoning world of Big Data, Master Data Management and Master Data Governance are two areas of critical concern to businesses. Big Data is the term used to describe the large volume of data—both in structured and unstructured format—that daily engulfs a business. Unquestionably, those businesses that achieve a high degree of accuracy in master data are far ahead of the game in providing stability and accuracy for both their internal and external customers. But, what’s the difference between the two practices, which are sometimes used (perhaps erroneously) rather interchangeably?

Data Management Software

According to the online dictionary Wikipedia, “In business, Master Data Management (MDM) is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference.” Analyst house Gartner, in their 2018 version of the Magic Quadrant for Master Data, focused on the idea of what is commonly called a golden record for master data, defining such a record as a “system of record and to which all other systems of reference (copies) revert for final validation.”

It would seem that supreme accuracy is the goal in master data management, because for good or whatever is currently being called master data has become the gold standard for data within a company.

Conversely, Master Data Governance (MDG) is just one (important) piece of the master data management pie, along with what Gartner called “workflow and BPM, data modeling, information quality and MDM internal integration.” It might be more appropriate to describe the two terms as opposite sides of the same coin, what is defined by Forbes Magazine (November 2017) as MDM: the discipline and technology and MDG: the practices used to enforce it.


Or, perhaps to state it in easily relatable terms, if MDM is the frontier town engaged in riding herd in a data rodeo, then we’ll call MDG the steely-eyed sheriff doing everything he can to bring law and order so the rodeo can proceed without disruption.

So, what do successful businesses do with this highly prized Big Data? They harness it for Business Intelligence…along with one largely unspoken BIG worry: the quality of the data being used might be flawed and could hinder even basic functions such as running predictive analyses. They are well aware that the predictions are only as accurate as the data being used to formulate the calculations. Worse yet, not only will the flawed predictions have limited value, but will likely create bigger problems by falsely augmenting or devaluing results throughout the datasets.


  • Increased inflow of customers
  • Improved customer relations
  • Streamlined marketing campaigns
  • Effective social media campaigns
  • Prediction of sales trends
  • Decreased regulatory fines
  • Maximized income generation
  • Improved data security
  • Reduced time wasted and resources

What no one disagrees on is the need for absolute accuracy in data records. But, nowhere is this more necessary than in the material masters. Up and down supply chains, as well as within the company, inaccuracy within these records can wreak havoc on an Enterprise Resource Planning (ERP) system. For while Master Data Management does a stellar job of managing shared data and serves as planned, MDM, unfortunately, does not provide full governance. That’s where a system of data governance becomes necessary. Or to continue with our frontier town analogy, without a workable system of data governance in place, our frontier sheriff is shooting paper bullets at a bunch of ornery sidewinders with less than satisfactory results.

The problem is that most ERP systems aren’t designed with data governance as the first priority. A typical system allows data records to be created with many attributes along with short and long descriptions. However, ERP systems do not build the descriptions from the data input into the system. An ERP is also not discriminatory—whatever is keyed into the short and long descriptions is acceptable. Furthermore, most systems do not allow searches using the long description, should one be desired.

An attempt to refine the data in this situation could provide a temporary solution. But sadly, if new processes aren’t put into place protecting the newly standardized information, it will soon enough return to yet another inexact and ungoverned state. For sure, data monitoring can be provided wherein the ERP provider notices that an aberration has been detected and the business is notified of this fact. This gives the illusion that some data governance is occurring but creates a situation where no long-term resolution is being offered. Changes made to the data don’t last, and the business is pretty much right back where it started with a jumble of inaccurate, inconsistent and duplicate information permeating the material masters.


Taxonomy & Dictionary: Standardization and enhancement of material description.

Procedures: Guidelines on how data policies are created and implemented.

Technology: Scalable tools to enable governance capabilities.

Governance Metrics: Measures to monitor performance of data.

Policies, Principles & Standards: Guidelines and principles to enforce data governance.


  • Eliminates the need for massive spreadsheets or databases
  • Focuses on the material masters, our specialty
  • Changes can be made in batches or one line at a time
  • Scalable software that is configured to meet each customer’s needs
  • Can be integrated with any ERP/WMS system; grows with the business
  • Allows customers to import data from existing spreadsheets or external system
  • Templates can then be created for existing materials as well as new material masters and exported back into the system
  • The cloud-based software is designed to streamline the mastering process completely; standard abbreviations, definitions and templates can all be created for noun-adjective combinations
  • The proper taxonomy created enforces and quickly identifies any duplicate records
  • Standard material masters based on templates can be created
  • Helps facilitate the United Nations Standard Products and Services Code (UNSPSC)
  • The software has both the templates and infrastructure to clean the data and keep it consistent going forward

At QuadraDot, we define data governance as the overall management structure of an enterprise’s data that establishes and maintains high quality data throughout multiple platforms of a business. Furthermore, it is responsible for usability, cleanliness and security of all data from the time it is initiated as well as going forward.

We know that company stakeholders want to be invested in the process that will take their data woes and turn them around once and for all. That’s why we work with company stakeholders to pinpoint the state of their material masters now and where they want to be in the future. Once the gaps are branded, we’ll help you decide on the desired naming taxonomy and create templates to keep your newly cleansed date standardized to order and once in place, easily maintained.

Once again, the big guns of Master Data Management and Master Data Governance can be large and in charge with a boost from our Six Sigma led team of QuadraDot consultants. You might say there’s a new sheriff in town primed and ready to corral your data rodeo with QuadraDot’s Granite Material Masters™ software.


QuadraDot Master Data
Granite Material Masters