What is Master Data Management?


What is Master Data Management?

How normalizing your internal data can exponentially speed up your organization’s growth and profitability

Business leaders have been talking about master data management since the early 2000s, but it’s never been more important than right now.

  1. As we venture off into the 2020s, there are a few business trends that have already begun to prove themselves out.
  2. Almost all businesses (even traditionally physical ones) have gone online. This means a global reach and more complex processes.
  3. Sales cycles have shortened by a large margin due to the ever-increasing research and buying power of consumers.

Reliable and actionable data is now the precious currency of forward-thinking businesses.

In this post, we’ll concern ourselves with that last point.

As I write this, any single online consumer is generating dizzying amounts of structured and unstructured data. When we talk about master data management, though, we’re only talking about one specific, limited subset of all that data.

You’ve probably heard a lot about consumer data (perhaps even from this very blog). This is likely due to privacy concerns and related legislation (which the media is quick to exploit for clicks and views).

But master data is something different. It has to do with the values internal to a given company. Master data essentially structures all the applications that occur within an operation.

It’s essential to keep this data organized, normalized, visible, and clean. That’s where master data management comes in.

Read on to learn more about:

  • Master data and MDM
  • Why it’s so important
  • The basic steps to getting started with MDM implementation

What is Master Data?

A lot of the data you deal with on a day-to-day business might fall under the rubric of transactional data. That is, data about sales, orders, invoices, inventory, and so forth.

You might not think as often about master data. Master data is the consistent, uniform set of identifiers and attributes that describe the characteristics of a business’s core functions and activities.

As such, businesses use master data across several different kinds of business processes. In fact, you could call master data the reference for those other, transactional types of data. Transactional data depends on master data—without it, you couldn’t even make a sale.

Transactional data and customer data tend to be dynamic. They change day-to-day as organizations bring in new customers and make new sales. But master data classes tend not to change much. Their value is in their consistency.

Managing master data is a crucial part of perfecting an organization’s processes and communication. It keeps internal behavior uniform, consistent, and streamlined across all sectors and departments.

The larger and more complex a business is, the more that company should be leveraging its master data.

That’s because maintaining master data at the enterprise level breaks down information silos, allowing the business to more smoothly integrate its activities throughout various departments. The end result: A lighter, leaner, nimbler operation.

What Data is Considered “Master Data”?

While the number of datasets that could be designated as “master data” is theoretically unlimited, in practice, master data is not the sum total of all your data.

On the contrary, master data is a limited list of elements required for sharing and standardization.

So how do you zero in on what kind of data you should be managing as master data?

It starts with creating a common set of metadata classes for your business. This is an organizational task, not a technological one.

In other words, this is the important “back-of-the-napkin” work that’s necessary before implementing technology.

It requires stakeholders from across the business to work together to identify those master data classes (more on that below).

The type of master data you’ll need to manage is going to vary depending on the unique specifications of your business.

There is no “one-size-fits-all” classification scheme. But bearing that in mind, here are a few examples of how different businesses might organize their master data classes:

Enterprise A’s Master Data Classes

These classes are most common in startup to growth-sized companies. Most software companies will likely borrow data classes from this group.

  • Organizational hierarchies
  • Sales territories
  • Product roll-ups
  • Pricing lists
  • Customer segmentations
  • Preferred suppliers

Enterprise B’s Master Data Classes

These are common in large-capitalization companies with complex supply chains or many locations. Yet, there can be overlap between various company growth stages.

  • People
  • Customers
  • Prospects
  • Employees
  • Vendors
  • Suppliers
  • Partners
  • Places
  • Locations
  • Offices
  • Regional alignments
  • Things
  • Accounts
  • Assets
  • Services
  • Products
  • Whichever of these classes you choose to implement, it’s imperative that all stakeholders understand and agree on the criteria.

If folks aren’t clear on what an attribute means, then you’ll end up with serious flaws in your master data modeling.

It’s also crucial to ensure the quality of that master data. As we’ll see below, improving data quality is a key component of effective master data management.

Before getting started with MDM, businesses should audit their data collection and maintenance practices. This will help in assessing the value of their extant data.

What is Master Data Management (MDM)?

So, now that we’ve got a grip on what master data itself is, the question becomes: what is master data management?

MDM is a collection of data management practices that seek to solve two disparate, but linked goals.

  1. Unifying information across a company.
  2. Breaking down data silos.

MDM helps enhance the value and efficiency of the business by borrowing from:

  • Data management tools
  • Information management methods
  • Various business applications

Finally, MDM ensures easy integration and sharing of that data across the company.

Let’s back up for a second and talk about the dangers of data silos.

Whenever I write about silos, my mind immediately jumps to the large, metal cylinders found on farms and manufacturing plants.

Farmers and manufacturers use silos to store and protect various materials.

As a result, they’re tightly sealed to prevent even the slightest permutation from the surrounding air and elements.

But sealing data in a silo has an adverse effect. It causes the data to go sour and departments to operate with an incomplete picture of the business.

In other words, a data silo is what happens when different departments in the same business have disparate, stand-alone systems and applications, inconsistent processes, and fragmented data that may not cohere with that of other departments.

Why is Messy Master Data so Problematic?

Because more silos often lead to more operational barriers. Leaders are more likely to neglect the needs of other departments and duke it out over whose version of “the truth” is the right one.

It also leads to inaccurate, redundant, and incomplete data. That means low-value analytics, which in turn translates to uninformed business decisions.

Simply put, master data management exists to break down those dreaded siloes.

Let’s drill a little deeper into how MDM actually functions. Firstly, your typical master data management solution is made up of two parts:

  1. A technological component that profiles, consolidates, and synchronizes the master data
  2. A platform through which users can manage, clean, and enrich the master data

Stakeholders from across the business need to work with the IT department to get those two parts working in harmony.

Implementing MDM involves a rigorous, thorough, possibly unflattering assessment of your business operations.

You also must get enthusiastic buy-in from leadership as well as other “data stewards.”

These will likely be the leaders of data-heavy departments such as:

  1. Customer service
  2. Business intelligence
  3. Sales and marketing

Starting to sound like a lot? It is! Most businesses today, particularly those functioning in a data-driven contexts (like sales), intuitively know how important MDM is.

They just don’t know where to start. Or, perhaps they’re not confident all the effort in cleaning their data will produce a positive ROI.

But master data management pays dividends pretty quickly. Think of it as the foundation for everything else you can do with your data.

Accurate, useful predictive analytics, for example, is only possible with MDM.

It can be a powerful tool to help firms run smarter and faster. In short, MDM helps you spend energy on what matters most instead of wasting energy on inefficiencies.

Ultimately, the benefits of MDM include:

  • Cost savings
  • Operational efficiency
  • Operational effectiveness
  • Abilities to provide better customer service
  • More creative problem-solving
  • Better innovation
  • Revenue maximization

And here’s the thing: though the task may look daunting (and, at times, feel that way, too), it actually isn’t as time-intensive as you might think.

Industry experts say efficient MDM processes can be designed and implemented in a business in as little as six to 10 months.

How to Get Started with Master Data Management

As we touched on above, establishing master data management within an enterprise is a complex process. It entails several steps and must incorporate many viewpoints.

But all those steps must be specific to your business. You can’t swipe a process or solution from a competitor, because they face factors that don’t impact you the same way.

In fact, this is the key to getting started with implementing MDM. You must figure out how to align your master data management processes with the unique mission, vision, and priorities of your business.

Hence, a great first step is holding a strategic planning exercise with all involved stakeholders. The goal should be to generate a maximum of five goals for the process.

That’s right, no more than five—your strategy needs to be laser-focused to be effective.

Secondly, it’s also crucial to get buy-in from a so-called “executive sponsor.”

This should be someone from the C-Suite who believes in data and will be willing to do what’s necessary to secure resources for your MDM effort.

This specifically means the human and financial capital needed to implement and sustain your MDM strategy.

That strategic planning exercise we mentioned above could be a good opportunity to identify such a sponsor for the project.

Third, everyone else needs a role, too! Data stewardship is essential to success in MDM, and each person at each data touch point needs to know what their role is and receive support in how to perform it.

As we noted above, that means people on both the IT side and the business side of the firm, as well. We’ll go more into detail about that below.

Finally, a major priority when getting started with MDM is assessing and improving data quality.

Though we did say above that master data management is a workflow process, not a form of technology, the truth is this is one area where technology can help.

Take a look at tools such as the AI-powered solutions offered by Accent Technologies that can help you improve your data quality via automatic data capture and data enrichment.

Who needs to be involved in MDM?

MDM is a highly interactive social process that involves several different stakeholders. It must yield to divergent viewpoints coming from different departments.

This wide spectrum of metadata management stakeholders includes:

  • Top management
  • Process owners
  • Data users
  • Employees who enter data
  • IT experts
  • Business intelligence (BI) experts

And more, depending on the nature of the business

That’s why clear and continuous communication among all parties is so essential to this process. Each team member must feel that their needs are being considered.

Creating master data lists

Now that we’ve laid the foundation, it’s time to get into the nitty gritty of how to create all that master data that you need to manage.

Step one: Develop a master data model

This is the most critical step in laying the foundation for master data management.

Here, you and your team members decide what the master data classes are, what attributes they include, what type of data those attributes are, etc.

It’s crucial here, like we said above, to stay laser-focused and streamlined. The master data model should start lean.

Consider these examples of MDM lists. They’re simple, easy-to-grasp data classes. They include classes like customer data, employee data, and product data.

Remember, you must unify this data across the entire organization. You’re driving a cruise ship, not a jet ski.

Finally, while you design your master data model, you also need to map the data classes and attributes to the actual sources of data your business utilizes.

Step two: Standardize and sanitize data

There are so many potential terms for describing data in sales. Is it a “customer” or a “user”? Or how about when they’re in your funnel. Are they “leads,” “prospects,” or “MQLs”?

This step is all about getting everyone reading from the same piece of music, so to speak.

That is to say, you need to change the data you have on hand to meet the requirements of the master data model. This includes procedures like:

Standardizing data formats (as mentioned in the above paragraph)

Inserting missing data values

Standardizing data values

There are several software tools out there that can do a lot of this for you. You’ll need such a tool, since your business is likely swimming in an ocean of data that’s far too vast for your employees to tackle.

Step three: Eliminate data redundancies

Part of dismantling the dreaded data silos in any business is getting rid of duplicate data.

Dealing with duplicate or junk data within a single department can often be a full-time job. Anyone with experience in a CRM or marketing automation platform will tell you that.

But trying to manage duplicate data across several departments? That’s a next-level challenge.

That means running various data records together to identify and eliminate duplicate entries.

This requires the collection of all data from all databases into a single source of truth: one database to rule them all.

Maintaining master data lists

When we gave our master data management definition above, we noted that master data tends to stay relatively constant. However, some degree of change is inevitable.

After you’ve gone through the above steps to compile your master list, then you also need to have procedures in place to maintain that list going forward.

Businesses usually opt for one of three routes when it comes to master data maintenance:

The single copy approach

This entails having one single master data list, and any changes that arise are recorded directly on that list. Any application relying on master data is then rewritten to draw on this newly recorded data.

That means that this particular route is typically the least feasible for most businesses. Constantly rewriting applications would be, at the very least, incredibly time-consuming and expensive. At worst, it’s downright impossible.

The single maintenance, multiple copies approach

This data is modified, again, on a single master list, but those modifications are then also delivered to the source systems, which locally store their own master data copies. Each application then updates the new data.

The continuous merge approach

This means applications can modify their copy of the master data, and those changes are then merged into the master list. On paper, this is the most efficient, high-value road to take, but be warned that it does come with some potential pitfalls, such as update conflicts.

Next Steps for Successful Master Data Management

As we’ve gone through the fundamentals of master data management, we’ve touched quite a bit on the critical importance of company-wide communication and the sharing of information throughout the process.

In your own MDM journey, as you research different processes and possibilities, take a look at the technological tools currently on the market that could help to ease your path to holistic information integration.

Accent Technologies’ AI-driven solutions go beyond automatic data capture and enrichment. Accent’s Integration Solution, for instance, can help you consolidate all your data (even from custom and third-party data sources!) into one platform that can be easily accessed and viewed by the entire team.

It also lets you integrate and pull or push content and information from any content repository, allowing for easy access and unified search from one convenient place.

There’s a bevy of such tools currently available, with more constantly entering the marketplace as technology continues to develop at breakneck speed. Spend some time familiarizing yourself with different options to find the solution that matches best with your company’s needs.

Final Thoughts

Accent Technologies is the first and only SaaS company to bring together Sales AI and Content Management in a true Revenue Enablement Platform. We provide both sales and marketing with better visibility into the performance of their teams.

This drives revenue through intelligent recommendations for complex sales scenarios and provides the data for rich analytics that power better coaching, forecasting, and long-term customer support. Learn more about our solutions or request a live demo to see it in action.

By Accent Technologies

30th April 2021

Master Data Management the Accent Way

The first step in master data management is actually getting your hands on the data.

Leveraging sales data and analytics has become a necessity for revenue teams to stay competitive. With that, teams are looking to ensure they are gathering data and insights from all of their available sources and warehouses.

Learn how Accent can help you access, centralize, and analyze all of your sales activity data so that you can deliver actionable insights for your team.