The ROI of data synchronicity
It’s an undeniable fact: data is a core asset required for organizations to perform, compete and thrive more successfully, regardless of industry or discipline. But despite the acknowledged importance of data to doing business today, the way it is handled for use tends to be overlooked or taken for granted. Data is often incorrectly captured, curated or utilized. Consequently, this valuable asset may not just be ineffective, it may also be detrimental to an organization.
Remember when NASA lost the Mars Climate Orbiter? The cause behind this blunder: the engineering team responsible for developing the Orbiter used English units of measurement while NASA used the metric system. This information was critical to providing the correct orbit coordinates. A relatively small data inconsistency cost NASA the loss of the $125 million spacecraft.
Master data management, which curates, defines and synchronizes data for trusted use within the enterprise, should be viewed as an organizational necessity and a competitive advantage.
 In survey after survey, decision makers have responded overwhelmingly that high quality, trusted data is key to the success of an organization. For example, a 2023 Experian study showed that 87% of the leadership surveyed said that trusted data plays a role in responding to market disruption. Yet in another survey from TDWI, 41% of companies admit to not having any data quality strategy in place. |
Master data management: conforming data across the enterprise
Master data management (MDM) is a key component to implementing a sound data quality strategy. Conforming data across the enterprise, MDM acts as a lynchpin of sorts between the disparate shared data sources. It provides a single trusted view across departments and systems.
Master data management solves numerous aspects of the data quality predicament, including issues with data uniqueness, accuracy, completeness and consistency. To accomplish this, MDM creates a centralized definition of an organization’s key business domains. Typically, customers, products, locations and suppliers are the primary domains the master data strategy will account for first.
For organizations that lack an overall data and data quality strategy, knowing where to begin with master data management can seem overwhelming. Often, MDM implementations are reactive; a response to a problem that has already caused damage or has become so prevalent that it can no longer be ignored.
Quick primer on data domains and master data
We touched on “domains” earlier. Defining some terminology helps to understand the what and how of MDM.
Think about the diverse systems used to run business in your organization. You may have a CRM, ERP, OMS, WMS, and a point of sale (POS) system. While different, those systems share commonalities when it comes to the data in them. And those commonalities appear differently in different systems. In the NASA example, feet in one system, meters in the other.
Data domains are basically logical groupings of data relevant to stakeholders within a business, for example customers, products, locations, vendors, assets.
Master data comprises the attributes that make up those domains. For example, for the data domain “product,” attributes could include product color, product size and product availability.
Master data management is the great unifier. It conforms, or unifies, master data across an organization’s shared data assets. And unity fosters business and organizational efficiency.
Fragmented data, fragmented customer experience
Managing fragmented data across divisions and data sources creates all manner of problems for companies. Take for example an online retailer. Data disparity often leads to inconsistent customer experiences, which in turn leads to increased churn rates.
Master data management can give the retailer a 360-degree view of each customer to improve personalization and support. Retailers undergoing aggressive M&A can greatly benefit from a flexible MDM infrastructure., as it enables timely integration of critical data from acquired entities, improving the customer experience and accelerating growth.
By providing consistent, accurate data, MDM removes the inefficiencies, delays and inevitable errors that come with managing disparate master data.
MDM is bigger than IT. It’s a cross-departmental activity.
Traditionally, organizations have considered MDM as a technical issue for IT — something that can be inherently managed through data engineering tools. In fact, a recent McKinsey study showed that 64% of MDM initiatives are driven by IT. The business tends to be oblivious to the need for MDM and the strategic value it offers.
Unfortunately, the IT-only approach typically fails to maximize the true value of MDM. As is true for most technology initiatives, the chances for success with master data management also improve significantly when cross-disciplinary stakeholders are engaged.
A master data management solution should ultimately be looked at as a competitive advantage for the organization. It can ultimately drive increased revenue, decreased costs, and risk mitigation.
How to develop the case for ROI with MDM
MDM project planning starts with identifying the business problem or use case that needs to be accomplished. Once the use case is defined, the next step is to rally all responsible parties.
The chance for success with master data management is significantly better when the business case has been built and agreed upon by all affected parties.
In order to build a business case, it’s important to take the following steps into consideration:
- Identify and engage business stakeholders from inception.
- Select the initial business domain and business sponsorship wisely.
- Calculate the cost of not taking action.
- Define the KPIs used to measure success up front.
- Clearly articulate the benefits of your MDM program.
- Demonstrate how the program will show value early and often (be agile!)
- Assess and present the value of the effort often.
When these steps are stringently followed, the organization as a whole benefits.
Master data management de-risks AI
No good strategy in the current environment of data and analytics ignores the implications of AI. Having sound master data is absolutely crucial to the success of an AI program.
Consumers already have a watchful eye on how their data is being captured, interpreted and utilized. Consider the example of a chat bot misidentifying a customer and making recommendations based on another customer’s data. At best, the customer’s confidence will be lost in the product or services being offered and you lose a customer. At worst, the organization can be subject to regulatory fines or lawsuits related to adherence to Personally Identifiable Information (PII).
MDM can mitigate this type of concern. It substantially increases confidence that organizations are capturing and identifying the right customers across all the disparate source systems that hold their data.
Additionally, there is a mutually beneficial relationship between AI and data quality overall. The more an AI model is trained to operate within a well-governed, high-quality data environment, the more likely AI will be able to identify when it comes across data points or situations that do not fit into its trained model. Creating a symbiotic relationship between AI and your MDM implementation allows AI to improve over time and avoid making costly mistakes.
MDM: Business necessity, competitive advantage
How your organization’s data is being handled and ensuring trust in that data is a business necessity and a competitive advantage. Lack of the data governance and quality that MDM enables leaves insights on the table. Worse, it can put your organization at risk.
A master data management strategy ensures the organization’s data is complete, accurate and consistent. The process requires a marriage of technology, process and people and a clear understanding and articulation of the project’s business value.
With a solid MDM program in place and trust in their data, organizations can respond with certainty and speed to market disruption. They can act with confidence to improve and solve problems.
Implementing MDM is a significant effort. Wherever you are with your data environment, Senturus can help, from strategy design to tactical implementation. Bridging IT with business units and executives, we bring stakeholders together to identify priorities and build the business case for funding. Matching the client to the appropriate technology for their business needs, we design and architect MDM solutions that maximize the value of data assets to increase revenue, decrease costs, and mitigate the risks of associated with poor data quality.
We offer a half-day MDM workshop to help jumpstart building your business case. Contact us to learn more.
Check out our other resources on MDM and data quality practices:
| Webinar | MDM: De-Risking Data Modernization, AI and Fabric Initiatives | |
| Blog | Will Your Data Governance Program Hold Up to AI? | |
| Webinar | Why Bother with Data Governance? |
