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White Paper |  10 min read

Justifying a Master Data Management project to the business in 2024


Diving into the realm of data quality from a unique perspective, we pose the question: "Can our business afford to operate without access to high-quality data?" This rhetorical query underscores the indispensable role of premium data in the seamless functioning of a business. Acknowledging the constraints of time, talent, and financial resources, it becomes evident that tackling monumental projects requires a strategic breakdown into manageable segments. Business leaders should embrace the urgency of addressing data quality issues, recognizing that the present moment is opportune for championing high-quality data within their organizations.

The burgeoning fields of data science, machine learning, and artificial intelligence are revolutionizing the way businesses derive insights from data. Yet, this technological advancement has cast a spotlight on a prevalent issue: many organizations lack a robust data foundation essential for leveraging these innovative tools effectively. At CluedIn, we are on a mission to demystify the complexities of data management, aiming to elevate the quality of your business data to a level where it becomes a reliable asset for insightful decision-making.

Our experience with a diverse clientele provides tangible proof of the transformative power of a solid data foundation. In a recent collaboration, we assisted a client who, prior to our intervention, reported business transactions across 4,632 global cities. By establishing a proper data foundation, we refined their data, resulting in a more accurate city count of 1,591. The dataset remained constant, but its narrative transformed, ensuring that the business stakeholders received a truthful and reliable account of the company’s global reach.

Now, let’s pivot our focus and delve into a critical query:

"What justifies the investment in Master Data Management (MDM), Data Quality (DQ), and Data Governance (DG)?"

In our extensive work with various clients, a recurring theme has emerged: the unanimous consensus that enhancing data quality is paramount in driving substantial business value back to the organization. These clients seek our expertise, not as novices, but as experienced entities grappling with the complexities and challenges of previous implementations in MDM, DQ, and DG.

When we turn to the industry’s leading analysts for insights, there is a clear consensus: establishing robust MDM, DQ, and DG frameworks is a more intricate endeavor than setting up a Data Lake or Data Warehouse. This complexity stems from the multifaceted nature of these systems, requiring a nuanced understanding and a strategic approach to data management.

But what exactly makes MDM, DQ, and DG so intricate?

Traditionally, deeming an MDM implementation successful is a milestone reached when a single domain is operational within a six-month timeframe. This prolonged process is largely attributed to the heavy reliance on manual, human-driven efforts. Dissecting these efforts further, it involves meticulous data modeling, crafting precise business rules, and constructing intricate ETL (Extract, Transform, and Load) pipelines to ensure seamless functionality. It’s akin to the arduous task of fitting a “square peg into a round hole.”


A traditional MDM simply doesn't work in today's modern environment and the expectations that come with it.


At CluedIn, we’ve reimagined this process, offering a streamlined path to justify the investment in a modern MDM solution. Our innovative “zero modeling” approach eliminates the need for upfront data modeling, allowing your data to seamlessly integrate into our system. This accelerates your data ingestion process, propelling you towards immediate data-driven insights.

Our methodology empowers businesses to integrate new data environments through a scalable pattern, accommodating even the most complex modeling scenarios where traditional MDM solutions falter. Whether you’re scaling from 5 to 25 or even 250 disparate data sources, CluedIn ensures a harmonious integration, automating a substantial portion of the process for easy maintenance and adaptability as new sources emerge.

Drawing wisdom from the project management realm, we’ve learned the efficacy of targeting incremental victories, steering clear of the “all-or-nothing” mindset.


This approach is crucial, especially considering Gartner’s observation that over 50% of MDM projects falter, primarily due to businesses banking on everything seamlessly falling into place.


Data is a living, breathing, and forever morphing business asset.

Data within a business is not static; it’s a dynamic asset, constantly evolving and adapting to the ever-changing business landscape. Recognizing this, CluedIn emphasizes the importance of targeting quick, impactful wins within Master Data Management (MDM), addressing specific business challenges head-on and ensuring a tangible Return on Investment (ROI).

Breaking down monumental MDM projects into manageable business use cases, CluedIn accelerates both the implementation process and the realization of business ROI. This granular approach ensures that each facet of the MDM initiative is meticulously addressed, fostering a more robust and reliable data foundation.

A core tenet of CluedIn’s philosophy is the acknowledgment that it’s acceptable for data to have imperfections as it enters the system. The integration, cleansing, training, enrichment, and de-duplication of data is a journey, not a destination. In instances where businesses require immediate access to raw data, such as Twitter and Facebook feeds, CluedIn provides the flexibility and tools necessary to accommodate these needs. Our system is designed to proactively identify and rectify common data issues, such as spelling errors, ensuring a seamless and efficient data management process.

By focusing on operationalizing business use cases swiftly and effectively, adding value and functionality to the business becomes a more streamlined process. This approach not only accelerates the time to value but also enhances our ability to identify and address common data quality issues across various business scenarios. It’s important to note that this methodology is applied judiciously, with careful consideration given to sensitive data types, such as Personal Identifiable Information (PII). CluedIn ensures that the appropriate business rules are applied at the right time for each data point, maintaining data integrity and compliance.

Contrast this with the more traditional MDM approach, which necessitates extensive consultations with each business unit to define their specific data quality criteria, resulting in the creation of complex and numerous business rules. This not only prolongs the MDM project but also increases the risk of failure due to the heavy reliance on manual interventions and the potential for human error. CluedIn’s experience has shown that while there are commonalities in data quality issues across different companies, there are also numerous unique challenges that do not conform to predefined business rules. By adopting a more agile and responsive approach, CluedIn mitigates these risks, ensuring a smoother and more successful MDM implementation.


The time taken to generate all possible data quality issues upfront is nearly impossible to achieve, and often will delay a project by several months.


We take at different approach

At CluedIn, our approach to Master Data Management (MDM) is fundamentally different, embracing a smarter, more scalable methodology. As data flows into CluedIn, our advanced technology takes the helm, automating the tedious, repetitive tasks that traditionally bog down data management processes. This includes deploying sophisticated tools for anomaly detection and intelligent text processing, ensuring that data inconsistencies and errors are promptly identified and addressed.

In this automated environment, the role of the data steward is transformed. Rather than being mired in manual data cleansing and validation tasks, they are empowered to oversee and guide the process, simply approving or rejecting the suggestions made by our intelligent systems. We estimate that this approach enables the technology to autonomously resolve approximately 80% of data challenges, while also providing valuable insights and guidance on addressing the remaining 20%.


Our guiding principle at CluedIn is clear: "It is better to make a process obsolete than to make a process simpler."


This philosophy drives us to innovate continuously, seeking ways to eliminate data problems at their source rather than merely mitigating their symptoms. By doing so, we ensure that our clients’ data is not just managed, but truly mastered.

Our pricing model reflects this commitment to efficiency and scalability. Rather than basing costs on the volume of data points, attributes, users, or sources (a common practice in traditional MDM solutions) CluedIn’s pricing is determined by the speed at which you wish to clean, train, enrich, and de-duplicate your data. Specifically, it revolves around the number of computer processing cores required to accomplish these tasks. This approach ensures that CluedIn can seamlessly scale alongside your business, providing a cost-effective solution that grows with your data needs.

Advice for and key takeaways from this white paper


By following these guidelines, you can ensure that you are making informed decisions about your MDM strategy, leading to improved data quality, operational efficiency, and business insights in the not too distant future.

  1. Reevaluate Your Data Strategy:
    If you’re stuck in the traditional MDM rut, it’s time to reconsider your approach. Look for solutions that offer quick wins and can address specific business challenges efficiently.
  2. Embrace Modern MDM Solutions:
    Consider adopting modern MDM solutions like CluedIn that offer a zero modeling approach, ensuring faster data ingestion and quicker access to insights.
  3. Focus on Scalability:
    Ensure that your chosen MDM solution can scale with your business. Look for pricing models that are based on processing speed rather than data volume to ensure cost-effectiveness.
  4. Leverage Automation:
    Utilize technology to automate manual, repetitive tasks. This not only speeds up the data management process but also frees up valuable human resources for more strategic tasks.
  5. Don’t Wait for Perfect Data:
    Understand that data is a living asset and it’s okay to start deriving insights from it, even if it’s not fully cleansed or integrated. Focus on improving data quality over time.
  6. Prioritize Quick Wins:
    Identify and prioritize business use cases that can be quickly addressed through MDM, ensuring a faster return on investment.
  7. Seek Cost-Effective Solutions:
    Look for MDM solutions that offer a cost-effective pricing model, ensuring that you can scale your data management efforts without breaking the bank.
  8. Understand the Importance of High-Quality Data:
    Recognize that high-quality data is a critical business asset and make the necessary investments to ensure its accuracy, reliability, and timeliness.

Gartner Market Guide: Master Data Management in 2023  

Guide: Best practices for implementing an MDM strategy