April 20th, 2023 | 10 min read
The past, present and future state of Master Data Management
Master Data Management (MDM) is a discipline that involves managing and governing master data in order to support decision-making, analytics, compliance, and customer engagement. MDM aims to ensure that master data is accurate, consistent, and complete across various systems and applications within an organization. In this article, we will explore the history of MDM, where we are today, and what the future holds.
Origins of MDM
The concept of MDM can be traced back to the 1990s when organizations started to realize the importance of having a single source of truth for critical business data such as customer, product, and financial data. During this period, many organizations were struggling with data silos, where data was scattered across various applications and systems. This led to data inconsistencies and duplication, which in turn resulted in poor decision-making and lost business opportunities.
In response to these challenges, a new discipline emerged, called MDM. The first generation of MDM solutions were focused on creating a single, centralized repository for master data, which could be used by various applications and systems. These solutions were typically built on top of relational databases and used ETL (Extract, Transform, and Load) tools to populate and update the master data repository.
Evolution of MDM
As MDM gained traction, its scope expanded beyond just creating a centralized repository for master data. Organizations realized that MDM was not just a technology issue, but also a business problem. They needed to establish governance policies and processes to ensure that master data was accurate, consistent, and complete. This led to the second generation of MDM solutions, which focused on governance and stewardship of master data.
In the mid-2000s, the MDM market experienced significant growth and consolidation, with many large software vendors acquiring or partnering with MDM vendors to expand their capabilities in this area. During this period, many MDM solutions evolved to become more flexible and scalable, enabling organizations to manage a broader range of master data domains, such as location data, supplier data, and employee data.
Current State of MDM
Today, MDM has become a critical component of many organizations' digital transformation initiatives. With the rise of big data, cloud computing, and Artificial Intelligence (AI), organizations need a trusted source of master data to fuel their analytics, Machine Learning (ML), and other advanced technologies.
In recent years, MDM solutions have evolved to meet the demands of the modern enterprise, with many solutions offering cloud-based deployment options, APIs for integration with other systems, and advanced analytics, AI, and ML capabilities. Some MDM solutions have also incorporated blockchain technology to provide a more secure and transparent way of managing master data.
In addition, the role of MDM has expanded beyond just managing master data within an organization. Many organizations are now looking to collaborate with their partners and suppliers to manage master data across their extended enterprise. This has led to the emergence of new solutions and standards, such as Product Information Management (PIM) and Global Data Synchronization (GDS), which aim to enable organizations to share and synchronize product data across their supply chains.
Despite all of these advancements, however, MDM is still dogged by many of the problems it suffered 30 years ago. Some MDM systems still need data engineers and IT specialists to model and map data upfront, before the MDM platform can even be deployed. Not only is this a drain on time and valuable resources, but it also keeps MDM firmly in the realm of technology teams and away from business users. Many MDM systems also place a limit on the type of data they can ingest, and the number of sources they can integrate. All of these factors can thwart MDM projects, and thwart the digital transformation projects that data should be feeding.
The future of MDM
MDM has come a long way since its inception in the 1990s and is now an essential discipline for managing and governing critical business data elements. As we look towards the future, MDM is poised to evolve and adapt to the changing needs of the digital enterprise.
Cloud computing has transformed the way organizations deploy and manage software applications. In the future, we can expect to see more organizations adopt cloud-based MDM solutions to manage their master data. Cloud-based MDM solutions offer several benefits, such as scalability, agility, and cost-effectiveness. They also enable organizations to leverage advanced analytics and machine learning capabilities to extract insights from their master data.
As the volume and complexity of data continue to grow, data governance will become more critical than ever. Organizations will need to establish governance policies and processes to ensure that their master data is accurate, consistent, and complete. In the future, we can expect to see more organizations invest in data governance tools and solutions to manage their master data, and we should also expect the convergence between the two disciplines to accelerate.
Artificial Intelligence and Machine Learning
AI and machine learning have the potential to transform the way organizations manage and govern their master data. In the future, we can expect to see more MDM solutions incorporate AI and machine learning capabilities to automate data quality tasks, identify data anomalies, and suggest corrective actions. CluedIn, for example, is the first MDM platform to integrate with the Microsoft Azure OpenAI service. The introduction of advanced machine learning and natural language processing capabilities to CluedIn means that business and technical users alike can now clean, standardize and enrich their data in a matter of minutes, as opposed to days, with a reduction in manual work of around 50 to 1.
DataOps is a relatively new term that has emerged in the last few years to describe the practice of integrating data engineering, data integration, and data management processes with DevOps methodologies. Essentially, it is a set of best practices for managing the entire data lifecycle in a more agile and efficient way. DataOps provides several benefits, such as automating the processes of data integration, data profiling, and data validation. It also encourages cross-functional collaboration between data teams and business users which can help to break down silos and improve communication. DataOps also provides a more agile approach to MDM, allowing organizations to respond to changing business needs and data requirements quickly. One of the other benefits of DataOps is that it emphasizes continuous improvement and feedback loops, which can help organizations to identify and address data quality issues more quickly.
Businesses today operate with diverse and complex data from various sources, including structured, semi-structured, and unstructured data. Data can come in different formats, such as text, images, videos, and audio, and it can be stored in different systems and databases. As we look ahead, MDM systems will have to handle any type of data in order to ensure that organizations have a complete and accurate view of their data assets, and can use them to support business decisions, drive innovation, and enhance customer satisfaction.
The future of MDM is bright and full of opportunities. As organizations continue to digitize their operations, MDM will play an increasingly critical role in managing and governing master data. We can expect to see more cloud-based MDM solutions, data governance tools, AI and machine learning capabilities, data collaboration solutions, blockchain technology, and IoT capabilities in the future. The race will be on to produce MDM solutions that are scalable, secure, and agile enough to keep up with the changing nature of data and digitalization projects. All of which is good news for those organizations that recognize the increasing criticality of data as an asset and are seeking tools to support it.
By Natasha Scott
Head of Demand Generation at CluedIn
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