AI in Master Data Management (MDM)
solulab.com/ai-in-master-data-management
February 28, 2024
Enterprises face the difficulty of handling enormous amounts of data in today’s digitally
driven world, as well as navigating the complexity of many data kinds, especially from
emerging sources like Internet of Things (IoT) devices and linked technologies. The situation
is made more complex by the notable trend towards cloud computing, which is pushing
companies to use technology and services more strategically in order to maximize the value
of their data assets rather than just buying equipment. In light of this, the idea of “data agility”
becomes imperative. It represents an organization’s capacity to adjust and react effectively to
the changing requirements of global data management. Given the significant effect that
erroneous master data may have on an organization’s income, this agility is crucial. Adopting
cutting-edge data management solutions becomes essential in a market where AI (Artificial
Intelligence) and ML (Machine Learning) are having an increasingly significant impact. In the
current competitive world, an effective Master Data Management (MDM) strategy is essential
for organizational success. MDM solutions are essential for future-proofing data repositories
and Big Data analysis, serving as the fundamental source of truth in the corporate world.
Through the exploration of new data categories and the extraction of deeper insights from a
variety of data kinds, they enable companies to improve their capacity for making decisions.
What is Master data?
1/14
The term “master data” describes the vital, core information that an organization needs to run
its operations and make wise decisions. This data, which often changes infrequently,
includes essential details on the main entities that are the subject of commercial
transactions. Although master data is not transactional, it is essential to the definition and
direction of transactions. Typically, customers, goods, workers, suppliers, and locations are
the key domains of master data. These domains may all be further broken down into subdomains, offering thorough segmentation and categorization according to different
characteristics and circumstances. Complete segmentation and categorization improve data
use and manageability, supporting strategic data use across a range of corporate processes
and decision-making contexts. Master data management calls for an all-encompassing
strategy that goes beyond basic lists and explores a more complex, organized, and
integrated handling of these many kinds of data.
It’s critical to distinguish master data from other forms of data that are frequently seen in
enterprises.
Unstructured Data: This category comprises generic data types including white
papers, emails, and promotional materials. Unstructured data is not considered master
data, despite its importance.
Transactional Data: Consists of thorough logs of commercial dealings. Unlike master
data, which is often more stable, it is characterized by its temporality and uniqueness to
certain events or actions.
Metadata: Data that describes other data and provides further context and insight is
known as metadata. Although it is not thought of as master data per se, metadata
clarifies and complements both master and transactional data.
Hierarchical Data: Data that is organized hierarchically shows the connections and
interdependence among various data pieces. Although it might be closely connected to
master data, its main purpose is to show the relationships and hierarchies that exist
within the data.
Reference Data: Reference data is a specific type of data that is used to relate or
categorize other data pieces, usually to external categories or standards. Although it is
related to master data, it has a different function in that it provides uniformity and
context.
Any company that wants to manage data effectively must be aware of these differences in
order to properly classify and handle various types of data for maximum business efficiency
and insight.
What is Master Data Management (MDM)?
2/14
MDM is more than just the combination of tools, procedures, and technology for organizing,
managing, and protecting master data in a business. Ensuring that this essential data is
precise, standardized, and widely accessible throughout an enterprise and its subsidiaries,
MDM goes beyond a purely technological fix to include critical business procedures and
policy modifications that are frequently required to maintain the integrity of master data.
An MDM strategy must be organized around six core disciplines in order to be as effective as
possible. These disciplines are all essential to putting up a strong MDM program.
Governance: Putting in place a strategy framework to oversee and control
organizational structures, rules, guidelines, and standards in order to make verified and
certified master data easier to access. It entails assembling a cross-functional group to
clarify and describe the MDM program’s numerous aspects.
Measurement: Monitoring the MDM program’s progress toward its goals while keeping
an eye on data quality and continuous improvement.
Organization: Making sure that master data owners, data stewards, and governance
participants are all positioned correctly throughout the MDM initiative.
Policy: Defining and upholding a set of guidelines, directives, and specifications that
the MDM program must follow.
Process: Implementing well-defined procedures used to maintain master data
throughout the data lifecycle.
Technology: Putting in place a master data hub and any other technology that will help
the MDM program as a whole.
To summarize, master data management (MDM) extends beyond the conventional confines
of a technology solution. It adeptly navigates the complex paths of organizational politics and
technical obstacles, guaranteeing that master data persists as an unblemished, dependable,
and uniform asset throughout the firm. In order to ensure that master data not only fulfills its
immediate functional purpose but also creates a long-lasting foundation for managing data in
a constantly changing business landscape, a strong MDM strategy should incorporate these
six disciplines holistically.
Use Cases of AI in Master Data Management
3/14
The introduction of AI into master data management (MDM) signals the beginning of a new
phase in data efficiency and optimization. Businesses across a range of sectors are using AIpowered solutions to expedite MDM procedures and extract useful insights from their data.
Artificial Intelligence is transforming the way businesses handle their master data, from
strengthening data governance to boosting data quality. This section delves into particular
applications of AI in MDM, examining actual scenarios where AI-powered solutions are
revolutionizing data management procedures.
1. Data Extraction
It has been acknowledged that navigating the complex and large-scale master data
landscape is a complex problem, especially in light of the excessive amount of continually
created data. IDC estimates that 64.2 Zettabytes (ZB) of data were created or replicated
globally in 2020, and that number is expected to expand at a compound annual growth rate
(CAGR) of 23% from 2020 to 2025. According to the Businesses at Work Study by Okta,
enterprises—especially bigger ones—use an average of 175 apps, while smaller ones use
an average of 73. This underscores the necessity for effective data management. When one
considers that data lakes are predicted to increase at a 30% compound annual growth rate,
the need for effective data management techniques is evident.
Under such circumstances, it is not practical nor sustainable to use manual methods to
examine data, particularly when it is spread across millions of columns from several sources.
The use of clustering, data similarity evaluation, and semantic tagging procedures in master
data management machine learning approaches has emerged as a critical tool. These
4/14
machine-learning techniques may automate the complex tasks of domain identification and
master data discovery, which will streamline the discovery process, improve scalability, and
increase overall productivity.
2. Lineage of Data
Understanding and visualizing the data’s origin, transportation, and transformations is crucial
in the intricate ecosystem of master data management. This is especially true when it comes
to complying with regulations, preserving data quality, and carrying out well-informed
business choices. This path, which is commonly referred to as data lineage, may be carefully
charted and tracked with the use of artificial intelligence (AI) technologies, which are
increasingly playing a key role in streamlining and improving this essential component of
MDM. Data lineage mapping may be efficiently automated master data management by AI
technologies because of their capacity to analyze technical metadata and identify
relationships using machine learning-based relationship discovery. This enables companies
to catalog the sources and kinds of master data as well as the complex routes that this data
takes as it moves between different sources and applications across the whole organization.
A general AI engine in the master data management domain might function as a useful
illustration of the features included in contemporary data management systems. An engine
like this does more than just list master data sources and the domain types that they belong
to. Additionally, it creates a comprehensive map that shows how master data moves between
different applications and sources throughout the whole business environment.
3. Information Modeling
Digital commerce, cloud data warehousing, and data lakes, application modernization—
particularly in master data management—and other digital transformation initiatives all
depend heavily on data modeling. MDM is made easier and more scalable for operational
and analytical use by creating a centralized MDM hub that is used as a single source of truth
by applications and analytical data storage. As a result, the hub must effectively maintain
master data models to guarantee that the fundamental characteristics and hierarchies are
the same in all sources.
Artificial intelligence plays a crucial role in this intricate situation by offering sophisticated and
automated solutions to problems related to data modeling in MDM. Schema matching is one
of the primary jobs where AI shines; this procedure is essential to obtaining coherence and
alignment in data models across various data sources. Schema matching is the process of
finding and connecting characteristics, or groups of attributes, across semantically related
master data models. This may be challenging since data varies and changes over time
across many organizational platforms.
4. Obtaining and Classifying
5/14
The maintenance of master data is a difficult process that gets much more so when a large
amount of data from many sources is added to it. The two essential elements of the MDM
domain are the “Acquisition” and “Categorization” of master data, which include integrating
the data and appropriately classifying it within the overall data model. These components
experience increased scalability, decreased error margins, and increased efficiency when
used in the context of artificial intelligence in MDM.
AI for automated master data ingestion and onboarding can significantly improve the MDM
master data management process during the “Acquisition” phase. The process of locating
and classifying fields in data sources and then matching them to master data models may be
greatly streamlined and automated with the use of AI technologies such as genetic
algorithms, named entity recognition (NER), and natural language understanding (NLU). This
data integration and structuring procedure is not just for file-based data; it can also be
applied to data from API endpoints and integrated into application operations. The
productivity of business operations that exchange master data with partner and customer
apps is improved by this wide applicability. This capacity is best demonstrated by AI-driven
solutions, which streamline and expedite the acquisition process by automating data
integration and ingestion. simplifying and accelerating the acquisition stage in MDM.
5. Data Integrity
A fundamental need of master data management is ensuring its flawless quality, which has a
direct impact on the reliability, precision, and usefulness of the operational features and
insights that are obtained from it. Within this framework, artificial intelligence is particularly
noteworthy as a revolutionary enabler, cleverly integrating into different aspects of master
data quality assurance to improve correctness and dependability while also introducing a
great deal of automation into related procedures.
Ensuring the correctness, completeness, and consistency of master data across all domains
is a critical task related to its quality. Artificial Intelligence (AI) makes sense of this complexity
by using a combination of machine learning methods, including probabilistic, heuristic, and
deterministic approaches, together with Natural Language Processing (NLP). These
6/14
solutions enable businesses to streamline the data quality assurance process and improve
its scalability and efficiency by automating the challenging tasks of master data profiling,
cleaning, and standardization.
6. Combine and Align
Because organizational data is so complex and has so many facets, the match and merge
activity in MDM master data management is essential to improving data quality and integrity.
This activity involves carefully identifying and merging duplicate records, which is an
extremely difficult task. Artificial intelligence plays a crucial role in this situation by providing
accuracy, scalability, and a certain level of automation to the matching and merging
processes while taking into account a changing data environment.
Deduplication is closely related to the match and merge activity and is worth considering
carefully because it entails searching through large amounts of data from various
applications for duplicate master data records and then wisely combining them into a single,
authoritative version that is known as the “golden record.” A golden record is the
embodiment of one complete picture, containing precise information that has been gathered
and organized from several sources.
7. Data Connection Identification
In the current context of digital transformation, businesses are paying close attention to
expanding their comprehension of company procedures and client interactions. They use a
variety of methods, including value stream mapping, business ecosystem modeling, and
consumer experiences and journeys. These approaches seek to guarantee that optimization
efforts are in line with overall company results rather than favoring particular functional areas
and to reveal insights that may be concealed inside departmental silos.
Master Data Relationship Discovery is a crucial component of this strategy, particularly when
it comes to the use of AI in master data management. This procedure entails a thorough
investigation of the relationships between several master data domains, including supplier,
product, and customer data. This makes it possible to comprehend and control end-to-end
business processes holistically.
8. Data Management
Master Data Governance (MDG) describes how to precisely control and guarantee data
security, quality, and accessibility within an organization. It entails painstakingly coordinating
the standards, procedures, and rules that specify how data is used, managed, and
distributed within an organization. By combining data quality, management, and policy
enforcement in a seamless manner, artificial intelligence (AI) in master data management
plays a critical role in optimizing and automating many aspects of the Millennium
Development Goal (MDG).
7/14
An AI engine for master data management, for example, can show how AI can efficiently
expedite the process of connecting business glossary definitions, rules, and data owners to
master data. By combining domain discovery, data similarity analysis, and Natural Language
Processing (NLP) applications, this kind of AI engine may automatically increase these
associations’ productivity and accuracy. This improvement has a major positive impact on
cross-functional cooperation in master data governance by guaranteeing that different
business facets are precisely and efficiently managed and integrated in a seamless manner.
9. Protection and Privacy
Safeguarding the confidentiality and integrity of master data is critical in the broad field of
master data management, particularly in the context of a data-driven operational and
technical landscape. Artificial intelligence greatly improves the capacity to protect, handle,
and maximize private and sensitive information using accurate, automated, and flexible
methods.
When it comes to master data privacy and security, artificial intelligence functions as a
resolute sentinel, always keeping an eye out for, recognizing, and categorizing sensitive data
as well as implementing proactive safeguards in real time to preserve its confidentiality and
integrity. It makes its way across the complex and varied terrain of data, identifying private
and sensitive information, linking it to the appropriate privacy policies, and dynamically
applying pertinent security regulations to protect the data from misuse and illegal access.
10. Data Exchange and Use
As we get closer to a time where data is the foundation of strategic initiatives and decisionmaking, the exchange and use of master data inside an organization’s boundaries are
essential to coordinating dependable and insight-driven operations. Intelligent automation,
predictive analytics, and dynamic data management are integrated across the data lifecycle
by using AI to enhance the effectiveness and strategic value of master data exchange and
utilization.
By adding intelligence and flexibility to the processes of producing and using data for
analytical endeavors, artificial intelligence (AI) enhances the talents and productivity of
scientists, data curators, and business analysts. It balances the diversity and complexity of
data, suggests relevant master data ahead of time, and guarantees that data consumption
and sharing take place in an efficient, safe environment that complies with data governance
guidelines.
Benefits of AI Application for Master Data Management From an
Analytical Stance
8/14
Artificial Intelligence is a powerful tool for improving master data management because of its
ability to mimic cognitive functions like learning and problem-solving. Combining AI and MDM
creates a mutually beneficial partnership that improves the effectiveness, precision, and
intelligence of an organization’s data management procedures.
A. Enhanced precision and expedited data processing
Increased processing speed: AI systems are skilled at quickly sorting through large
amounts of data, making sure that conclusions drawn from it are timely and useful.
Reduced human error: AI’s automation eliminates the possibility of human mistakes
altogether, guaranteeing consistency and accuracy of data across big datasets. Finding
trends and intelligent data analytics.
Exposing latent trends: Artificial Intelligence assists in identifying patterns and trends
in data that human analysts could miss, resulting in a piece of more comprehensive
knowledge and, eventually, well-informed decision-making.
Business strategy optimization: The insights derived from AI’s analytics can show
how to improve product designs, marketing plans, and other business processes so
that they are more in line with current customer trends and habits.
B. Improved security and governance of data
Automated data quality assurance: By using AI to do jobs such as data quality
checks, organizations can make sure that data complies with guidelines and is
accurate and consistent throughout the whole organization.
Strong data security: AI algorithms may also support data security protocols,
protecting against breaches and guaranteeing that data management complies with
relevant regulatory requirements.
C. Using AI to revitalize data validation and purification
Automated error correction: By methodically locating and correcting mistakes or
discrepancies in the data, master data management machine learning algorithms may
automate the process of data cleansing.
Data that has been categorized and structured: AI’s capacity to classify and
categorize data guarantees a methodical and cohesive arrangement, which simplifies
further analysis.
Support for data stewardship: AI may help data stewards proactively by suggesting
classifications or adjustments, which the stewards can then assess and put into
practice. This improves the accuracy and efficiency of the data cleaning process.
9/14
D. Intelligent and automated data enrichment
Completing attributes: By finding patterns and connections in already-existing
datasets, AI’s predictive powers may be used to detect and fill in missing qualities in
data.
Wide-ranging data use: Artificial intelligence algorithms have the ability to infer
pertinent information from a variety of sources, including textual documents and social
media, which enhances the data that is accessible for study and guarantees a more
comprehensive perspective.
Symbiotic relationship with data: Data and AI systems have a symbiotic connection
whereby the former improves the latter’s comprehension of underlying patterns and
correlations and builds upon its predictive and analytical powers.
Organizations may become more insightful, accurate, and efficient in navigating the intricate
web of their data ecosystems by combining AI’s analytical and predictive capabilities with
MDM. AI’s automated master data management skills not only reduce mistakes and speed
up processing, but they also extract richer, more detailed insights from data, giving
organizations the knowledge they need to make better, more strategic decisions. As such, AI
in master data management is more than just a technology improvement; it’s a strategic
advancement in the handling, interpretation, and value extraction of corporate data.
What Applications of AI are There for Master Data Management at
Different Stages?
10/14
The use of AI in master data management highlights how incorporating cutting-edge
technology into conventional data management procedures may have a revolutionary
impact. With automated data collection, cleansing, predictive analytics, and data quality
enhancement, artificial intelligence (AI) improves master data management (MDM) and
increases accuracy, foresight, and alignment with corporate objectives. Businesses can now
traverse complicated data environments with more intelligence, agility, and accuracy thanks
to these developments.
AI-powered data gathering, cleansing, and processing
1. Automated data collection
Web scraping: AI systems automatically collect data from a variety of online sources
by using web scraping techniques, which guarantees a rich and varied data set.
IoT data collection: Data extraction and processing from Internet of Things (IoT)
devices may be done using AI algorithms, which improves the usefulness of real-time
data.
2. AI-powered data cleansing
Error detection: AI systems regularly find inconsistencies or duplications in the data,
among other problems.
Auto-correction: By utilizing correlational and historical data, AI is able to anticipate
and carry out adjustments automatically.
3. Data preparation and processing:
Normalization: By helping to standardize data, AI algorithms make sure that different
datasets have a consistent format.
Transformation: AI is capable of automating the translation of data into forms that are
suitable for use with analytical models.
Analyzing data quality using machine learning methods
1. Checks for consistency:
Cross-validation: To guarantee consistency and dependability, machine learning
models cross-validate data inputs using pre-established rules or previous data.
Pattern recognition: Algorithms are used to find patterns in data, spotting and fixing
aberrations that might point to problems with the quality of the data.
2. Profiling of data:
11/14
Analytical statistics: To guarantee data quality, AI uses statistical models to analyze
data for distribution trends, mean, mode, and other metrics.
Anomaly detection: Machine learning algorithms find patterns in data and highlight
possible problems that need more investigation.
3. Validation of data:
Constraint checking: In order to preserve the authenticity and integrity of data, AI
systems impose certain limitations.
Semantic validation: AI uses natural language processing (NLP) to make sure that
data follows semantic norms, which assures logical consistency.
AI-powered predictive analytics and data forecasting
1. Modeling that predicts:
Regression analysis: AI makes predictions about future data points by using
regression models, which are based on past patterns and data correlations.
Classification models: To enable predictive analytics, artificial intelligence classifies
data into predetermined groups.
2. Data projection:
Time series analysis: AI algorithms use time-sequenced data to estimate future
patterns, allowing companies to plan ahead and adjust their strategies in response to
changes.
Demand forecasting: In order to project future demand, AI examines historical use
patterns, customer behavior, and market conditions.
3. Analytics that prescribe:
Decision trees: AI uses decision tree models to investigate possible results of various
strategic options.
Algorithms for optimization: AI analyzes several situations and results to suggest the
best plans of action for achieving organizational goals.
12/14
Conclusion
A Guide to AI in Master Data Management concludes by highlighting the revolutionary
potential of AI in streamlining master data management procedures. SoluLab, a top AI
development company, provides specific AI development services designed to increase the
effectiveness of master data management. Businesses may simplify data governance,
enhance data quality, and obtain actionable insights for well-informed decision-making by
utilizing AI in MDM. Discover how AI can improve master data management with SoluLab,
and use AI-driven solutions to propel success and creativity in data-driven settings.
FAQs
1. What is Master Data Management (MDM), and how does it relate to AI?
Master Data Management (MDM) is a process that involves managing the organization’s
critical data to provide a single point of reference. AI enhances MDM by automating data
cleansing, matching, and enrichment processes, thereby improving data quality and
consistency.
2. What are the key use cases of AI in Master Data Management?
AI is utilized in various aspects of MDM, such as entity resolution, data deduplication, data
classification, data standardization, and data enrichment, to ensure data accuracy,
completeness, and consistency.
3. How can businesses leverage AI in Master Data Management effectively?
Businesses can effectively leverage AI in MDM by investing in AI-powered MDM platforms or
integrating AI algorithms into existing MDM systems. Additionally, hiring AI developers or
partnering with AI development companies like SoluLab can help customize AI solutions to
meet specific MDM needs.
4. What are the benefits of using AI development services for Master Data
Management?
13/14
AI development services enable businesses to create custom AI solutions tailored to their
MDM requirements. By hiring AI developers, organizations can access expertise in AI
technologies and algorithms to build advanced MDM solutions that drive efficiency and
innovation.
5. What challenges should businesses consider when implementing AI in Master Data
Management?
Challenges in implementing AI in MDM include data privacy concerns, integration with legacy
systems, ensuring algorithm transparency and fairness, and the need for skilled AI talent.
Addressing these challenges requires careful planning, collaboration, and adherence to best
practices in AI development.
6. How does SoluLab support businesses in AI development for Master Data
Management?
SoluLab, as an AI development company, offers specialized AI development services to help
businesses leverage AI in Master Data Management effectively. Our team of expert AI
developers collaborates with clients to design and implement AI-powered MDM solutions that
optimize data management processes and drive business value.
14/14