I am currently working as co-founder and CTO of Copenhagen based startup CluedIn .

CluedIn is a master data management platform giving companies the data foundation they need to fulfil their data-driven initiatives and deliver more value than ever before.
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• Tim Ward 
• Pierre Derval 
• Stephen Pope 
• Alex Shyba 
• Kieran Marron 
• Anders Kalhauge
• Brian Kenneth Nielsen
• Esben Sundgaard
• Henrik Fangel
• Jacob Nielsen
• Jacob Winther
• Jais Holstein
• Jesper Lykkeby
• Jon Friis
• Mads Laumann
• Martin Morpain
• Mogens Jacobsen
• Søren Andersen
• Thomas Kingo
• Allan Hansen 

Other Websites:

• HyldahlNet.dk
• GameLauncher.dk 
• UmbracoDev.org 
• LinkFellaz.dk 
• CluedIn.com

Online Profiles:

AI Readiness Checklist for Data Leaders and Practitioners

Microsoft Fabric and Purview MDM Integration

Microsoft Fabric and Purview Integration announced at Microsoft Ignite

MDM in the IT Domain

Why keeping Master Data Management (MDM) in the IT Domain is bad for business

Most Common MDM Myths

The Most Common MDM Myths. Debunked.

The Definitive Master Data Management Toolkit

Unlock the secrets of Master Data Management (MDM) and learn how it revolutionizes data management. Our comprehensive toolkit equips you with the resources, tools, and knowledge needed to navigate data challenges and build a robust business case for MDM implementation. From understanding MDM's impact on streamlining operations to becoming a certified expert, embark on a journey to data excellence with our guided learning paths and lifetime access to valuable content.

CluedIn AI Assistant - the future of master data management

Meet the CluedIn AI Assistant - the future of master data management. Powered by Azure OpenAI, it revolutionizes data management with natural language capabilities. Enhance decision-making, automate data governance, and streamline collaboration effortlessly. CluedIn accelerates innovation, ensures compliance, and democratizes data for organizations worldwide. Join the journey to intelligent master data management with CluedIn as your trusted partner.

Solving complex data challenges with CluedIn and Azure OpenAI

Unlock the potential of CluedIn and Azure OpenAI to tackle intricate data dilemmas effortlessly. Tim Ward, CluedIn's CEO, explores the synergy between graph databases and Azure OpenAI, offering insights into how CluedIn amplifies Azure OpenAI workflows. Seamlessly bridging human-AI interactions, CluedIn empowers non-technical users to harness the power of natural language to navigate complex data landscapes. By enriching ChatGPT prompts with context, CluedIn closes the human-AI gap, ensuring precise and reliable data outcomes. Discover how CluedIn's integration with Azure OpenAI revolutionizes data management and simplifies data-driven decision-making.

Master Data Management and Data Governance

Data Governance and Master Data Management. What is the difference and why do I need both?

Cost of poor data quality

The cost of poor quality data and how to fix it.

CluedIn third-party Purview experience

CluedIn announces integration with Microsoft Purview, enabling seamless data transfer and unified data management. Leveraging AI-powered assistance and federated governance, Purview streamlines operations across multi-cloud data estates. The collaboration extends CluedIn's master data capabilities, enhancing data quality, lineage, and accessibility within the Purview interface. With advanced analytics and AI-driven insights, organizations can harness data for informed decision-making and regulatory compliance. Additionally, CluedIn's integration with Azure services and forthcoming SaaS offering further strengthens data governance and accessibility for businesses of all sizes.

What is Master Data Management?

Master Data Management (MDM) is explored as a strategic approach to governing core business data, ensuring accuracy, consistency, and accessibility across organizations. The article delves into its components, benefits, challenges, and best practices, highlighting its pivotal role in driving informed decision-making and operational efficiency in today's data-driven landscape.

Master data management: The key to getting more from your data

Master data management can give organizations a leg up by allowing them to easily organize and access data about customers, suppliers, products, and employees.

Data Lineage: Building Trust from the Ground Up

Trust is the cornerstone of any successful data initiative. With CluedIn, the journey towards trust begins with a Detailed and Automated Data Lineage from Source to Target. This granular visibility allows for a transparent and explainable audit trail, paving the way for trust paths to be established.

In every data project, adherence to a Business Ready Glossary is non-negotiable. CluedIn ensures that your business communicates with its entire data landscape in a common language. No longer is there a need to navigate the complexities of source systems or engage with domain experts. This collaborative Data Glossary facilitates endorsement, rating, and sharing with ease, working seamlessly across languages, departments, and various industry lingo.

Proactive Data Policies: Ensuring Compliance and Security

CluedIn takes data governance to the next level with proactive business rules. These rules monitor and alert on sensitive data or data requiring action, guaranteeing a free-flowing, compliant, and secure data environment. The platform incorporates sophisticated rules, providing detailed logs of processing to enable a comprehensive view of data actions over time.

Security is paramount in the realm of data governance, and CluedIn's Granular Access Control offers a solution. With Active Directory integration, the ability to build custom roles, and the flexibility to set different permissions at various processing stages, the platform ensures a secure, compliant, and auditable environment.

Transparency Through Audits: Never Second Guess

CluedIn thrives on transparency, storing an audit trail of every action within the platform. This detailed history, coupled with the ability to explain every decision, is a testament to the platform's commitment to clarity. Whether it's SEC Rule 613 compliance or other audit trail regulations, CluedIn eliminates the need for second-guessing, empowering users to confidently navigate their data platform.

In essence, CluedIn's comprehensive approach to data governance encapsulates the core elements necessary for success - from detailed lineage and business-ready glossaries to proactive policies, granular access control, and transparent audits. It provides the framework for organizations to not only manage their data effectively but also to do so with trust, confidence, and compliance.

Read more about data governance

The ETL Conundrum

For decades, ETL has been the stalwart for importing data, ensuring its cleanliness, and planning meticulous architectures for system connections. However, as data ecosystems burgeon and diversify, ETL hits scalability roadblocks. Its assumptions about pristine data, simple system connections, and straightforward identification of joins become untenable, especially when dealing with a multitude of sources.

Enter Eventual Connectivity

The Eventual Connectivity pattern challenges the status quo, offering a dynamic and scalable alternative. Unlike ETL, it doesn't demand upfront planning of system connections or intricate modeling. Instead, it adopts a "load first, connect later" philosophy. This approach involves flagging records with metadata and relationships as data is loaded, allowing relationships to emerge organically.

Why Eventual Connectivity Shines

1. Scalability Unleashed: With ETL, connecting 500 systems prompts concern about months of upfront modeling. Eventual Connectivity, however, allows a systematic, one-system-at-a-time approach, easing the integration process.

2. Denormalized Discoveries: The pattern aids in discovering denormalized references and relationships between data, removing the need to revisit the drawing board when new systems are introduced.

3. Graph Databases: Leveraging graph databases, Eventual Connectivity ensures a more flexible and less modeling-dependent approach, catering to the needs of modern data consumers, such as Data Warehouses and Machine Learning applications.

4. Transparency in Non-Blending: Identifying data that doesn't blend well becomes straightforward, thanks to floating "edges," enabling a clearer understanding of potential issues.

    

Acknowledging the Challenges

While Eventual Connectivity introduces a breath of fresh air, acknowledging its challenges is crucial for a balanced perspective:

1. Post-Ingestion Duplicates: The process might lead to the appearance of duplicates during data ingestion, posing a challenge that needs to be addressed after the fact.

2. Perceived Lack of Control: Some may feel a diminished sense of control over data connections. However, this is often a result of oversight in architecture planning sessions.

    

A Reading Analogy

To understand the distinction, consider reading a book.
ETL would ask you to explain character relationships on page 14, akin to predicting the data connections upfront. In contrast, Eventual Connectivity suggests finishing the book, marking relationships along the way, and creating a comprehensive map-a more intuitive and flexible approach.

Embracing the Data Revolution

As businesses grapple with data from myriad sources, embracing the data revolution becomes paramount. Eventual Connectivity emerges not as a silver bullet but as a potent tool for dynamic, scalable, and less modeling-restrictive data integration. It's time to reevaluate our strategies, ushering in a new era of connectivity that aligns with the evolving demands of the digital landscape.

Read more:
CluedIn Eventual Connectivity Whitepaper

Documentation

Podcast

CluedIn and Microsoft Purview MDM offer a unified, scalable, and
transparent means to manage, govern and derive insights from data.

CluedIn ensures true end-to-end data lineage by automatically tracking data in Microsoft Purview. Users can unify business glossaries for Golden Records, collaborate on terms, and build data products with Microsoft Purview assets. Detailed processing logs enable tracking of data changes, and CluedIn provides data quality scores for accuracy and completeness within Microsoft Purview. The system is highly scalable, offering a seamless data pool by connecting with all enterprise data through CluedIn and Azure applications, with automatic Azure Data Factory pipeline construction.

Read more: Data governance with CluedIn and Microsoft Purview 

CluedIn is a native solution for Master Data Management on Microsoft Azure.

As part of the Microsoft Intelligent Data Platform partner ecosystem, CluedIn helps companies to accelerate the process of delivering data that is ready for insight. By eliminating the hardest parts of the master data management journey, CluedIn delivers high quality, integrated data that can be used by business and technical teams to fuel data-driven projects. CluedIn offers native integration to 27 Microsoft Azure services including Microsoft Purview, Azure Data Factory, Azure Synapse and more. The highlights of the solution include:

See the solution here: CluedIn Microsoft Azure Master Data Management

Learn how to build an end to end data governance and master data management stack with Microsoft Purview and CluedIn 

  - Software developer/architect with 25 years experience

Address:	**************** ****************
Mobile:	********
E-mail:	martin  hyldahlnet . dk
Date of birth:	June 26, 1980
Marital status	Cohabitation

Work experience / Employments

25+ years of experience from full-time jobs.

Certifications / Courses

Education

Personal characteristics

• Sense for quality, sense of detail
• Systematic
• Curious and easy learner
• Team oriented

Linguistic skills

• Fluent in writing, speaking and understanding technical English and Danish.

Competencies

1 = Lowest level, 5 = Highest level

Personal Interests

• Software development
• Computer games
• Photography
• Music
• Movies
• Books

Early version of Mondo's website ( captured by archive.org on 11 Nov 1996):

Klik på billederne for at besøge vore kunder

Early years of Mondo

Scope.dk

Since internet access was not widespread in every household in Denmark, a compelling showcase was needed to demonstrate Mondo's capabilities and the types of websites it could deliver to customers. Consequently, the Danish movie review site scope.dk was created very early on.

For its time, scope.dk was quite advanced. During this period, most websites were primarily static, comprising of hand-written HTML pages and images. Scope.dk, however, was built using a CGI program written in C and a custom database to serve dynamic web pages.

Read more

$-HTML Programming Language

The ability to create dynamic websites quickly became a necessity, and in those days, there were no very easy, widely available solutions targeting web development other than CGI/C programs and Perl. Consequently, Mondo developed its own programming language.

$-HTML name originated from the use of the dollar sign character to indicate script blocks. Like other web programming languages of this era, $-HTML script blocks could be embedded in the same files as HTML markup, controlling the output of rendered HTML to the browser. These source files were called mask files and used the file extension .msk.

Microsoft was, of course, also building something similar to $-HTML. Since it was known that percent (%) characters were to be used to indicate script blocks, this was internally referred to at Mondo as %-HTML until it was released as Active Server Pages (ASP).

Code example:

<html>
<head>
<title>$$-HTML examp101.msk</title>
</head>
<body>
$SET(Message, "World")$
Hello $Message$!
</body>
</html>

Read more

MondoSearch

In the days before Google, finding information on the web was not as easy as it is today. In Denmark, the largest web portal was the Danish equivalent of Yahoo's link index, Jubii.dk. As websites grew in size, the need for search functionality to help users easily find information became apparent.

To address this need, Mondo built a general-purpose full-text search engine that could be easily integrated into any website, intranet, extranets, etc.

MondoSearch was developed using a combination of C and $-HTML and featured numerous advanced capabilities:

• Automatic deduplication of crawled content
• Language detection
• Document classification (link list, empty page, article)
• Automatic detection of site navigation elements 
• Robot.txt and custom crawling rules
• Indexing of sites using HTML frames
• Customized ranking
• Search page custom branding and styling
• ... and a lot more

Eventually, MondoSearch was spun off as a separate company, and MondoSoft was established, while Mondo continued as Mondo Solutions, delivering web solutions to customers.
 

Later years

Mondo Branded Merchandise

Mondo Offices Lyngbyvej

Mondo Offices Bådehavnsgade

Data Integration. Reinvented.

Automate Data Integration.
We provide an integration platform that does all the joining of records for you. Forget complex designers and ETL pipelines.... you don't need them.

A pattern that supports endless data sources.

You will never stop growing. Your company will bring on more systems, generate more data and need to move faster. CluedIn's integration pattern paves the way for flexible and scalable data integration.

• You can take one tool at a time, integrating tool 1 is as easy as tool 301.
• No need for domain experts to sit together and map systems.
• No need for expensive data engineers to build complex pipelines.
• The pattern will find more connections than you can with other tools.
• The pattern is simpler, better, easier to understand and you will never go back to your old ways.
• Dynamic schema recognition and correction of schema drifts

Ready to go.

Time to value has never been this quick.
CluedIn is designed to take data at its face value. We don't need or want you to clean or prepare data for CluedIn. CluedIn utilises an ELT approach to simply load data from systems as is. We take care of the rest.

• 220+ Connectors.
• Support for Cloud or On-Premise or a hybrid.
• Support for CDC, Batch, Streaming, Push.
• Active Metadata Driven Integration.
• Can integrate into existing ETL Pipelines like Informatica, SSIS, Data Factory, SAP, Nifi and more.

Data Integration solved for good.

There are so many ways to integrate data and CluedIn makes sure that this is all abstracted away from you. What matters for you is that you want data ready-to-use, don't get lost in all the ways to deliver that - that's CluedIn's job.

• Access to Multiple Data Sources
• Structured, Semi-Structure and Unstructured Data Support
• EDI, Swift and HL7 Support
• Active Metadata Support
• Bulk/Batch Data Movement
• Data Preparation and Usability
• Data Replication and Synchronization
• Data Virtualization
• Message-Oriented Data Movement
• Passive Metadata Support
• Scalability and Performance
• Stream Data Integration

Built for scale.

Throw anything at CluedIn.
CluedIn was designed to support structured, semi-structures and unstructured data and lots of it. It is not unfamiliar for CluedIn to process 100's of TB's of data.

• Unlimited Data Connectors and amount of data, no cost per MB or consumption cost.
• Fast.....really fast.
• TB and PB scale.

Resilient and Robust

Guaranteed Delivery.
Data needs to get from A to B. Many things can go wrong in that process, but that shouldn't be your burden. CluedIn is backed by robust and reliable infrastructure so data will find its way in the world.

• Schema-Drift-Proof
• Robust Data Delivery Guarantee
• Buffering, Back-Pressure
• Verbose Monitoring, Metrics and Telemetry

CluedIn's mission is to give companies the data foundation they need to fulfil their data-driven intiatives.

Motivation

CluedIn's motivation is grounded in a strong belief that data will drive significant innovations in the future. The team is driven by the goal of providing companies with a solid technological foundation to harness the power of data effectively. Their underlying motivation is to contribute to solving global challenges using data in a scalable, robust, and ethical manner.

Philosophy and ideology

CluedIn's philosophy adopts a contrarian approach, challenging conventional perspectives to drive innovation. CluedIn critically examines existing problem-solving methods, questioning them comprehensively from various angles, believing that this scrutiny is the catalyst for true innovation.

At its core, CluedIn's ideology focuses on identifying fundamental pillars of data essential to any undertaking. CluedIn is committed to delivering a master data management platform and ecosystem that support these foundational aspects, emphasizing a robust and fundamental approach to data in all its endeavors.

CluedIn.com

Charity

As 'Be Good People' is one of CluedIn's core values, the company has established a charity to help others in need.

Read about Den socialøkonomiske virksomhed Lend a Hand ApS.

Learn More about CluedIn:

• Master data management with Azure and CluedIn
• CluedIn Master Data Management Azure Marketplace
• CluedIn Microsoft Intelligent Data Platform
• CluedIn Master Data Management and Azure OpenAI
• CluedIn Master Data Management with Power Apps
• CluedIn Master Data Management

CluedIn in the News

• Yahoo Finance:
  CluedIn raises $15m Series A to take revolutionary Master Data Management platform to more enterprise customers globally
  
  
• Globe News Wire:
  CluedIn raises $15m Series A to take revolutionary Master Data Management platform to more enterprise customers globally
  
  
• Venturebeat:
  CluedIn raises $15M to grow its data prep and analytics platform
  
  
• Dawn Capital: Why We Invested in CluedIn
  
  
• Nordic9:
  Cluedin raised a $15 million Series A funding round led by Dawn Capital.
  
  
• Sales Techstar:
  CluedIn Raises $15m Series A To Take Revolutionary Master Data Management Platform To More Enterprise Customers Globally
  
  
• Tech.eu:
  Copenhagen's CluedIn raises $15 million in Series A round led by Dawn Capital
  
  
• Watch Medier:
  Softwareselskab henter tæt på 100 mio. kr. fra internationale investorer
  
  
• Watch Medier:
  Dansk softwareselskab lander stor amerikansk kunde
  
  
• Børsen:
  IT-hjerner bag milliards­ucces vil gøre det igen: Har landet kæmpe amerikansk kunde
  
  
• Børsen:
  To Sitecore-stiftere skyder millioner i deres tidligere medarbej­deres startup
  
  

In the intricate landscape of Master Data Management (MDM), reference data emerges as a pivotal component, offering organizations a compass for navigating the vast sea of information. As a subset of master data, reference data provides context and categorization, acting as a fixed point of reference for the comparison, validation, and enrichment of other data elements within a system or organization.

The Crucial Role of Reference Data in MDM

Without effective reference data management, MDM is akin to steering a ship without a compass. Reference data establishes common standards that master data must adhere to, bringing consistency and standardization to the MDM process. This, in turn, leads to enhanced data quality, integrity, and accuracy.

Benefits of Reference Data in Master Data Management

Data Integrity: Reference data provides standard values and codes for describing and categorizing master data. By managing reference data alongside master data, organizations ensure the accuracy and consistency of values, preventing data quality issues and maintaining overall data integrity.

Data Governance: Reference data serves as the foundation for defining data standards, policies, and rules. Coordinated management of reference data and master data enables the establishment and enforcement of data governance practices, including quality controls, security measures, and usage guidelines.

Reducing Complexity: Integration of systems and data sharing across different applications or departments becomes more efficient when reference data and master data are managed together. Consistent reference data facilitates the mapping and transformation of data between systems, ensuring compatibility and interoperability.

Taking Reference Data Management to the Next Level with CluedIn

Allowing Non-Standardized Data In: CluedIn, built on a Graph database, treats both reference and master data uniformly. This innovative approach addresses a common challenge by allowing non-standardized data into the system, automatically fixing discrepancies within the platform.

Real-Time Data Streaming: CluedIn supports streaming data, allowing for real-time updates. Leveraging this capability ensures efficient and prompt propagation of data changes, enabling a more reactive and real-time ecosystem. This facilitates data-driven processes, event-driven architectures, and near-instantaneous updates across connected systems.

Flexible Cross-Service Access: Utilizing both REST and GraphQL endpoints, CluedIn provides flexible access to reference data. REST endpoints offer predefined resources and operations for common queries, while GraphQL endpoints allow clients to request specific fields and related data tailored to their individual needs.

Automatic Write-Back: Through the mesh API, CluedIn automates the write-back of normalized data into operational systems. This automated normalization mechanism simplifies integration, reducing the effort required to map and transform data from different sources.

In conclusion, effective reference data management is indispensable for organizations seeking to harness the full potential of Master Data Management. With CluedIn's innovative approach, organizations can seamlessly integrate reference and master data, ensuring data consistency, quality, and governance while embracing flexibility and real-time capabilities.

Read the whitepaper here

How Graph-based Master Data Management Accelerates Data-powered Projects

Big Data LDN 2022, 21 Sept, 4:20pm, DataOps Theatre
Speaker: Tim Ward, Cluedin

Over three quarters of Master Data Management (MDM) projects fail, which means that how we currently approach MDM is fundamentally flawed. The time has come to completely reinvent how we prepare data for use in business-critical projects like creating a single customer view and identifying cost efficiencies. Graph-based MDM platforms allow you to eradicate upfront modelling, integrate thousands of data sources and process millions of records in minutes. Join this session to learn how you can use your use your data to drive process efficiencies, accelerate innovation and reduce time to market.

Links:

Forbes: How Graph Databases Supercharge Master Data Management

LinkedIn Post: Why is Graph "key"​ to the modern MDM movement? - Christian Heckenberg

Flexible Modelling

Graph based modelling for ultimate flexibility

Zero upfront modeling in Master Data Management (MDM) is an approach that defers the creation of a detailed data model until a later stage. It emphasizes agility, flexibility, and faster time-to-value by allowing organizations to quickly implement MDM solutions without extensive upfront design. This method utilizes a flexible schema, such as Graph databases or schema-less approaches, enabling iterative development cycles and accommodating evolving data requirements. Zero upfront modeling encourages continuous iteration and improvement based on user feedback, data analysis, and changing business needs. It excels in dynamic environments, making it suitable for areas like customer 360, mergers and acquisitions, public health, and identifying investment opportunities.

You will never build a model that will cater for everything single use case, so why are we all doing that in our Master Data Management systems then?

• Data Modeling 

• Data Stewardship 

• Hierarchy Management 

• Semantics Layers

Read more about CluedIn's flexible data modelling

Multi domain entity modeling

Don't model your data for what your world looks like today, do it for what it will be at any point in time.

MDM has always been about focussing on models such as Customers, Products and Transactions. Why limit yourself when you don't need to anymore?

• Loading/Sync/Business Services. 

• Multiple Domain and Multidomain 

• Multiple Implementation Style 

• Multiple Usage Scenarios 

• Perform/Scale/Availability

• Workflow/BPM 

Read more about mastering data with CluedIn

1. Upfront Data Modeling is a Must

Traditional MDM systems required defining the data model upfront, suitable for stable datasets. However, modern organizations often operate in dynamic environments. Graph-based MDM systems address this by allowing the data model to evolve naturally as data is ingested, eliminating the need to predefine the model while retaining control over the modeling process.

2. Data Governance must be centralized to be effective

Centralizing data governance is not the sole or most effective approach. A balanced strategy involves a unified governance framework with departmental autonomy within defined parameters. In the past, coding skills were essential for direct data management, but modern MDM systems, incorporating Natural Language Processing (NLP) like Azure OpenAI, enable data stewards and business users to create and enforce governance policies without coding expertise.

3. MDM Systems are Solely for IT Professionals

Initially, MDM systems aimed to provide businesses with reliable data but failed due to reliance on technical specialists. The evolution of MDM, like in CluedIn, now integrates Generative AI. This ensures direct involvement of domain experts in data management. With Generative AI and AI Assistants, even non-IT professionals can navigate the platform effortlessly, making data governance and mastery accessible to everyone.

4. MDM is Just About Managing Data

MDM goes beyond managing clean datasets; it's about leveraging data to enhance decision-making, drive efficiencies, and support AI and ML initiatives. Business involvement in the data supply chain is crucial, serving as a launchpad for various commercial initiatives and operational improvements, ultimately contributing to profitability and growth.

5. MDM Implementation Equals Disruption

MDM implementations are often associated with disruption, but with careful planning and strategic steps, they can seamlessly integrate into existing systems. Involving all stakeholders, from IT specialists to department heads, ensures the MDM system respects each department's needs. Strategic planning, executed in phases, makes the rollout manageable, minimizing disruptions and enhancing proficiency in the process.

6. It takes months to deliver your first MDM Use Case

The misconception that the initial MDM use case requires months of preparation is dispelled by adopting an agile and focused approach. Concentrating on specific data domains or business areas and employing iterative, agile methodologies enables rapid delivery of MDM use cases. By identifying and prioritizing a high-impact and feasible use case, businesses can achieve quick wins and incrementally build upon them.

7. MDM Has to be Expensive

Contrary to the myth that MDM is financially burdensome, MDM solutions, such as CluedIn, offer diverse pricing models to suit various budgets. Options include cap-ex pricing (upfront payment, yearly recurring) and per-hour pricing (consumption-based), allowing flexibility for different business needs. Hourly pricing caters to companies seeking to avoid significant upfront investments, and a combination of cap-ex and op-ex models proves powerful, especially when transitioning from consumption to commitment.

8. You must have a fully baked Data Governance Strategy Before Embarking on MDM

The misconception that a comprehensive data governance strategy must precede MDM implementation is debunked. Both can evolve concurrently, with insights from MDM shaping a refined data governance strategy and preliminary governance guidelines ensuring alignment with overarching objectives. This symbiotic development enhances the effectiveness of both MDM and data governance, creating a more tuned and applicable framework.

Read more about CluedIn's modern MDM solution