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Cluedin articles

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CluedIn introduces transformational new ways to clean, visualize and govern data


We are pleased to announce that CluedIn 2023.04 is now live and available to install from the Microsoft Azure Marketplace.

This new release also comes with a brand new CluedIn training course on Microsoft Learn - making CluedIn the first ever Master Data Management platform to offer Microsoft-accredited training (but more on that in a moment).

Our latest release is packed with features that will help businesses around the world to code less, and achieve more.

Let's jump straight in:

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Announcing the CluedIn & Microsoft Azure OpenAI service integration


Business users now able to complete 80% of data engineering tasks that would have required support from technology teams

Today, we announced that CluedIn has become the first Master Data Management (MDM) system to integrate with the Azure OpenAI service. As the most Azure-native MDM platform available, this development is the latest in a series of advancements that puts CluedIn at the forefront of helping organizations to realize their data-driven ambitions on Microsoft Azure. 

The integration of CluedIn with Azure OpenAI's advanced machine learning and natural language processing capabilities means that business and technical users alike can now clean, standardize and enrich their data in a matter of minutes, as opposed to days. During internal testing, data management tasks that would once have taken 25 – 30 hours to complete were performed in under 30 minutes, including verification of results.

From its inception, the CluedIn platform has been designed to give greater autonomy to business users by taking a low/no-code approach to helping them understand their data. With OpenAI, business users now have even greater power to do what would previously have only been possible with the support of IT and Engineering teams. 

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CluedIn enhances user experience and Golden Record transparency with new version launch


We are pleased to announce that CluedIn 2022.10 is now live and available to install from the Microsoft Azure Marketplace.

At CluedIn, our goal is to help customers realize their digital transformation goals by accelerating the process of preparing data to deliver insights. Rather than hold users back by forcing them to undertake time consuming and costly practices like upfront data modelling and manual rule creation, we eliminate or automate as many of these tasks as possible.

With this latest release, CluedIn has never been a more powerful tool for realising that vision.

Here are some of the changes we are most excited to share with you:

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CluedIn joins the Microsoft Intelligent Data Platform Partner Ecosystem to accelerate delivery of ready-for-insight data


When CluedIn was founded in 2015, the platform was built and designed on the premise that there are a number of core data management pillars that must be in place in order for businesses to realise the true value of their data. Without these pillars, there will always be an insurmountable void in the proliferation of data that is ready-for-insight.

That’s why, when Microsoft announced the Microsoft Intelligent Data Platform on 24 May 2022, it felt like another crucial piece of the puzzle fell into place. We see these pillars converging into a fully integrated platform of databases, engineering, analytics and governance. And we’re convinced that this helps to answer the urgent need to have a trusted source of high quality data.

The introduction of the Microsoft Intelligent Data Platform was a pivotal moment for us, as the CluedIn platform complements these pillars with a native Master Data Management (MDM) experience on the Microsoft Azure Marketplace.

It is therefore with great pleasure that on behalf of the whole CluedIn team, we can finally announce that CluedIn is now officially part of the Microsoft Intelligent Data Platform partner ecosystem.


What does this mean? We have always strived to be the most natural choice for Microsoft Azure data services customers who want to accelerate the process of turning raw data into meaningful insights. There is no shortage of powerful technologies and services in the Microsoft Azure ecosystem. Yet it remains the case that fueling data-driven initiatives is of low value and high risk of failure unless we can provide these teams with high quality, integrated, and governed data that is ready-for-insight. Many organizations we talk with, which are quite mature in their data journey, still talk of a common feeling that “something is missing".

CluedIn complements the Microsoft Intelligent Data Platform technology stack by providing business users, data analysts and data stewards with powerful tooling for the delivery and management of data. We like to say internally that Azure Synapse and Azure Databricks are Data Engineering tools for IT, whereas CluedIn is the "Data Engineering" tool for non-IT users. Companies at a certain point in their data-driven journey have realised that making data available is little more than a milestone, and just the start of more complexity in the journey. This is why Data Governance has become so in demand over the last few years. Enterprises have realised that without the proper services, trust, lineage and details around the data, it does not yield the required value and often gets rejected by the business. At CluedIn, our job is to make data ready-for-insight.

We complement the Microsoft Intelligent Data Platform technology stack with some of the missing pieces around Data Quality, Data Enrichment, Data Standardization and Data Preparation - all operated by non-IT users.


Most notably, with CluedIn added to the Microsoft Intelligent Data Platform, we can move the data closer to use by abstracting away systems, files, tables and assets - and move the discussion into "Domains" and "Data Products”. By the time that end consumers consume the data that has passed through CluedIn, a large number of the hurdles that often slow data projects down – like duplicate records and dirty data – have been removed.

The combination of CluedIn and the Microsoft Intelligent Data Platform has already delivered proven results to joint customers including Sunwater, an Australian water service provider supplying commercial customers in the agriculture, urban and industrial sectors. “As a water services provider we’re committed to responding to our customers’ needs and managing the changing water environment,” said Carolyn Gray, GM Information and Data Services at Sunwater. “We recognize that establishing and maintaining high quality, fully governed data is vital to fuelling initiatives that improve the level of service we offer and empowering our employees. The combination of Microsoft Purview and CluedIn has already delivered results by allowing us to use our data more effectively and efficiently.”

CluedIn is one of the solutions that is well integrated with Microsoft ecosystem in the area of Master Data Management .For example, CluedIn provides a simple way for customers of Microsoft Master Data Services (MDS) – which reached end of support in October 2021 - to migrate to CluedIn with as little disruption as possible.

For CluedIn customers, this commitment to the Azure ecosystem brings immense benefits, such as utilizing as many existing Microsoft investments as possible, while capitalizing on CluedIn for the parts of the data journey that it excels at.

It is no secret that the CluedIn team is doubling down on its strategy to provide an even more tightly integrated solution for Microsoft Intelligent Data Platform. Our firm belief is that this is the right path for organisations that need to invest more time in creating value and driving business outcomes, and less in laborious, tactical data management. Now, who wouldn’t want to achieve that!

Learn More:

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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.

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Master Data Management for Life Sciences and Pharmaceuticals Industries


Master Data Management (MDM) is the process of creating and maintaining a single, accurate, and consistent source of information for an organization's critical data entities such as customers, products, suppliers, and patients. In the life sciences and pharmaceutical industries, MDM is especially important due to the ever-increasing amount of data that needs to be stored, managed, and used to drive better commercial outcomes.

In this article, we will explore the benefits of master data management in the life sciences and pharmaceutical industries, including how MDM can improve data quality, enhance operational efficiency, and support regulatory compliance.

The Benefits of Modern Master Data Management 

Improving data quality

Data quality can be broken down and assessed by several different metrics – including timeliness, relevance, and consistency – all of which combine to give an organization an overall view of the quality of its data. With such large amounts of data to manage and so much potentially dependent on the accuracy of that data, data quality should be a primary concern for every company in the healthcare industry. Advanced Master Data Management platforms such as CluedIn can improve data quality by over 50% in a matter of weeks, helping organizations to make better-informed decisions, minimize the risk of errors and inconsistencies, and reduce the need for manual data reconciliation.

Enhancing operational efficiency

Improving data quality and ensuring that both technical and business users have access to the right data when they need it helps organizations to streamline business processes, improve operational efficiency, and reduce costs. By having a single, consistent view of data entities, organizations can avoid duplication and redundancy, reducing the need for manual data entry and minimizing the risk of errors. This helps to reduce the time and resources required for data management, freeing up staff to focus on higher-value tasks.

Supporting regulatory compliance

Regulatory compliance is a significant concern for life sciences and pharmaceutical companies. MDM can help organizations to meet regulatory requirements by ensuring data accuracy and consistency, enabling traceability, and providing a complete view of their data. By having a centralized data management system, organizations can quickly and easily access data required for regulatory reporting, audits, and inspections.

Enabling better decision-making

MDM provides a single, consistent source of data that can be used across different functions and departments. This enables organizations to make better-informed decisions, based on reliable, accurate, and up-to-date data. With MDM, organizations can improve their ability to identify trends and patterns in their data, enabling them to make more effective strategic decisions. In order for those decisions to have maximum impact, they also need to be made in a timely manner. Traditional MDM systems have struggled with this, as they often require months of upfront modeling before the system can even be deployed. Not to mention the delays caused by constantly having to go back to the IT department to ask for fixes. Modern MDM systems do away with all of this, using techniques like eventual connectivity and low code/no code to accelerate time to value and empower business users, both of which are important when decisions need to be made in a proactive and agile way.

Facilitating collaboration

Data silos often reflect the organizational structure of a business and build up over time, causing an increasing technical debt and significant financial harm. Not only does modern MDM facilitate collaboration and knowledge sharing across different departments and functions, but by having a centralized data management system that is accessible to every department and team, organizations can break down data silos and enable cross-functional collaboration without overburdening technical teams.

Master Data Management use cases for the Life Sciences and Pharmaceutical industries

There are many ways in which these industries can use MDM to maximize commercial success and drive operational efficiencies. Here is a selection of the most popular:

  • Product information management: MDM can be used to manage and maintain accurate and up-to-date product information, such as drug names, dosage forms, strengths, and indications. This can help ensure consistency across systems and channels, and facilitate compliance with regulatory requirements.
  • Patient data management: Establishing a single source of truth for information relating to patients and their care is vital for these industries. MDM can be used to manage and maintain accurate and comprehensive patient data, such as medical histories, diagnoses, treatments, and outcomes. This can enable better patient care and outcomes, as well as support research and development efforts.
  • Supply chain management: Critical supply chain data entities, such as suppliers, materials, and inventory levels can all be centrally managed and maintained by MDM systems. This can help ensure that products are manufactured and distributed efficiently and that quality standards are maintained throughout the supply chain.
  • Clinical trial data management: MDM can also be used to manage and maintain critical clinical trial data entities, such as study protocols, patient data, and adverse event reports. This facilitates data accuracy, completeness, and consistency, and supports regulatory compliance and reporting.
  • Regulatory reporting: As heavily regulated industries, life sciences and pharmaceuticals companies are required to uphold high standards of regulatory compliance and reporting. This kind of information includes adverse event reports, drug safety data, and clinical trial results. Failure to meet compliance requirements can not only result in financial penalties but may also inhibit future initiatives, which means that data lineage, audit trails, and accuracy are imperative in this sector.
  • Sales and marketing data management: A consistent, reliable, and accurate supply of customer, product, and sales data is vital to supporting commercial interests and go-to-market strategies. For example, CluedIn customer Springworks were preparing for the FDA approval and commercial launch of their innovative treatment for rare desmoid tumours and used MDM to create a targeted list of leads to focus on to generate more sales.

Data management challenges in the life sciences and pharmaceuticals industry

The life sciences and pharmaceutical industry faces a number of unique data management challenges due to the complexity and high volume of data involved. Some of the key challenges include:

  • Data silos: In many organizations, data is stored in separate silos, making it difficult to share and integrate data across departments and functions. This can result in inconsistencies, duplication, and errors, and can create an unnecessary burden on data stewards and domain experts, especially if a high degree of manual intervention is required to fix these issues.
  • Data quality: Ensuring data accuracy and completeness is essential in the life sciences and pharmaceutical industry, as mistakes can have serious consequences. However, managing data quality can be challenging, especially when dealing with data from multiple sources and formats. Tackling this problem requires an augmented approach to MDM which is capable of accepting data regardless of its origin or repository, and automating the process of dramatically improving quality over time.
  • Data integration: Integrating data from different sources and formats can be complex and time-consuming. This is especially true in the life sciences and pharmaceutical industry, where data may come from a variety of sources, including clinical trials, research studies, and real-world data. CluedIn uses a Graph database which means that data can be ingested as is, without the need for upfront modeling, and that the natural data model is allowed to emerge as new sources are added.
  • Compliance: Compliance with regulatory requirements is a critical concern for life sciences and pharmaceutical companies. Not only must reports be timely, accurate, and comprehensive, but they also need to have proven provenance and credibility. However, maintaining compliance can be challenging, especially when dealing with large volumes of data.
  • Data security: The life sciences and pharmaceutical industry handles sensitive and confidential data, such as patient records and clinical trial data. Ensuring data security and privacy is essential, but can be challenging in the face of evolving cybersecurity threats. MDM systems must be able to enforce data protection policies relating to retention, consent, sovereignty, and access without compromising an organization’s ability to achieve data that is ready for insight.
  • Data governance: Establishing clear data governance policies and procedures is important to make sure that data is managed effectively and responsibly. Many organizations struggle to establish and maintain effective data governance frameworks, especially when dealing with complex data ecosystems. While MDM is highly complementary to Data Governance, they are not the same thing. Find out more about the difference between these disciplines and why you need both here.

There are few – if any – industries with a higher reliance on data than life sciences and pharmaceutical companies. By establishing a single, accurate, and consistent source of information for critical data entities, organizations can improve data quality, enhance operational efficiency, support regulatory compliance, improve decision-making, and facilitate collaboration. This is what traditional MDM was designed to do, but in many cases, it took too long to achieve and created an unnecessary management burden on technical teams.

Modern, augmented MDM systems have eradicated these barriers by accelerating the process of ingesting and integrating data, making it possible to deliver a successful use case in as little as six weeks. They are also designed to allow business users and domain experts direct access to the data. A Cloud-native platform such as CluedIn will also allow you to take advantage of the economics and scalability offered by the Cloud, as well as integrate with other Cloud-based data services with ease. With the legacy obstacles now a thing of the past, there should be nothing to stop life sciences and pharmaceutical companies from getting the data they need to deliver the outcomes that only high-quality, trusted data can offer.

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