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There are always hurdles (many are necessary) to starting any data initiative. Do you have the right team, the right technology, the right budget, and more. In fact, we are really underplaying it here, there are literally 100s of decisions that need to be made before kicking off an internal data project. One of our big focusses at CluedIn is to help you limit the number of hurdles in a positive and constructive manner.

Here’s a couple of good examples. Imagine trying to buy technology and with a budget of $10,000, and the technology you want is $9,000. This is a good example of a hurdle that doesn't exist. If it was $11,000, then suddenly, many months of effort has potentially been added while you figure out how to secure a bigger budget or how to negotiate with the vendor to discount the price. This isn't always a quick process.

At CluedIn, we considered the entire sales process from the point of view of the customer, and put in a strategy to make sure that WE, as the vendor, are removing as many hurdles as possible. Let's dive in and look at some of the options we’ve put in place to make this possible.

Self-install, start when you’re comfortable.

I am an engineer at heart. I have been a software engineer for the last 15 years and I can speak only for myself when I say that I need to use software before deciding whether it’s a good fit. I also realise that when I do this, I really only get a 25% view of what that software is actually capable of. At CluedIn, we have recently added support to deploy CluedIn directly through the Microsoft Azure Marketplace in a Managed Application Offering. This makes CluedIn dead easy to get started with by offering the data sovereignty of PAAS, with the ease and scalability of SAAS. I can get a rough idea of the platform, but am quite happy to accept that some things might not work as I expect or it may not reflect the final nature of what I will be getting.

Start sending data to CluedIn straightaway - no need for upfront modelling.

Let's be clear, having a plan makes so much sense - you should plan. But, you do not need your data model to be perfect and future-proofed before you implement MDM. If you wait until you do, you will literally never start because the perfect data model is a flawed and impossible concept. CluedIn's data model was designed around the idea of source control. This might get a bit technical, but it is literally the best analogy to equate to the way CluedIn stores, changes and processes data. You don't build code with perfection in mind. You evolve it and sometimes you might even fundamentally rewrite something. Without a doubt, source control systems like GIT have proven that they can manage huge repositories in any type of change that you could expect now and not even expect to happen in the future. We provide the same with data. At CluedIn, you do not need to perfect your data upfront, you can delay it until a point when it makes sense. This cannot be said about the majority of MDM systems. At CluedIn, although you will benefit from mapping the data on entry, and mapping the primary and foreign keys, it is not enforced. What is the value of this? Should this not be the time to actually map this data? The answer is, categorically, no. There are so many benefits one can get from simply placing data into CluedIn such as Data Quality Metrics, Data Lineage, Sensitive Data scanning and Data Sharing. Once you are ready, going back to the mapping of data and updating it will simply require the data to be reprocessed and CluedIn will handle the change on your behalf.

No need to complete your Data Governance program before you start.

CluedIn is different – think of it like the Agile alternative to Project Management. You can build and discover and modify and adapt along the way.

Let me reiterate, a plan is a good thing. But at some point, overplanning leads to a lack of agility to change in the face of necessary change. CluedIn is an Agile MDM platform, in that it expects change, it expects things to go wrong and is prepared for that. Which means that bumps in the road will not fundamentally kill a project with CluedIn.

Let business rules evolve over time.

We have mentioned this in previous posts, but CluedIn removes a huge hurdle from common MDM initiatives, which is to develop business rules to either detect and identify possible data quality issues or to setup rules to invoke an action once a certain condition is met. Instead of asking you upfront to manually develop these rules, it turns out that most of the rules that you actually want to build will come from working with the data, allowing the issues to surface and then putting the proper fixes in place. In the majority of cases, you won't be able to develop these proactively, but reactively. CluedIn embraces this idea, by onboarding data into the platform, and then using surfacing tools to help detect and automatically place business rules in place to fix the existing issues and prevent that problem from making its way through the system ever again.

Zero downtime upgrades.

Let’s face it, upgrading software is a massive ****ache in an enterprise environment. That complexity is escalated when you have software that is more of a "platform" that allows you to extend. Now that CluedIn offers generic, REST-based extension points in the latest version, it makes the process of upgrading painless. CluedIn can be setup to auto-update or you can opt-in and manage it yourself, giving you the choice. Considering the core of CluedIn is based off a schemaless data model, with support for reprocessing, then any actions that need to be triggered on new updates can be automated as well.

Auto-scaling across the entire cluster i.e. scales disks, CPU, RAM, network.

In the true spirit of CluedIn, we are not interested in providing a solution that is faster and better. We attempt to remove the need to actually do something in the first place. CluedIn is designed and setup to auto-scale according to your business drivers. Typically these factors will either be working towards a particular time and date, or a particular budget, or even "spend as much money as possible to get the job done as fast as possible." In saying that, although all of the above is possible, it still needs to make economic sense in most cases.

Native integration to 27 Azure services in just a few clicks.

Even with a multi-cloud strategy, native integration to the cloud provider you are hosting your platform in, is without a doubt, hugely valuable. CluedIn is focused on being the most native MDM solution on Microsoft Azure. Sure, we work and have many customers on the other cloud platforms, but on Azure we are easily the most native and obvious choice due to the number of native integrations we support. Want to use Azure Active Directory for authorization, SSO and authentication? One click away. Want to enable Azure Defender, Azure Sentinel? One click away. Want to share mastered and cleaned data from CluedIn to Azure Synapse, Azure DataBricks, Azure Machine Learning Studio? One click away. Want Azure Purview to register and govern all the data movement in CluedIn? One click away.

We want to provide a “think it, done!” type of experience for Microsoft Azure customers. If you have an idea, you should be able to make it a reality within moments, not weeks.

Kubernetes backbone means support for all environments.

It is well known that in the MDM space, many leading vendors take months to just install and setup. Without a doubt, the future of infrastructure is containers and Kubernetes. Kubernetes brings an abstraction that isolates environments, operating systems, and more. This essentially lowers the entry barrier, due to the abstraction, but also due to the native support for all cloud providers. In addition to this, Kubernetes brings some of the pieces expected for modern, enterprise applications such as auto-scaling, zero-downtime upgrades, and more.

Endorsed by Microsoft – already!

Just as we are investing in our Microsoft relationship, Microsoft is also heavily investing in CluedIn. CluedIn was one of the first applications to provide the Managed Application Offering for MDM on the Azure Marketplace. This provides a great combination of security and data sovereignty, combined with the beauty of a managed service.

Built for the enterprise - i.e. Logging, SSO, Telemtry, SSL, DNS, Inbuilt backups, Budget Allocation (scale to budget), Azure Defender, Azure Sentinel.

Just like knowledge of a particular industry will accelerate implementation, knowing what is expected by enterprise customers is also crucial to generating and sustaining momentum. At CluedIn, we know our customers intimately and have our finger on the pulse of what they will expect in the future from enterprise applications. CluedIn has native support to provide logging, SSO, Telemtry, SSL, DNS, Backup/Restore, and more. We have developed this not only from the teams experience, but also by monitoring what the cloud providers are enabling, as well as what customers are asking for during the purchase cycle - e.g. "What does CluedIn provide in terms of threat-detection?".

Accelerators for all industries, and partners that know your space.

Different sectors, industries and verticals require specific domain knowledge. After implementing and being part of over 40+ MDM implementations myself, I can say that each industry has specific identifiers that are known only to its industry e.g. NPI in Health. At CluedIn, we have vertically aligned partners that specialise in implementing MDM for particular sectors. These partners come with their own additions and pre-built packages for CluedIn in the shape of existing Domains, Vocabularies, Connectors to Systems, Enrichers (public datasets) - and that is just from the technology side.

A clear comparison between other MDM vendors and CluedIn.

All MDM vendors are different. Considering that MDM is a well-established industry, it is important to help our customers understand the revolution that Modern MDM has brought. For this, CluedIn provides many layers of research into what the main differences are between Modern MDM providers like CluedIn and traditional MDM providers. I would like to use another example of a shift in technology that has also drawn a clear line in the sand around modern and traditional approaches, and that is the Data Warehouse space.

The modern Data Warehouse makes a fundamental shift at a very low level that essentially optimises files for read access and then distributes jobs across multiple machines to answer a question. In addition to this, it has taken full economic advantage of the scalability of the cloud in that you can spin up a huge number of machines to run distributed computing at relatively low cost.

The same revolution has happened in the MDM space, driven predominantly by the shift to the cloud. I know this sounds like a cliche, but building for the cloud is a fundamental difference. The other big revolutions in MDM have been through either the automation or augmentation or processes that were complex with traditional MDM software.

Migrate from other MDM systems with ease.

CluedIn offers specific services for customers wanting to easily migrate from a traditional MDM solution to CluedIn. We can provide this through our partner network, in conjunction with our own team that has a plethora of experience and expertise in many MDM solutions. CluedIn has a generic framework for translating data, models, business rules, workflows, hierarchies and more.

Get started with a free trial.

Although not unique in the majority of categories, in the MDM space, free trials are really a rarity. Why is this? Well, I can only speculate, but our opinion is that MDM is hard to implement, and although I would like to stand here and say that has been solved, I don't think it has, it is still hard to implement ANY MDM system. It’s just not quite as hard with CluedIn!

Pricing that works for you.

Essentially there are two ways of purchasing software. It is either a Capital Expense (cap-ex) or an Operational Expense (op-ex). Each of these has their own advantages and disadvantages. This is why CluedIn offers both cap-ex pricing (upfront payment, yearly recurring.) and now per hour pricing (consumption based).

Hourly pricing means that you only pay when you are using the platform. This increasingly suits companies that want to avoid hefty upfront investments. On the flipside, with a capital footprint, you can often get quite heavy discounts. This is simply because vendors like CluedIn need some level of predictability. The operational footprint is fantastic for removing hurdles to getting started, but the caveat to this is that customers won’t be offered the same discounts. Why? Because there are literally 100 reasons why a project might not start, could or could be delayed. And the technology might only be one of them. In saying that, a combination of both the cap-ex and op-ex model can be very powerful, particularly when you start with a consumption and move to a commitment once trust has been established.

Buy CluedIn under your MACC agreement with Microsoft.

Finding budget is hard! It may be that your organization already has an agreement with Microsoft in regards to Azure spend. This commitment means that you have been given a discount on Azure services in return for a commitment on many years of agreed revenue. You can use this to purchase CluedIn.

Buy CluedIn under the standard Microsoft Ts&Cs.

When buying enterprise software, you can't just choose any software, it needs to meet the legal requirements of the business. Chances are you have already signed the Microsoft Standard Ts&Cs if you have bought off the Azure platform before. Hence, CluedIn can be bought under the same Ts&Cs.

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

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.