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Offensive vs Defensive Data Governance Strategies

CluedIn

Businesses are swimming in an ocean of information. This abundance of data is a double-edged sword – it can either drive innovation and growth or lead to chaos and inefficiency. To navigate this sea of data effectively, organizations need a comprehensive data governance strategy. Two prominent approaches in this regard are defensive data governance and offensive data governance.

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Key takeaways: The 2023 Gartner® Market Guide for Master Data Management Solutions

CluedIn

Recently, renowned analyst firm released its first event Market Guide for Master Data Management Solutions. It features solution overviews (like ours), must-have features (guess who has them all), rankings (spoiler alert: we're a pack leader), and Gartner's recommendations for data leaders considering a purchase.

Here’s our take on the key takeaways from this inaugural guide.

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Microsoft makes data governance available to the business with free version of Microsoft Purview

CluedIn

Microsoft has taken a significant step in the world of data governance by launching a free version of Microsoft Purview (currently in preview). This new offering is designed to make data governance and management more accessible to organizations of all sizes. Let’s take a look at what the free version offers, and explore the implications for master data management.

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Upfront versus Zero Upfront Data Modelling

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Traditional Upfront Data Modeling

Traditional upfront data modeling in MDM involves the creation of a comprehensive data model that defines the structure, relationships, and constraints of master data entities. This approach requires a thorough understanding of the business domain, data requirements, and anticipated data usage scenarios. The key characteristics of traditional upfront data modeling include:

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Microsoft Fabric and CluedIn: preparing your data for the era of AI together

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Microsoft Fabric and CluedIn: preparing your data for the era of AI together

At this year’s Microsoft Build event, Microsoft announced Microsoft Fabric - its brand-new data and analytics offering. In this article, we’ll outline what Fabric is, its key features, and how you can elevate Fabric’s capabilities with fully integrated, high-quality data from CluedIn.

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Graph versus Relational databases: which is best?

CluedIn

In the world of data management, businesses face the challenge of efficiently handling vast amounts of information. Traditional relational databases have been the go-to solution for many years, but the rise of graph databases has introduced a compelling alternative, especially when it comes to managing master data.

In this article, we will explore the key differences between graph and relational databases and discuss why graph databases are particularly well-suited for master data management.

Understanding Graph and Relational Databases:

Relational databases have long been the standard choice for storing structured data. They organize data into tables with predefined schemas, where relationships between tables are established through primary and foreign keys. Relational databases excel at managing transactional data and complex queries involving multiple tables.

On the other hand, graph databases are designed to store and manage highly connected data. They use a network-like structure composed of nodes (entities) and edges (relationships) to represent data relationships. Graph databases prioritize relationships as first-class citizens, making it easier to model and traverse complex connections between entities.

Master Data Management (MDM) and its challenges:

Master Data Management focuses on creating a single, consistent, and reliable version of key data entities within an organization. This includes customer information, product catalogs, employee records, and other critical data elements. The challenges in MDM stem from the need to handle vast amounts of interconnected data and maintain data integrity across multiple systems and business units.

Why Graph Databases Excel in MDM:

1.    Relationship-Centric Model
Graph databases inherently prioritize relationships, making them ideal for managing complex interconnections within master data. Integrating data using a Graph-based MDM system is much easier and quicker than one which uses a relational database, because the Graph will naturally find connections between the data that would be impossible for you to stipulate or discover on your own.

This is particularly beneficial for a number of use cases, such as building a single customer view. To be effective, a single customer view must aggregate data from various touchpoints and channels to create a holistic profile. This means integrating both unstructured and structured data from a number of source systems and applications and finding the relationships between them. This is simply not achievable using a relational database with pre-prescribed schemas and relationships. 

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Master Data Management for Life Sciences and Pharmaceuticals Industries

CluedIn

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.

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Why modern master data management (MDM) is to MDM what lakehouses are to warehouses

CluedIn

Introduction

As the volume and variety of data continue to grow, organizations face the challenge of effectively managing and utilizing all of their data assets for maximum business impact. Several solutions have emerged to address this challenge, including Data Lakes, Data Warehouses, Data Lakehouses, and Master Data Management (MDM) platforms.

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The role of AI in Master Data Management

CluedIn

Master Data Management (MDM) is the process of maintaining a central repository  - or single source of truth - of an organization's critical data, which includes customer data, product data, and other key data entities. The data is often scattered across multiple systems and applications, and MDM helps organizations to consolidate and manage this data effectively.

As organizations have embraced digital transformation in order to better serve their customers and enhance operations, this reliable supply of high-quality, accurate, and accessible data has become even more desirable. The problem, however, is that in many cases MDM has never quite lived up to the promise of delivering it.

Do we need AI to fix MDM?

Augmented data management techniques such as zero modeling and eventual connectivity have already gone a long way toward solving some of the well-established problems with MDM. For example, it is now possible for business users to wrangle with the data directly, without continually needing the support of IT teams. Upfront data modeling, profiling, and analysis are no longer a necessity as Graph-based platforms like CluedIn can do this work for you in a completely automated fashion once the data has been ingested. Systems like CluedIn are also capable of automating the integration of data in any format from a limitless number of sources. All of this has accelerated time to data value significantly and allowed businesses to fast-track insights, intelligence, and data science initiatives.

However, traditional MDM systems have struggled to keep up with these advances, and in some cases have turned to AI as a means of bridging the gap between what they should have delivered and the reality. For example, there are MDM players today using their own AI engines to help with data lineage – i.e. cataloging the sources of master data and their domain types, and mapping how master data moves between sources and applications. Advanced MDM systems like CluedIn can already do this – without relying on AI. Another example would be using AI to help automate schema matching. Again, not a job that requires AI if you’re using a Graph-based, augmented MDM platform.

What is the role of AI in MDM?

That said, there are areas in which the use of AI can dramatically improve the speed, cost, and ease of preparing data for ubiquitous use across an organization. As advanced as an MDM platform may be, there is no doubt that AI is a force accelerator when it comes to mastering data. Here are just a few examples of the potential application of AI in MDM:

  • Data Quality: Data quality is a major concern in Master Data Management, as data is often incomplete, inconsistent, or inaccurate. Advanced systems like CluedIn have already automated much of the data cleaning and enrichment process, but AI brings a whole new level of speed and simplicity to this exercise by using machine learning algorithms to automatically identify and resolve data errors, such as duplicate records or inconsistent data formats.
  • Data Governance: Creating and enforcing effective data governance is a challenge for every organization. It not only involves creating policies and procedures to ensure that data is properly managed and secured, but also the application of them which is where many data governance efforts fall down. With AI, however, the policy or rule can be automatically enforced immediately following its input into the platform.
  • Data Democratization: One of the main problems with traditional MDM is its heavy reliance on IT teams both in terms of deployment and ongoing use. Again, platforms like CluedIn have taken a low/no-code approach in order to make the system as accessible as possible, but the potential is for AI to take this to a whole new level as natural language processing makes even the least technical amongst us data scientists.
  • Data validation: A huge benefit in the application of AI with MDM is that it can effectively act as your “MDM co-pilot”. This not means that it can explain any of the decisions it took at your on-demand, but it will also intuitively corroborate (or challenge) your decisions too.
  • Data Maintenance: Ensuring that your data is up-to-date and ready to deliver at any time is an ongoing, resource-intensive task. AI can help to automate data maintenance by using machine learning algorithms to identify changes in data records and update them automatically. The benefit of doing this is that the model can essentially train itself based on the data in the MDM system – becoming more reliable and accurate over time.

Should MDM vendors build their own AI engines?

As previously mentioned, some MDM vendors have already built their own AI engines as part of their MDM offerings. The issue with this is that their models will never be as powerful or comprehensive as dedicated AI platforms like OpenAI, Google AI, and IBM’s Watson. Developing a high-quality AI engine requires significant expertise in machine learning, data processing, and software engineering. It is also a time-consuming and expensive exercise. Although MDM vendors may have some of the required specialisms and investment capacity at their disposal, AI is not their core focus, which is why in most cases they are better off partnering with AI vendors or utilizing existing AI platforms to provide their customers with the best experience.

What’s next for AI and MDM?

It’s an exciting time for the technology industry as AI gains momentum and starts to show exactly what it is capable of. At the moment, we have only witnessed a fraction of what AI can bring to the data management industry as whole, and MDM in particular. Without a doubt, AI will bring about a major transformation in how we prepare data to deliver insight in the future, and once its potential is realized the way we master data will be changed forever.

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