Articles

The value that engineers bring to the data stack is undeniable. They play a crucial role in ensuring data is primed for insights. However, the immense value and necessity of involving Domain Experts in the data supply chain, not merely as consumers but as contributors, cannot be overstated.

IT and Engineering have distinct focuses within the data supply chain. Their work aligns with the overarching goal of preparing data for insights. Yet, there's a specific persona that is interested in enhancing the intrinsic value of the data itself. Consider the analogy of a Netflix movie director. While Netflix's ability to deliver content globally, start streaming in seconds, and function across devices is impressive, the content—the movie—remains the star. It requires directors, writers, and editors to refine the content to the point where it captivates the audience. Similarly, one might ask, who is the "director" for your customer data?

During its 30-year history, many myths have arisen around Master Data Management (MDM), some of which may have been valid in the early days but are no longer true today. MDM has come a long way, and is experiencing something of a renaissance as progressive platforms take advantage of technical innovations like Graph databases and Generative AI, and offer increased commercial flexibility via the major CSPs.

Join us as we expose the eight most common MDM myths below, and discover how MDM has developed to become more accessible, manageable, and impactful.

We sat down with Fredrik, the Team Lead for Data & Integration Development at Svevia, to discuss all things Master Data Management and beyond. 

In this article, we focus on comparing relational databases and graph databases in the context of Master Data Management (MDM). We examine how each approach impacts MDM processes, highlighting key differences and considerations for organizations.

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.

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.

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.

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:

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.