Valorem Visions 1.14 – Data Mesh

In this episode of Valorem Visions, our expert, Erin Woodward shares insights into Data Mesh and its industry use cases.

Transcript

So, the trend we're talking about today is Data Mesh. And data mesh is really a modern data architecture that allows users to easily access their data without having to transport it in and out of the data lakehouse or a data warehouse, and without the need for an intervention for a data team. Data mesh really focuses on distributed data ownership. So, let's think about data as a product and each product team, like your sales department, or your marketing department, or your customer service department, they have ownership of their data independently and securely. What this does is this reduces bottlenecks in silos of data management and promotes the scalability of data governance. Now, a few of the biggest use cases for data mesh are an automated virtual assistant or machine learning projects. We all know that businesses commonly use chat bots to support call centers and customer service teams. As frequently asked questions can touch on a variety of datasets, a distributed data architecture can make more datasets available to virtual agents, thereby providing better answers and better customer service by your automated agent. Another really popular use of data mesh we're coming across are the machine learning projects. By standardizing domain agnostic data, data scientists can more easily stitch together data from a variety of data sources, reducing the time spent on data processing. This time saved can help accelerate the number of models which go into production, enabling the achievement of their automation goals.

Valorem customers are organizations that recognize the need to evolve and understand the power and value of making data-driven decisions. Data mesh represents a move towards a self-service, data centric organization. Data is no longer hidden behind the magic curtain of IT; it's distributed and controlled by the people and teams that know it and use it most. This move towards democratization of data increases ownership and adoption of data initiatives, thereby increasing engagement. Engagement and interaction drive better planning, better decisions, and cost reduction. The distributed architecture that comes with data mesh moves away from batch data processing and instead embraces the adoption of cloud data platforms and streaming pipelines to collect the data in real time. Cloud storage and cloud compute solutions provide an additional cost advantage by allowing data teams to spin up large clusters as needed, paying only for the storage specified. This means if you need additional compute power to run a job in just a few hours instead of a few days, you can easily do this on a cloud platform by purchasing additional compute nodes. This also means improved visibility into storage costs, enabling better budget and resource allocation for engineering teams.

Well, our Data & AI team is filled with professionals that love to be on the bleeding edge of technology. Our team has a natural curiosity but pushes them to learn more and stay on top of the newest trends. This continued expansion of our knowledge and tools adds to an already impressive list of solutions that will add value and help our customers succeed. Taking advantage of our status as a Databricks partner and a global Microsoft partner, we have access to the latest and greatest resources and professionals that provide us the best material and the best technology to help us build solutions and design products in new and innovative ways. No two clients are the same and no two solutions are the same, and I'm really proud of the team that we've put together, their creativity, and their desire to always find a way to do it better. Valorem Reply can do a data estate assessment where we can evaluate the maturity of your data organization and determine your readiness for data mesh. Together, we can build a roadmap to success to modernize and transform the organization to a data-driven environment.