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How treating data as a product is bringing speed, collaboration, and ultimately democratization to data use.
The concept of the Data Product has come to the forefront as a fundamental building block in the Data Mesh framework. Some of the very first questions that practitioners ask are:
Let’s start with defining a Data Product. In our daily life, we engage with many products, e.g., a shoe, a phone, or a car. Every product has the following common characteristics:
Data Products, much like other products in our day-to-day life, present a ready-to-use entity. Is a spreadsheet then a Data product? Not really. Just as leather or fabric are to shoes, a spreadsheet or a Data API or a database table, for that matter, are the raw materials, not the product itself. What makes a collection of data into a Data Product is the additional features that make the data ready to use.
These are the characteristics of ready-to-use Data Products:
The Data Mesh framework is centered around the idea of letting domain users control and manage data. This is a direct result of the need to democratize data; today, almost every person in every function needs to use data whether they are in marketing, sales, logistics, operations, HR, finance, or product. Data mesh takes us into a world of distributed control versus a model of centralized control that overwhelms engineers and frustrates data users.
While data democratization has previously been an important goal, up until now it had been unclear how to achieve that. Is data democratized if everyone has access to a database? Clearly that was not a viable approach. Data Products make the goal of democratization achievable by presenting an entity that provides consistency to data access, governance, documentation, discovery, and also the delivery of ready-to-use data.
So we understand Data Products and clearly the demand is high. But like any product, Data Products need to be produced before they can be consumed. So how can Data Products be created? At Nexla, we have been working on this concept for nearly 5 years and our approach has been twofold:
Derived Data Products are a result of applying a combination of transforms, filters, enrichments, and joins on a combination of data products. At Nexla, we deliver a simple formula
Nexset_Derived = Function (Nexset_1, Nexset_2, .., Nexset_n)
What makes a derived Nexset even more powerful is that it is a full-fledged Data Product and in every way identical to any other Nexset. That means it can serve as an input to create more derived Nexsets each with its own documentation, access control, etc.
So how are Data Products ready to use? One of the key things to know is what makes data ready-to-use for different users. An analyst might want data in Tableau, a developer might need it via an API, a business user might need it in a spreadsheet. while yet others prefer all their data in a warehouse.
Going from ready-to-use data to data-in-use actually requires delivering data into the application of choice. Data Products in Nexla come with hooks for delivery, so clicking the “Send” button in a Nexset allows the users to choose the format and system in which they want the data delivered.
Data Products are an exciting concept and a big leap forward in putting ready-to-use data in the hands of more users as we all march forward into a brave new data-powered world, ready to make our business smarter and more efficient. Visit our homepage to learn more about how Nexla delivers Data Products to some of the world’s most advanced companies including LinkedIn, LiveRamp, Doordash, Freshworks, Poshmark, Nerdy, etc.
This article is part of our Data Mesh series where we explore the technology behind data mesh, how to build a data mesh framework, the applications of data mesh, and how data mesh operates with your existing data technologies and frameworks.
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