Explainer: Building High Performing Data Product Platform
Building a high-performing data product requires a strategy and the essential functionalities' clarity. Here's a quick overview.
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Data is everything, and enterprises are pushing the limits to capture, manage, and utilize it optimally. However, given the monumental rise of Web3, companies may not be able to sustain themselves with conventional data management techniques. Instead, they are inclined toward futuristic analytics and need a stronger architecture to manage their data products. As per Forbes, in 2019, 95% of organizations were not managing their unstructured data and ultimately lost out on valuable opportunities.
As we know, a "data product" is an engineered, reusable data asset with a targeted purpose. A data product platform integrates with multiple source systems, processes data, and makes it instantly available to all stakeholders.
Building a high-performing data product needs a strategy and clarity of the essential functionalities. Here's a quick overview.
Expectation Scoping: The Bare Minimum That the Platform Should Deliver
An ideal data product platform should enable end-to-end test data management of products across various stages such as engineering, testing, deploying, and monitoring. This should accommodate a broad variety of workloads.
It should enable the data teams to seamlessly define and maintain the metadata for data products, including the schema, policies, connectors, policies, governance, etc. Furthermore, given the rapid increase in the rate of data generation in real-time, the platform should manage every data set efficiently while providing them on demand. Among others, the data product platform should deliver the following functions:
- End-to-end product monitoring: Total visibility about the performance of the data product, its utility, and ability to start/pause/stop data flow.
- Product cataloging: Enable users to build relationships between data products, the sources, and the end users, all represented in a knowledge graph.
- Built-in Business Analytics: Create and analyze smart reports through interactive dashboards.
- Support iPaaS model: Public cloud deployment with SSO authentication, multi-tenancy support, etc.
Faster Streaming: In-the-Moment Data Processing Like Never Before
At the core of every analytics application is a high-performing data product platform that streams and manages data in real-time. In pursuing the same, data enterprises are building competitive fabric and mesh products. K2view, for example, has successfully implemented micro databases to enable business entity-level data storage. This means their data product platform manages millions of micro databases, each of which only stores data for a particular business entity.
It empowers the platform to achieve enterprise-grade resilience, scale, and agility. Their platform performs end-to-end management in iterative delivery cycles that cover design and engineering, deployment and testing, and monitoring and maintenance. In addition, since billions of micro databases can be managed in a single deployment, it ensures faster filtering and streaming.
Data Managers Centric: Interactive, Easy to Adapt, and All Inclusive
Data managers are an integral asset as they develop strategies and define performance metrics for the platform. While they should have a diverse range of analytical and DevOps skills, an intelligent platform can utilize them optimally. To maximize business value and return on data investment, build a low-code/no-code integrated development environment (IDE).
This will enable the managers to seamlessly execute the building, testing, and deploying of products. Furthermore, it would simplify schema creation, transformation logic, orchestration, integrations, and more.
Data managers must bridge the gaps between data consumers and engineers across domains by continuously communicating their needs. They capture the requirements of the consumers and collaborate with engineers and scientists.
A high-performing and interactive data product platform would automate the process as far as feasible.
Flexible Architecture: Adaptive to Different System Landscapes and Operational Models
Like all contemporary products, data platforms should also be deployed across the system landscape – on-premise, in the cloud, or both. Not only it maximizes flexibility but makes the solution easily scalable.
You can choose either data mesh or fabric as your preferred architecture as a fundamental construct. While a fabric follows a modular and centralized framework, a mesh implements a federated data strategy.
In a data fabric, the centralized structure integrates the data with the analytical tools while enabling a central entity to define the data products. Furthermore, it adapts to changes over time based on metadata analysis.
On the contrary, the mesh decentralizes the architecture. It enables the business domains to define and create the data products as per need. With anonymity, business domains can create and scale product-centric solutions in real time and with more finesse.
While there's no bottom line to the fabric v mesh debate, a data product platform should provide enough flexibility to adapt to both.
Get Ready for Web3
Data-driven organizations have an early entrant opportunity to excel in the web3. However, to stem value from trusted and qualitative data sets, they should include data product platforms that embrace all the above features.
Since data products drive operational and analytical workloads, enterprises should steer all focus to build a high-performing data management landscape.
In this blog, I discussed the key components that make up a web3-ready data product platform.
I hope this cover all. I'd like to know more about your data product platform expectations and strategy.
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