Data is at the core of every successful business today, and leaders are investing heavily in modern data platforms that empower their organizations to address all the data-related challenges by transforming data processes end to end. With every advancement in cutting-edge technologies, modern data platforms are getting more robust to deal with the challenges of growing customer expectations, rising regulatory complexities, and stiff competition that every industry faces today.
Table of Contents
What is a Modern Data Platform?
A modern data platform is a centralized data management ecosystem powered by a set of cloud-native solutions that help in the collection, storage, transformation, and processing of an organization’s data for deep actionable insights.
What is the Role of Modern Data Platforms in the Current Business Landscape?
Understanding the 6 Fundamental Layers of a Modern Data Platform
1. Data Ingestion Layer
2. Data Storage Layer
3. Data Transformation Layer
4. Data Analytics Layer
5. Data Observability Layer
6. Data Orchestration Layer
In addition to these six layers, there are other layers such as the semantic layer to standardize the calculation of key metrics, the data catalog to leverage metadata, data discovery to navigate the data catalog, data governance to establish rules and frameworks for data management, and the access management layer to meet complex regulatory requirements.
Key Considerations While Adopting a Modern Data Platform
Whether an organization chooses to build a modern data platform, get a SaaS-based solution, or explore open-source options, is a strategic choice. It depends on various factors, including the organization’s resources, expertise, and specific requirements. Many times, the decision involves a combination of these approaches. For instance, an organization might use a SaaS solution for certain aspects while implementing open-source tools or custom solutions for specific components that require more customization. Here are a few key considerations in the decision-making process of adopting a modern data platform:
- Data Volume: One of the most important selection criteria for a data platform is the volume of data your organization works with and the future scalability plans.
- Data Team Size and Expertise: Building a modern data platform can be resource-intensive and not all organizations have the required team capacity or capability to support it. Seeking external guidance can be instrumental in making the right decisions for the long term.
- Budget: Starting from upfront costs to expenses for ongoing support and maintenance, evaluating the expenses and the budget at hand is critical to avoid unexpected roadblocks.
- End User: Establishing who is going to be using the tool—is it data engineers, analysts, business users, or a diverse set of stakeholders across the enterprise—can help decide whether to build the solution in-house or procure a collaborative platform.
- Objective: Establishing the use cases and problems the platform will solve is also a crucial deciding factor in the adoption of a modern data platform. The objective should be to build a modern data platform that is extensible to diverse use cases.
Getting Started
Author
Anand Subramaniam
Anand Subramaniam is the Chief Solutions Officer, leading Data Analytics & AI service line at KANINI. He is passionate about data science and has championed data analytics practice across start-ups to enterprises in various verticals. As a thought leader, start-up mentor, and data architect, Anand brings over two decades of techno-functional leadership in envisaging, planning, and building high-performance, state-of-the-art technology teams.
