Building a Modern Data Platform in the Big Data Landscape 

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?

Leveraging modern data platforms integrated with advanced AI tools and techniques, enterprises can efficiently handle and analyze large volumes of data for a data-driven enterprise approach. Modern data platforms empower businesses to streamline processes, deliver superior customer experiences, foster innovation, and ensure strategic decision-making for a competitive advantage in the long run. Businesses that ensure alignment with the continuous evolution of data platforms can successfully adapt to the changing business environment.  

Understanding the 6 Fundamental Layers of a Modern Data Platform

To understand the potential of a modern data platform in transforming big data to make it consumable across the enterprise for insights, it is important to know the six core components or layers of a modern data platform. 
Modern Data Platform Layers
1. Data Ingestion Layer
The vast volumes of data generated at various touchpoints, such as online portals, social media platforms, surveys, and feedback forms, in diverse varied formats, must all be leveraged efficiently for comprehensive insights. This is one of the biggest challenges faced by the data teams today. Modern data platforms streamline the Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) data ingestion processes, enabling enterprises to overcome challenges like siloed data. They support continuous data ingestion, either in batches or as real-time streams based on an enterprise’s evolving needs. 
2. Data Storage Layer
An increasing number of companies today are retiring from their on-premises data systems and embracing more affordable and modern cloud-native data storage solutions. Depending on the specific challenges and objectives, enterprises are adopting one or a combination of cloud-based data warehouses, data lakes, and even emerging data lakehouses to make data readily available for various analyses. Solutions like Databricks and Snowflake are gaining popularity as they offer integrated platforms combining data storage and analytics capabilities. 
3. Data Transformation Layer
The ingested and stored data must be cleansed and refined to make it useful for analysis and reporting. This stage of enhancing data quality is critical as poor quality data leads to inaccuracies and compromised insights. Apache Airflow, open source dbt, and Google Cloud’s Dataform are some leading data transformation tools that enable data engineers to transform data for use in the next layers of a modern data platform. 
4. Data Analytics Layer
The analytics layer serves as the user access layer, sitting on the foundational ingestion, storage, and transformation layers. This layer of a modern data platform allows access to data that is collected, stored, and transformed for decision support. This user access layer encompasses various business intelligence (BI) tools like Looker, Power BI, and Tableau, product analytics or other experimentation tools or even a Customer Data Platform (CDP) to be used by data analysts or business users for decision-making 
5. Data Observability Layer
The increasing complexities of data pipelines in a modern data platform call for a robust data observability layer to ensure the health and performance of the pipeline for accurate real-time insights. This layer allows organizations to identify and troubleshoot issues in their data ecosystem quickly, to ensure the data is recent, complete, formatted, tracked for schema changes and data lineage for reliable insights. The data observability layer ensures continuous monitoring of data even when it is at rest to meet security and regulatory compliance requirements. 
6. Data Orchestration Layer
The multiple manual and automated tasks in a modern data platform must be well-orchestrated to ensure seamless execution of tasks such as data workflows, ETL processes, and data pipelines. Organizations can utilize orchestration tools to automate, schedule, and monitor complex data tasks, facilitating a streamlined and well-managed data environment. As an organization’s data platform scales to accommodate the increasing volume of data and becomes more and more complex, the orchestration layer helps ensure optimized efficiency and maintains data integrity across the platform. 

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

In this increasingly data-driven world, organizations that understand the strategic significance of investing in robust data solutions for organizational success are better positioned to harness the full potential of their data assets. KANINI is a digital transformation enabler, guiding enterprises spanning industries such as banking and financial services, healthcare, and manufacturing to embrace modern data and analytics solutions that address their unique business challenges. If you want to know more about how a modern data platform can help your organization foster sustained growth, innovation, and resilience to evolve with the emerging trends in the data management landscape, speak with our experts. 

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.

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