What is a data platform?

A data platform is a centralized system designed to manage the entire data lifecycle—from collection and storage to processing and sharing. By integrating advanced tools and technologies, it empowers organizations to handle both structured and unstructured data efficiently, providing a secure foundation for governance, analytics, and business insights.

A data platform’s architecture consists of various components working harmoniously: 

• Effective storage solutions—in recent years, more often cloud-based.
• Robust processing systems capable of handling vast amounts of information quickly.
• Comprehensive data pipelines leveraging the most suited data transformation method for your project.

Ultimately, a well-built data platform enhances your organization’s capacity to leverage its internal and external informational assets effectively. It plays a pivotal role in achieving strategic objectives for every phase of the data lifecycle—from gathering to transformation to analysis.

We will review the essential components and key features of a data platform to highlight how it creates value for your organization.

The purpose of a data platform

A data platform serves as a foundational framework that enables you to efficiently manage, process, and derive value from your data. It integrates various technologies and tools to support end-to-end data workflows. 

The key purposes include:

1. Centralized data management: It acts as the central repository for your organization’s data, consolidating information from multiple sources into a single system for easier access and management. 
2. Support for analytics and insights: Enables large-scale analytical processes, from historical reporting to real-time insights, empowering you with actionable intelligence derived from data.
3. Scalable processing: Supports various data processing needs, including batch processing for massive data volumes, stream processing for real-time analytics, and complex computations for machine learning and AI applications. 
4. Governance and security: Provides robust governance features to ensure data quality, compliance with regulatory requirements, and secure access for users and applications. 
5. Adaptability to use cases: Serves diverse needs ranging from integration and middleware functions, analytical workloads, stream processing, to advanced machine learning and AI models, offering flexibility to align with your business goals.

By reducing isolated information silos, data platforms foster enhanced collaboration across different departments. 

We would like to showcase some real-life purposes of a data platform.

• A predictive analytics platform using Hadoop and Hive technologies, for example, integrates diverse data sources, enabling a company to reduce fulfillment costs by £1 million and improve order delivery rates by 0.2%.
• A forecasting framework leveraging parallel processing and complex time series analysis achieved forecasting speed and flexibility, allowing faster and more accurate demand predictions.
• A cloud-based data platform with a medallion architecture automates data integration from over 20 sources, resulting in a scalable, error-free data platform that improves data quality and reduces operational costs.

Components and architecture of a data platform

The effectiveness of your organization’s decision-making process heavily relies on the quality of data generated through a data platform. This means that the better the data platform and its processes are set up and engineered, the better the insights you get from it.  

A data platform’s architecture consists of several crucial components that facilitate efficient data management and utilization. 

Storage layer

This layer supports specific data processing patterns such as structured, semi-structured, and unstructured data. It ensures durability, scalability, and accessibility for data across your organization, supporting various formats and performance needs like real-time or batch processing. 

Ingestion layer

The ingestion layer includes components and services that extract data from diverse sources—like databases, APIs, event streams—and load it into the data platform. 

It involves gathering information from external or internal sources with structured and unstructured data and transferring it into the system for future use through real-time, batch ingestion, or event streaming. This caters to varied use cases and ensures data integrity during the transfer. 

Among these sources, internal data, such as operational information, holds significant potential that is often overlooked. By integrating this internal data into analytical platforms, you enhance operational efficiency, reduce costs, and uncover new opportunities.

Transformation or processing layer

Data transformation is an essential part of a data platform. It converts raw information into a structured format ready for examination. It processes data by:

• Cleaning data to eliminate duplicates and inconsistencies
• Aggregating data from multiple sources
• Structuring data into a defined schema or format
• Enhancing datasets with extra details

This process supports sophisticated data analytics by delivering accurate and comprehensive datasets, hence improving data quality. Several techniques are available to choose from based on your project requirements:

• SQL-based data transformations
• Programming language-based data transformations
• GUI-based tools for data transformations
• ETL and ELT data transformation

If you want to know more about data transformation techniques, we invite you to read What does data transformation enable data analysts to accomplish? You’ll get a complete overview of the different transformations, the pros and cons of each method, and what to look for when choosing the right method for your project. 

Identity management and security layer

Robust identity and access management ensures data security and compliance by controlling access at multiple granularities, such as platform-level, column-level, or even individual rows. Policies ensure users have appropriate permissions based on roles, responsibilities, or compliance requirements. 

Egression layer

These are components allowing the export of processed data to external systems for further use. This includes APIs, data connectors, and other mechanisms that enable seamless data sharing or integration with downstream systems. 

Data observability layer

Data observability focuses on monitoring and investigating the health and performance of systems and data pipelines. It provides end-to-end visibility into the platform’s operations, tracking issues like data anomalies, schema changes, and performance bottlenecks. Monitoring across multiple processing stages ensures data reliability and helps identify the root causes of issues quickly. 

Orchestration layer

Orchestration involves the automatic coordination, configuration, and management of the platform’s components and services. It enables seamless execution of complex workflows, ensuring tasks are executed in the correct order, resources are optimized, and dependencies are managed effectively.

Data governance layer

Data governance is the overarching framework that establishes policies, processes, and standards for managing data within your organization. It focuses on ensuring:

• Data quality improvement ensures data is accurate, complete, consistent, timely, valid, and unique to support better decision-making.
• Regulatory compliance helps you to comply with legal requirements regarding data protection and privacy.
• Enhanced data security protects sensitive information from unauthorized access through well-defined secret management. It relies on effective secret management to protect sensitive information, like API keys, passwords, and tokens. A robust approach involves:
  • Automating secret storage and rotation
  • Using centralized systems
  • Implementing just-in-time role-based access control (RBAC)
  • Secrets are accessible only to authorized users or systems on a need-to-know basis
• Data democratization is the process of making data accessible to everyone within your organization, regardless of their technical expertise or role. The goal is to create a data culture that enables decision-making based on data instead of guesswork. This increases productivity, innovation, and informed decision-making. 
• Operational efficiency streamlines business processes by minimizing redundancies and optimizing the use of data resources.

Collaboration and sharing layer

The collaboration and sharing layer in a data platform is designed to foster teamwork and enhance the collective value derived from data. It provides tools and features that enable users to share insights, resources, and workflows effectively, ensuring that data-driven decisions are made collaboratively.

Analytics layer

The primary role of an analytics layer is the analysis of ingested and processed data. This happens through various methods.

• Visualization: This layer often includes tools for data visualization, such as Tableau or Looker Studio. 
• Business Intelligence: BI tools provide reports and dashboards focused on KPIs relevant to business performance.
• Advanced analytics: Depending on your organization’s data maturity stage, you can integrate advanced analytics capabilities, including predictive modeling, machine learning, and AI solutions. 

Within the analytics layer, we can find two further layers:

Metrics layer

The metrics layer defines and centralizes business metrics, ensuring consistent calculations and aggregations across the organization. Creating reusable logic for key performance indicators (KPIs) like revenue, churn rate, or active users, it eliminates redundancy and discrepancies. With proper governance and version control, the metrics layer ensures that teams have a single source of truth, promoting accuracy, efficiency, and transparency in metric usage.

Semantic layer

The semantic layer provides a business-friendly abstraction of raw data, making it accessible and interpretable for both technical and non-technical users. By organizing data into business-relevant concepts, like customers, orders, or products, it simplifies interactions and aligns data with organizational goals. Together, these layers ensure accurate, consistent, and accessible insights, empowering stakeholders to make informed decisions with clarity and confidence.

Machine learning and AI layer

Some machine learning solutions need the support of a data platform and others don’t. If you already know you will need the support of a data platform for your advanced analytics projects, it is crucial to integrate components that facilitate the right data processing, model development and deployment, as well as monitoring. 

End-to-end data management

End-to-end data management refers to a comprehensive approach that encompasses the entire data lifecycle within your organization—from initial collection to final analysis and reporting. It integrates various data processes into a single, cohesive system, ensuring that all aspects of data handling are streamlined and efficient.

It leverages the above-mentioned layers to cohesively develop and execute architectures, policies, practices, and procedures that allow you to manage its data lifecycle needs effectively.

The benefits of end-to-end data management include:

• Holistic view: It provides a unified perspective on data across your organization, facilitating better decision-making.
• Efficiency: By consolidating various functions into one platform, it reduces complexity and operational costs.
• Data integrity: It minimizes the chances of data mishandling by ensuring consistent processes across all stages of the data lifecycle.

Data quality: It ensures data quality through robust governance practices and strategic engineering.

Benefits of using a data platform

Once you embrace a data platform, you’ll experience notable advantages. We discussed some of the benefits of using a data platform here and there, but let’s consolidate them all here for better understanding. 

Improved decision-making

Providing reliable, real-time insights and advanced analytics enables you to make faster, data-driven decisions that enhance competitiveness and drive growth.

Operational efficiency

Streamlining data workflows and automating processes reduces redundancies, minimizes manual effort, and cuts operational costs, improving overall efficiency.

Enhanced collaboration across teams

Breaking down data silos and enabling shared access to insights fosters cross-department collaboration, improving transparency and alignment on business goals.

Scalable growth

Scalable data platforms support increasing volumes of data and complex analytics, enabling businesses to adapt and grow without infrastructure limitations.

Cost savings

Cloud-based storage and automated processes reduce infrastructure costs, minimize errors, and optimize resource utilization.

Predictive and proactive business insights

Leveraging advanced analytics and machine learning helps forecast trends, predict customer needs, and proactively address business challenges.

Improved customer experience

A unified view of customer data allows businesses to personalize interactions, improve service delivery, and enhance overall customer satisfaction.

Enhanced data quality and reliability

Ensures that decisions are based on accurate, consistent, and high-quality data, reducing the risk of costly mistakes.

Compliance and risk management

Robust governance and security frameworks ensure regulatory compliance, protect sensitive data, and mitigate risks associated with data breaches.

Faster time-to-market

By automating and orchestrating data processes, businesses can gain actionable insights more quickly, enabling them to respond faster to market opportunities.

Innovation enablement

Flexible and adaptable platforms empower you to implement machine learning, AI, and other advanced capabilities, driving innovation and competitive differentiation.

Holistic organizational visibility

Provides a unified view of data across all departments, enabling leadership to monitor performance, identify gaps, and align strategy with operational goals.

How to build a data platform that supports the business

The timescale of building a data platform varies with the goal you want to reach. As experts in the data realm, we identified three major time frames.

1. In some cases, building a data platform needs to happen quickly because the business is in dire need of one and is actively losing money and opportunities to grow.
    
2. In other cases, organizations already have an idea of what the data platform should do and look like, and a working PoC can be created in a reasonable timeframe. The idea is to create a long-term solution based on the PoC, so planning is a vital part of it. Yet the actual engineering provides only the most important fraction of the data platform to scale from.
    
3. Last but not least, the long-term plan involves creating a complete strategy for scalability, viability, and robustness and then building the data platform bit by bit or in a complete step. 

You need to choose the right technology and create a coherent and sustainable solution to support future business needs and data requirements and withstand changes and unexpected incidents. 

There are several ways to do this.

Open-source technology

Open source is a great way to avoid vendor lock-in but comes with a trade-off in the form of adoption and integration complexity. The data platform’s architecture is built on components that are mostly publicly available or are easily exchangeable. This approach frees you from sticking to one vendor or technology that could become expensive to change in the future if the need arises. 

Off-the-shelf solutions

Off-the-shelf solutions are a possibility, even if they mean vendor lock-in. However, you need to choose wisely when it comes to such solutions. They come with some restrictions and compromises you will need to assess. 

Complete data platforms

Complete data platforms, such as Databricks, are a great way to enable ease of use, ongoing support, and data platform completeness. In specific cases, these factors are more important than vendor lock-in. 

A combination of two or more solutions

One of the best possibilities to cater to your specific business needs and data requirements is to mix and match open-source, off-the-shelf solutions and/or complete data platforms. Depending on those needs, you choose what technology your organization is already familiar with and support long-term goals. This approach demands extensive experience in various technologies, where a software development partner comes in handy. 

Technical considerations

Here are key considerations for designing and implementing a data platform, broken down by focus areas:

• Cloud: Determine if there are strict requirements for using a specific cloud vendor.
• Security: Incorporate data protection and retention regulations directly into the platform design.
• Data engineering: Clarify the scope—whether it involves building a new platform, extending existing solutions, or migrating to a different stack.
• Software engineering: Address customer-specific needs like custom data ingestion, exports, or integrating with surrounding systems.
• Frontend: Assess the need for custom integrations with reporting tools or specialized interfaces for monitoring, alerting, or cataloging.
• DevOps/DataOps: Evaluate the maturity of your organization’s processes, tools, automation, and self-service capabilities.
• Dashboard engineering/data visualization: Ensure effective data presentation using standard visualization tools on the market or if you want to create your own visualization.

Data platform challenges to consider

There are certain challenges when it comes to both off-the-shelf and open-source software. 

Off-the-shelf solutions

• Lack of customization: Off-the-shelf solutions are designed for a broad audience. Sometimes they fit your organization’s data requirements, and sometimes they lack specific features the business needs may require. The one-size-fits-all approach might lead to inefficiencies as organizations grow, and you may need to adapt your processes to fit the software instead of the other way around. 
• Integration challenges: If your existing technology stack is hardly compatible with the chosen solution, it might be hard to connect with legacy systems or other tools in use. 
• Scalability issues: In some cases, off-the-shelf solutions do not scale well with growing business needs. Over time, your organization’s requirements may outpace what the software can offer, resulting in performance degradation.
• Data security and privacy risks: You must be careful about using and integrating open-source and off-the-shelf solutions. Some third-party vendors’ solutions might not align with your organization’s specific security needs, such as the location of data storage in the context of data transfers from Europe to America. 
• Ongoing costs: Off-the-shelf solutions initially appear cost-effective, yet often come with hidden long-term costs such as licensing fees.

Open-source software

• Limited official support: Open-source solutions often lack formal support channels, leading to difficulties in troubleshooting and maintenance. Organizations mostly rely on communities and documentation. Depending on the software, communities may be active or sparingly write their suggestions and solutions. 
• Resource intensity: Implementing open-source solutions requires significant resources, like skilled personnel with expertise in programming, architecture, and database administration. Organizations often underestimate the time and skill needed to effectively build efficient solutions. 
• Integration challenges: Many open-source solutions operate independently, creating the need for specific integrations. To achieve a cohesive data strategy, knowledge of these platforms and tools is paramount.
• Fragmentation risks: These solutions' open nature can lead to fragmentation, where multiple versions or forks of the same software may cause compatibility issues. 
• Management complexity at scale: Scaling open-source solutions might introduce management complexity. This encompasses governance, control, and tracking changes in the codebase.

How software development vendors help

Software development partners, like VirtusLab, help you overcome challenges and cater to their individual business needs. 

• Designing robust architectures: At VirtusLab we select the appropriate technologies and tools, define schemas, and establish governance to ensure compatibility, scalability, security, and efficiency. 
• Implementing data integration solutions: We integrate various sources of external and internal data into a unified data platform. By employing advanced integration techniques, we ensure that disparate data systems communicate and share information seamlessly.
• Automating data management processes: Automation is key to ensuring data quality and accessibility. We automate processes for data ingestion, cleansing, and transformation. 
• Enabling self-service capabilities: We empower self-service for non-technical users with platforms like Databricks, which are ideal for these situations. 
• Ensuring data governance and security: VirtusLab ensures the security of your sensitive data. We help establish a data governance framework that includes metadata management, lineage tracking, and access control. 
• Supporting advanced analytics and BI tools: We create top-notch data pipelines and integrations to support your advanced analytics capabilities. From predictive analytics with ML models or integration with BI tools, VirtusLab ensures high-end data quality. 

The future of data platforms

Data has become a cornerstone of modern organizational operations, driving decision-making and strategic initiatives. As organizations increasingly adopt data platforms, the need to adapt and accommodate advanced analytics and predictive modeling has never been greater.

The rapid advancements in automation and artificial intelligence (AI) are transforming data management and processing. By implementing automated workflows and intelligent systems, organizations enhance efficiency, enabling in-depth analysis and reducing reliance on manual tasks.

Machine learning further amplifies the value of data by enabling sophisticated analytics with minimal manual effort. This technology enriches workflows and delivers deeper insights into organizational and operational data, empowering businesses to make more informed and effective decisions.