For an organization that wants to be data-driven, a data platform is the heart and soul of its business. Data Platforms enable organizations to manage and orchestrate their data landscape efficiently to drive data-driven business decisions.

A data platform is a collection of key processes and technology components that enable the collection, transformation, sharing, and analysis of data to generate business value. A good data platform takes care of end-to-end data engineering and management needs of converting raw data into actionable business data products.

Key components of a data platform

Based on organizational requirements, a data platform’s capabilities might differ. Multinational companies’ data platforms would be significantly different from what startups are using. However, there are certain building blocks of data platforms that should be relevant for organizations of all shapes and forms.

Data ingestion: The first key component of a data platform is data ingestion. Data ingestion refers to the process of connecting to the data sources and migrating that data to a target destination (data warehouse, data lake etc.) using data pipelines. There are two major kinds of data ingestion processes: batch and near-real-time data ingestion. Batch ingestion is used for a variety of use cases including retail sales analysis, supply chain management, historical data analysis, etc. Whereas, real-time data ingestion is crucial for financial fraud detection, online gaming, trading platforms etc.

Data Storage: Data resides in the data storage layer after being collected. There are different types of data storage options available for organizations such as – data warehouse, data lake, and data lakehouse. Organizations can choose specific data storage as per their use cases’ demand.  A Data warehouse is ideal when you want your data to be in a structured format for fast querying and analysis. Data lake provides a low-cost data storage option for all data types (structured, semi-structured, and unstructured). A data lakehouse architecture combines the capabilities of a data warehouse and data lake and provides the capability to store data in its raw form with efficient and fast data queries.

Data Orchestration: This layer provides end-to-end management and scheduling of all data workflows. In the current data landscape, organizations deal with large volumes of data spread across multiple sources. This makes the data pipelines complex and interdependent, which makes manual scheduling and management an impossible task. Data orchestration provides an automated way to streamline the management of data pipelines to ensure timely data delivery.

Data transformation: Data in the storage layer is not yet consumption-ready for data and analytics initiatives. It needs to go through certain transformation steps before it’s ready to be consumed by downstream users. In data transformation, raw data goes through a series of cleansing (for example, missing value treatment), data validation (including outlier treatment), and restructuring (like merging datasets) steps.

Data analysis and visualization: Data analysis is a data consumption layer with different tools such as BI reports and dashboards, machine learning-enabled tools, and self-service business user tools (say, a pricing tool for a sales team) – all of which consume data from the consumption layer for its intended business purpose. 

Data quality and observability: The best of an organization’s data initiatives are likely to fail if the quality of data isn’t good. In today’s complex data environment, it has become very difficult to detect and troubleshoot data quality issues. Organizations need modern data quality tools that provide data monitoring, troubleshooting, and automated business quality checks to ensure consistent delivery of high-quality data to end users. Good data quality tools enable organizations to proactively monitor and analyze their data pipelines, model performance, and potential biases in real time and reduce the cost implications of poor data quality. 

Components of a Data Platform

Data ingestion tools

Organizations need data ingestion tools that provide out-of-the-box connectivity to various popular structured, unstructured, and semi-structured data sources. Ideally, organizations should have a combination of batch and real-time data ingestion tools. AWS Glue, Stitch, and Fivetran are some of the popular data ingestion tools.

Data storage

Based on the specific use case requirements organizations can select data lake, data warehouse, or data lakehouse options. AWS, Microsoft Azure, and GCP provide low-cost data lake options whereas Snowflake, Amazon Redshift, and Google Big Query are popular choices for cloud data warehouses. Data lakehouse is pioneered by Databricks and Databricks Delta Lake is one of the most popular solutions. Other industry leaders like Snowflake and Microsoft also provide data lakehouse solutions and offerings.

Data orchestration

Data orchestration tools automate the process of managing and scheduling data pipelines to increase efficiency and improve consistent data delivery. Some popular data orchestration tools are Apache Airflow (open source), Luigi, Dagster, and Prefect. Apache Airflow is one of the most popular orchestration engines due to its flexibility, scalability, and community support. Dagster provides unified data pipeline programming whereas Luigi provides simplicity and ease of use of pipeline orchestration.

Data transformation

Data transformation tools convert data into analytics-ready datasets format. Dbt (data build tool) is one of the most popular open-source data transformation tools. Other popular players in this domain include – Datameer, Hevo Data, and Matillion. Datameer excels in integrating seamlessly with the Hadoop ecosystem, leveraging its strengths in processing large-scale data on distributed systems. 

Data visualization and analysis

Data analysis and visualization tools provide an interface to explore and visualize data. Some of the popular data visualization tools include Power BI, Looker, and Tableau. Tableau has been used by companies for many years due to its ease of use, powerful analytics capabilities, scalability, enterprise features, and continuous innovation in the data visualization space. On the other hand, enterprises using the Microsoft Suite may prefer Power BI due to easy compatibility with MS Excel and other Microsoft products.

Data quality and observability

Data quality is the heart of an organization’s data-driven initiatives. Poor data quality can derail progress and prevent organizations from being truly data-driven. To ensure robust data quality management, organizations need tools that enable data quality and observability through a single platform. DQlabs’ Modern Data Quality platform provides role-based and relevant data and business quality checks that fuel better decision-making and ensure effective business outcomes.

Benefits of a data platform

Single source of truth: A data platform is the first step for an organization to become data-driven. By eliminating data silos in the organization and integrating all data sources effectively, a data platform provides a single source of truth for data.

Efficient data management: A data platform enables organizations to manage their ever-increasing data landscape. By providing end-to-end data management solutions, data platforms enable data integrity, consistency, and quality across the organization. 

Improved efficiency: With the correct set of tools and technologies, a good data platform enables automated and augmented data management and improves the efficiency of data and business teams by eliminating and reducing manual data handling tasks.

Collaboration: A good data platform provides a mechanism for different data and business personas to interact and work together on data initiatives. This promotes a culture of data sharing, collaboration, and data-driven decision-making across the organization.

Self-service capabilities: A good data platform should allow business and non-technical users to discover, access, and explore datasets by reducing the dependency on the technical team. This enables data democratization and accelerates the process of unlocking insights from data.

What to consider before you start building your data platform

Organizations need to consider some key questions before starting their data platform journey.

Data sources: What are the types of data sources you own? Are they structured, semi-structured, or unstructured? How many different data sources are you using? These questions, and more, will help organizations to select and then shortlist data ingestion vendors, based on their relevant capabilities.

Distributed architecture: To avoid vendor lock-in, leverage the strengths of different cloud providers, and based on regulatory and compliance needs, more and more organizations are choosing multi-cloud and hybrid cloud architecture approaches. Organizations should build a data platform that unifies their data and analytics landscape in the multi-hybrid cloud-driven distributed architecture. 

Persona needs: Who is the target consumer of various data at your organization? Is it Max, who works in the data science team, or is it Mark, the marketing manager? With more & more focus on data democratization, there might be certain organizations that need a data platform just for their technical personas like data scientists and data analysts. For this, they might not invest as much in self-service data management or collaboration capabilities. Other organizations may want to empower their business and non-technical personas, for which they would create a data platform that enables collaboration and self-service data management practices. Here, the focus would be on building a culture, in addition to data access & management.

Security and compliance: Data security and compliance policies might differ from organization to organization. Based on one’s own needs, organization-level policies should be established for data security and compliance. 

Scalability: Organizations should project their data volume needs, based on which, they should build a data platform – one that is able to handle their growing data needs.  

The above questions would help organizations to introspect, reflect on their data needs, and accordingly choose & build a robust data platform.

Conclusion

Establishing a robust data platform is not just a technological effort but a strategic initiative for any organization aiming to enable a data-driven culture. 

A well-executed data platform empowers businesses to make informed decisions, improve operational efficiency, and accelerate innovation. It serves as one of the key levers for achieving data-driven success and staying competitive in today’s dynamic business environment.

By addressing fundamental questions such as the number and diversity of your data sources, which user personas your data platform is catering to, what security and compliance measures need to be in place, and by planning for scalability, organizations can lay a strong foundation for their data initiatives.