Guide to Data Platforms

A data platform is a system designed to manage, store, integrate, and activate your organization’s data. It acts as the foundation for turning vast amounts of data into business value. That value includes everything from providing 360-degree views of your customers and your business to powering numerous use cases — such as supporting AI, agentic AI, and analytics, informing decisions, and driving automation across sales, service, marketing, and commerce. By improving how your data is collected, processed, and analyzed, data platforms help you manage and use data across its entire lifecycle.

These platforms also play a crucial role in unifying data across your whole organization, breaking down silos, and making data accessible and usable by various groups and stakeholders.

A data platform architecture typically includes these components:

• Data ingestion
• Data storage
• Data transformation and integration
• Analytics and visualization
• Governance and security

A data platform infrastructure includes cloud or on-premises environments, networking, and storage systems that work together to ingest, process, and serve data in real time or batches. A modern data platform infrastructure is designed to scale with growing data volumes and complexity. It also enables secure and compliant data handling.

Data platforms help you collect and manage your data, which involves the steps below.

Data ingestion is the process of collecting and importing data into the platform, serving as the entry point for all data workflows. It involves collecting data from various sources, such as enterprise systems, applications, data lakes and warehouses, social media, IoT sensors. Data is usually integrated with either:

• Real-time ingestion: Ideal for scenarios requiring immediate insights, like stock market monitoring.
• Batch ingestion: Best for scheduled processing of large datasets, like monthly sales reports.

It’s common to use the traditional Extract-Transform-Load (ETL) or Extract-Load-Transform (ELT) pipelines to handle batch processing.

Once ingested, data is stored in centralized repositories which are, ideally, scalable, durable, and accessible. These repositories can be:

• Data lakes: They can store large volumes of structured, unstructured, or semi-structured data that is usually raw and unprocessed.
• Data warehouses: Enterprise data management systems that collate structured data from multiple sources, such as your customer relationship management software and point-of-sale transactions. Data warehouses can also store unstructured data once it’s processed.
• Data lakehouses: By combining the best features of data warehouses and data lakes, data lakehouses make it faster and easier for you to combine all your data and extract insights from it, no matter the format or volume.

Raw data must often be cleaned, standardized, and enriched before it becomes useful. This data transformation step involves:

• Removing errors or duplicates.
• Converting data into compatible formats.
• Harmonizing datasets for a 360 view of your customers.

Tools like Apache Spark work well for distributed data processing while SQL is useful for structured transformations.

The processes here include:

• Data cleaning : Removing errors, duplicates, and inconsistencies.
• Normalization: Standardizing formats for better compatibility.
• Enrichment: Adding context or external data to enhance datasets.

Tools that automate these processes can speed up data integration, so you can unify your data faster. Following that same retail company example, data analytics and AI analyze the data, creating unified customer profiles with details like preferences, shopping behavior, and demographics.

Data platforms can revolutionize your business strategy and operational execution. Here are some key benefits.

• Improved operations and efficiency: Data platforms can unify your data and reduce data silos. Unified data can boost efficiencies across your organization. You won’t have to waste time searching for answers or wait for weeks before analytics teams can pull data from various systems to create custom reports. Unified data is more accurate because it’s not duplicated across several systems and it’s more comprehensive — combining diverse data sets from numerous sources. Most data platforms give you the tools to optimize unified data workflows and manage your data health throughout its lifecycle.
• Powering the data foundation for AI success: AI and agentic AI play a crucial role in making data platforms more effective. These technologies can process and analyze data much faster than humans, ensuring that your data is always ready to use. For example, AI can automatically clean up messy data by removing errors and duplicates, which saves a lot of time and effort. Agentic AI, which can work on its own, constantly checks and improves the quality of your data, making sure that the insights you get are always accurate and current.
  
  AI-driven data platforms can also bring together data from different sources, like customer information from your website and sales data from your stores, to give you a complete picture. This helps you make better decisions, whether you're predicting future trends or making real-time choices. For instance, a retailer can use AI to quickly combine data from online and in-store purchases to identify which products are most popular and adjust their inventory accordingly. By doing this, AI turns raw data into valuable information that can drive your business forward.
• Personalize customer experiences: By unifying data across touchpoints, data platforms can give you a 360-degree view of customers that helps your marketing and sales teams deliver tailored experiences. E-commerce companies like Amazon, for example, use data platforms to personalize product recommendations, driving higher customer satisfaction and loyalty.
• Accelerate innovation and agility: Data platforms foster innovation and rapid experimentation. For example, a logistics company can track shipments in real-time and optimize delivery routes with the help of AI to avoid inclement weather. And any company with a new product can test hypotheses and pivot strategies based on sub-second real-time data updates.
• Drive better decisions with actionable data: Data platforms help you analyze historical and real-time data, uncover trends, and predict future outcomes. With advanced analytics and visualization tools, you can make faster and more accurate decisions. For example, a retailer can analyze historical data to identify best-selling products and increase stock as needed for an upcoming sale.

Data platforms come in various forms, each designed to address specific business challenges and data management needs. Below are four types that many organizations use.

Cloud data platforms use cloud infrastructure to store all your data. They can be a scalable, easily accessible, and flexible data solution because you can process, analyze, and store data without the constraints of on-premises systems. Below are some advantages of cloud data platforms.

• Scalability: Elastic infrastructure allows cloud data platforms to handle growing data volumes
• Real-time processing: Many cloud platforms enable real-time data ingestion and analytics
• Cost efficiency: Pay-as-you-go pricing models reduce upfront infrastructure costs

Enterprise data platforms (EDPs) are comprehensive solutions tailored to meet the complex data management needs of large organizations. These platforms integrate data from multiple sources consistently in a way that’s scalable and compliant.

Some common uses of EDPs include:

• Enterprise resource planning (ERP): Systems like an ERP can help you integrate data across functions such as finance, supply chain, and human resources
• Customer relationship management (CRM): Platforms like Salesforce centralize customer data and improve customer engagement strategies

By offering integration capabilities, EDPs are essential for businesses looking to align their data strategy with organizational goals.

Customer data platforms (CDPs) focus on unifying customer data from multiple touchpoints to provide a 360-degree view of each customer. These platforms are particularly important for delivering personalized customer experiences. A CDP will typically gather data from various sources, such as websites, mobile apps, social media, and offline interactions.

CDPs are mostly used to deliver targeting recommendations based on customer preferences and provide a single source of truth for customer behavior trends. CDPs can help your organization improve customer satisfaction by turning data into actionable insights that benefit your customers and drive customer loyalty.

Big data platforms (BDPs) are designed to manage and analyze massive, complex datasets that exceed the capacity of traditional data processing systems, such as clickstream data from millions of users interacting with your website. These platforms specialize in high-performance computing and large-scale data storage — and can process both structured and unstructured data in real time.

Some common use cases for BDPs include:

• Healthcare: Patient data comes in many formats, such as electronic medical records, patient notes, MRI imaging, scan results. Much of this data is unstructured and takes up a lot of storage space. Running analysis on this data can help providers improve patient outcomes and research.
• Finance: Companies can use the insights from a BDP for fraud detection, algorithmic trading, and risk management.

Deciding whether to build a custom data platform or purchase an existing solution depends on your organization’s goals, budget, technical expertise, and timeline. Both options have distinct advantages and challenges.

Building a custom data platform involves creating a tailored solution designed to meet the unique requirements of your organization.

Building your own data platform comes with these advantages:

• Full control over design, architecture, and functionality
• Ability to address specific use cases or industry-specific needs
• Flexibility to scale and adapt as your business evolves
• Custom security and compliant capabilities

However, there are a few challenges as well:

• High upfront investment in time, money, and resources
• Demand for skilled technical teams with expertise in data engineering, architecture, and security
• Longer time-to-market compared to prebuilt solutions

Building a custom platform is ideal for organizations in niche industries or those with unique use cases that off-the-shelf platforms cannot support. For example, a biotech company requiring specialized data integration for genomic research may build a platform to accommodate the complexities of its data workflows.

Purchasing a data platform involves adopting a prebuilt solution from a vendor, offering out-of-the-box features and capabilities.

This helps organizations have:

• Faster implementation and time to value
• Access to tested, reliable features and ongoing updates
• Reduced need for internal technical expertise and maintenance
• Scalability and flexibility provided by cloud-based solutions

This does come with some challenges, such as:

• Limited customization compared to a custom-built platform
• Ongoing costs for subscriptions or licenses
• Dependency on vendor reliability and support

You might find it more effective to purchase if you need an immediate solution or if you don’t have the resources to maintain a custom platform. For example, a retail company aiming to implement real-time customer personalization might purchase a cloud-based data platform like Data Cloud to quickly integrate and use customer insights.

Choosing between building and purchasing depends on your specific goals, technical capabilities, and budget. If you need speed and proven capabilities, purchasing an established solution is often the best choice.

Having a data platform can help your organization manage your data from extraction to analysis. In the Salesforce ecosystem, Data Cloud is central to the management of all your data — structured and unstructured. It unifies your data into a native Customer 360 view, delivers trusted, contextual data to Agentforce, and amplifies your existing data lakes and warehouses to activate real-time insights and intelligent action. With automation tools, data governance, and advanced AI, Data Cloud can help you get the most out of your data.