What is a data platform? 

A data platform is a centralized, end-to-end system that collects, processes, stores, and analyzes data from multiple sources. It can include software and hardware components and is designed to help businesses make better decisions to improve their operations.

This guide outlines the functions of a data platform and examines how companies can benefit from it.

Data platforms vs data warehouses 

Data platforms are often confused with data warehouses. While they are related, they are not the same. The main difference between a data platform and a data warehouse is the function they perform. 

Data platform:‍

• Enables the storage, processing, and analysis of data
• Typically supports multiple use cases, such as real-time analytics and machine learning
• Focuses on optimizing query performance for business intelligence

Data warehouse

• Typically supports multiple use cases, such as real-time analytics and machine learning
• Is specifically designed with structuring data storage and historical analysis in mind
• Handles diverse data types and workflows

How data platforms can support business functions

A data platform can support various business functions as it enables the real-time processing of reliable data and comprehensive analysis of it. Let’s explore how.

Predictive inventory management

Retailers like Walmart, Amazon, and Target use data platforms to analyze sales trends, manage inventory, and predict demand.

It helps them forecast stock levels for perishable goods, which reduces wastage and makes sure products and produce are available.

Personalized customer experiences

Most e-commerce platforms use their customers’ data to provide them with more personalized recommendations.

Netflix and Spotify, for instance, analyze viewing and listening history to suggest relevant shows, songs, or podcasts customers might like.

Fraud detection

All major banks and financial institutions use data platforms to identify suspicious transactions in real time. This allows them to spot unusual purchases or transactions and flag or block them instantly.

Operational efficiency

In the aviation and manufacturing industries, many companies monitor the performance of various pieces of equipment and machinery through the Internet of Things (IoT)-connected data platforms. 

Airlines like Air France, Lufthansa, and United Airlines can notably reduce downtime and safety risks by using predictive maintenance alerts to detect potential equipment failures before they happen.‍

How exactly does a data platform work?

Data platforms are typically powered by five core components.

1. Data storage and processing

Data platforms have to store huge amounts of structured data, semi-structured data, and unstructured data. That is why many companies use cloud-based storage solutions (such as data lakes and data warehouses) to scale their storage needs more dynamically.

Thanks to processing engines like Apache Spark and Snowflake, data platforms are able to handle large datasets efficiently, which creates a much faster response to a query, generating more accurate analytics in real time.

2. Data ingestion

Data ingestion is the process of collecting and importing data from multiple sources, such as databases, IoT devices, and Application Programming Interfaces (APIs).

In the case of streaming data, this can happen in real time. It can also happen in batches through scheduled imports.

Using tools like Apache Kafka or Amazon Kinesis can help businesses create a seamless data flow with minimal latency.

3. Data transformation and modeling

Before it can be analyzed, raw data needs to be cleaned, structured, and formatted. This is why ETL (extract, transform, load) or ELT (extract, load, transform) processes are used. They standardize data and guarantee it is consistent across the entire platform.

Data modeling defines the relationships between these datasets to make it easier for businesses to derive insights. Subsequently, it is a critical step in certifying the accuracy of analytics and reports.

4. Business Intelligence (BI) and analytics

Once data is processed and structured, business intelligence (BI) tools help to turn it into actionable insights. This allows businesses to do things like visualize trends, generate reports, and make data-driven decisions. 

In addition, AI-powered analytics further improve its predictive capabilities by helping companies to optimize their operations.

5. Data observability

Data observability is responsible for ensuring the data’s quality, reliability, and compliance are up to scratch.

Data observability primarily involves monitoring data pipelines, detecting anomalies, and addressing inconsistencies. It can also take the form of automated alerts that advise teams of issues that must be resolved before business operations are affected.

As you can imagine, this is especially important for industries like finance and healthcare that rely on real-time decision-making.

Additional platform layers

Along with these five core components, there are a number of other layers to data platform architecture.

• Security and compliance: Ensures data privacy and encryption, as well as adherence to regulations like GDPR and HIPAA.
• Governance and access control: Defines user roles, permissions, and policies for secure data management.
• Scalability and performance optimization: It adapts to growing data needs with cloud-based and distributed computing solutions.
• Integration and APIs: Enable a seamless connection with external tools, databases, and third-party applications.

What are the different types of data platforms? 

Data platforms come in various forms, depending on how they are deployed and their specific functions. We’ve organized some of the key types into two main sections: deployment-based platforms and function or purpose-based platforms.

Deployment-based platforms

1. Cloud data platforms

These platforms are hosted on cloud infrastructure and offer scalability, cost efficiency, and remote accessibility without the need for on-premises hardware.

2. On-premises data platforms

As their name suggests, on-premises data platforms are deployed within a company’s own servers.

While they provide full control over security and performance, they do require significant maintenance and investment in infrastructure.

3. Hybrid data platforms

A hybrid data platform combines both cloud and on-premises capabilities. This offers businesses the flexibility to assert both local data control and cloud scalability within their operations.

4. Colocation data platforms

Colocation involves placing privately owned servers in third-party data centers. This model allows businesses to maintain control over their hardware while benefiting from external infrastructure, such as enhanced security, power redundancy, and cooling. Colocation can be considered a form of hybrid solution, as it blends on-premises control with outsourced data center facilities.

Function/purpose-based platforms

1. Data integration platforms

These platforms ensure that the data movement between various systems and formats is seamless. Some examples include Talend and Apache Nifi.

2. Data analytics platforms

Through data analytics platforms such as Google BigQuery and Snowflake, businesses are able to analyze large datasets and generate astute insights. 

3. Customer data platforms (CDPs)

CDPs can centralize customer data from a number of different touchpoints to create unified customer profiles that can be applied to marketing and personalization.

4. Data management platforms (DMPs)

These types of platforms are primarily used in digital advertising to collect, analyze, and segment audience data for targeted marketing campaigns.

5. Data discovery platforms

Often featuring AI-driven advanced analytics tools, data discovery platforms such as Tableau and Looker help users explore and visualize data for insights and reporting.

Six benefits of data platforms

Businesses that adopt a data platform system to collect, store, process, and analyze large amounts of data can benefit from it in six key ways.

‍1. Better decision-making

Real-time analytics and AI-driven insights help businesses make data-driven decisions more quickly and accurately.

2. More scalable and flexible 

Cloud-based platforms can adapt to a company’s growing data needs, which enables them to enjoy efficient performance without the need for costly infrastructure upgrades.

3. Improved data quality and governance

Through automating their processes, businesses can ensure their data is accurate, consistent, and compliant with regulations like GDPR.

4. Better customer experiences

By having access to centralized data, companies can personalize their interactions with their customers to improve their levels of engagement and satisfaction.

5. Operational efficiency

As they are able to streamline their workflows, organizations can reduce their need for manual data handling, saving time and minimizing errors. 

6. Stronger security and compliance

Thanks to features like advanced encryption, access controls, and monitoring tools, companies are able to better protect their sensitive data from breaches.

A step-by-step guide to implementing a data platform

If you want to set up a data platform for your company, you’ll need a plan of action and the right technology stack. Here’s a step-by-step process of what this involves. 

Step 1: Define your data strategy

Before selecting a data platform tool, it’s important to understand your business goals and data needs clearly.

Consider what types of data you will collect (structured, unstructured, real-time, or batch, for example) and how the data will be used – for example, for analytics, machine learning, or business intelligence.

In addition, make sure you’re clear on what kind of compliance and security requirements you will need to meet.

Step 2: Choose the right type of data platform

Select the data platform that best meets your needs. That might be a cloud-based platform, an on-premises solution, or a hybrid platform. Base your decision on a combination of factors (including pricing, capabilities, and ease of integration), and make sure you do your due diligence in researching your options.

Step 3: Plan the data ingestion and integration

It is important that your organization has a seamless data flow to work with. Take the time to identify the right data sources for you, such as databases, APIs, and IoT devices.

Additionally, select a relevant ingestion method, which might be real-time streaming or batch processing, and use integration tools like Apache Kafka, Talend, or AWS Glue to connect to different systems.

This is an important step to get right. Proper integration will prevent the threat of data silos and ensure you have consistency across the platform as a whole.

Step 4: Set up data storage and processing

When it comes to setting up data storage and processing, it is essential to choose the right storage data architecture.

This might be data lakes for raw, unstructured data, data warehousing for structured, optimized analytics, or a hybrid solution that provides you with flexibility by combining both.

Processing engines like Apache Spark or Snowflake can help your company handle large-scale data more efficiently.

Step 5: Set up data governance and security

The next stage is to ensure the integrity of your data is safeguarded and compliant. This process should involve defining user roles and access controls, implementing encryption and security policies, and using monitoring tools to detect anomalies and potential breaches.

Step 6: Deploy analytics and business intelligence tools

The whole point of a data platform is to help your business make better data-driven decisions.

Therefore, integrating BI tools like Tableau, Power BI, or Looker into yours can ensure you make better predictive insights. It will also automate the process of reporting through generative AI-driven analytics.

Step 7: Monitor, optimize, and scale

This stage should be ongoing as it’s important to regularly track how the platform is performing. When you can, take the opportunity to optimize the data pipelines and scale your storage as your business grows.

Summing up

Data platforms are strategic tools that businesses use to handle vast and complex sets of data. They can help your team analyze, evaluate, and monitor data, providing insights to help with decision-making. 

We can help you maximize the potential of your system’s capabilities. Contact RecordPoint today to learn more.

FAQs

What is a modern data platform?

A modern data platform is a scalable system that helps businesses store data, process data, and analyze it efficiently. It enhances data accessibility and supports real-time decision-making.

How does an enterprise data platform improve security?

An enterprise data platform provides unified security by enforcing encryption, access controls, and compliance measures to protect sensitive data.

How can I make the most of a data platform?

To maximize the value of a data platform, explore resources from providers like RecordPoint, which offer guides on leveraging platforms for improved insights, workflow optimization, and enhanced security. These guides often cover best practices for data integration and management.