How the Different Data Platforms Differ
Many things are referred to as a “data platform.” Here’s a handy primer for those who are new to the space.
A data platform is an essential set of tools for any modern business, storing data and analyzing it so that it can be used for effective decision making.
There are many kinds of data platforms, and selecting the right one can be challenging for someone just starting down this path. In this article we will highlight a handful of examples of technologies commonly referred to as data platforms—enterprise data platform, cloud data platform, data analytics platform and customer data platform—explain how they differ from each other and go over the important factors to consider when choosing a data platform for your organization and use case.
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Different Types of Data Platforms
In general, data platforms integrate databases, data warehouses, data lakes, data lakehouses, processing engines, analytics, governance, security and machine learning capabilities to manage and derive business value from large volumes of data. Let’s dive into the examples to explain what each of them does to achieve that goal.
Enterprise Data Platform
Large enterprises often struggle with data scattered across various departments and systems, which makes it difficult to get a holistic view of the business. Not having a centralized data repository creates other challenges, too, such as security and compliance.
Enterprise data platforms (EDPs) are designed to address this complexity. They can integrate vast amounts of structured and unstructured data from many sources (databases, data warehouses, data lakes, business applications, social media and so on) to create a unified framework.
EDPs also come equipped with advanced visualization, reporting, compliance and security tools. For example, Microsoft SQL Server comes with a Power BI integration for creating rich visualizations and dashboards and Windows Firewall to protect the platform. The integrated analytics and reporting tools help organizations make data-driven decisions. For example, they can spot emerging market trends and change business strategy to quickly capture the opportunity.
Designed to scale alongside businesses, EDPs ensure that an organization's data infrastructure can support growth without dragging down performance.
Cloud Data Platform
Speaking of data infrastructure, managing it can be complex and costly, especially for businesses using traditional on-premises solutions. Besides managing the infrastructure itself, the challenges include data security, disaster recovery, global accessibility and scaling.
Cloud data platforms are constructed using cloud compute and data repositories and consumed through a pay-as-you-go model. Fully managed by the cloud providers, they mostly eliminate both the initial deployment and ongoing infrastructure management tasks for customers. This makes them ideal for businesses of all sizes that are looking to optimize data management without incurring significant upfront costs. They are flexible, scale seamlessly and enable global data access.
Finally, teams using cloud data platforms have access to a great variety of data management, analytics and visualization tools offered by cloud providers and their many partners. Different cloud data platforms available for a variety of use cases.
Data Analytics Platform
Typically provided as part of a cloud suite or SaaS, data analytics platforms help derive value from data. They help businesses make informed decisions in order to grow revenue and optimize operations.
Data analytics platforms like Splunk and Tableau focus on extracting meaningful insights from large data sets and come equipped with machine learning and AI capabilities for advanced analysis and predictive capabilities. Such platforms can help businesses capitalize on emerging trends and opportunities.
Real-time analytics, for example, helps businesses react quickly to market changes, while intuitive data visualization tools help users understand and communicate insights. A data analytics platform can analyze customer behavior and preferences, enabling a business to tailor offerings and marketing strategies for better personalization and customer engagement.
Customer Data Platform
Customer data is often scattered across various systems, such as CRMs, email marketing systems, e-commerce platforms and other marketing tools. The fragmentation results in inefficient marketing campaigns and misguided personalization efforts. The introduction of increasingly stringent data privacy regulations, such as GDPR, are putting more and more pressure on businesses to manage customer data responsibly.
Customer data platforms (CDPs) integrate data from disparate sources, breaking down silos and creating a unified, accurate customer database. They provide the detailed insights needed for effective personalization, ensuring that marketing messages and offers that aim to grow revenue from the existing user base are relevant to each customer. CDPs also integrate with existing marketing tools for enhanced campaign management and help businesses comply with data privacy regulations through features like consent management.
Choosing the Right Data Platform
The right data platform can transform how your organization handles data, making processes more efficient, insights more accessible and decision-making more informed. Your specific use case should drive the decision about the type of platform to deploy. If you need to manage and use existing customer data, for example, you need a CDP; if you need to consolidate and integrate data from disparate sources across an enterprise and to get insights from it through AI or other types of advanced analytics, you should be looking at enterprise data platforms; if you want to analyze market data, track campaign performance and improve marketing strategies, look into data platforms designed specifically for marketing analytics and business intelligence.
Beyond the specific use case considerations, you need to think through the underlying infrastructure your data platform will run on and the corresponding business implications. The big factors to consider here are scalability, flexibility and cost.
Scalability
A data platform must be able to handle an increasing volume of data and users without performance loss. Cloud data platforms excel in scalability, while on-premises data platforms are more challenging to scale. If smooth and rapid scaling is a must for your use case, a cloud data platform is the way to go.
Flexibility
It’s important for a data platform to be flexible across multiple dimensions. It should be able to integrate data in different formats, coming from different internal and external sources. It should support different data modeling techniques and transformation features, as well as a variety of analytics and visualization tools.
A platform built on an open architecture provides the most flexibility, because it can be extended beyond its native feature set via integration with third-party tools, libraries and frameworks. For many sizable enterprises, it’s also important that the platform is flexible enough to support a hybrid infrastructure, consisting of cloud and on-premises resources.
Costs
Data platform cost is a complex subject that would merit its own article—or a book! But the key aspects that affect overall cost are the cost of data storage, the cost of accessing data through a network if stored in the cloud (data egress), the cost of owning and operating physical infrastructure if you run an on-prem data platform and the cost of software licenses and/or subscription fees for all the data management and analytics tools you are going to use.
Automation
Automation capabilities are crucial across all types of data platforms. Processes must be automated at every stage, from data ingestion, integration and cleansing through data analysis, visualization and alerting.
There are also use case-specific automation capabilities you should be on the lookout for. A CDP should have automated personalization capabilities, for example, while a marketing data platform should have robust campaign automation tools. Meanwhile, a business intelligence-focused tool should be able to automatically generate reports tailored to a variety of business-user types.
Data Accuracy
Data is only useful if it’s accurate, of course, and data platforms have many features to ensure data accuracy. These are capabilities like data validation, data cleansing, data profiling, version control, data lineage and auditing.
Cloud-based and on-prem data platforms offer a variety of data accuracy features, and it’s important, again, to ensure that the platform you choose supports the ones that are necessary for your use case.
Data Governance
Data governance is a key part of data management, crucial for ensuring data availability, usability, integrity and security. Governance features help organizations with data accuracy, consistency and ethical and compliant handling of sensitive data.
Because EDPs handle vast amounts of data across complex landscapes, they typically feature robust tools for data quality management, compliance, security and access control. While data analytics platforms often include some governance features related to data quality, metadata management and usage tracking, their focus isn’t usually on comprehensive data governance. CDPs are increasingly incorporating data governance features, particularly those addressing privacy laws like GDPR, and consent management and privacy compliance.
Cloud-based data platforms offer strong governance tools, including encryption and access controls. Cloud providers have been improving their governance capabilities in general to meet enterprise demands.
Security
A data platform must safeguard sensitive information from unauthorized access, security breaches and other cyberthreats through measures like encryption (both at rest and in transit), access control and authentication and network security protocols.
Choosing the right data platform and using it effectively can help a business gain market share and improve margins. While cloud data platforms offer the best scalability, flexibility and extensibility, many enterprises have good reasons to keep at least some of their data infrastructure on premises. It is possible to combine the two in a hybrid cloud and leverage the elasticity and the powerful tooling public clouds offer while retaining full control over the data that must remain private.
Equinix Metal provides dedicated cloud infrastructure as a service in about 30 locations around the world (and counting) that can be easily connected to any major public cloud or SaaS provider via private network links. Customers can provision market-leading high-performance storage solutions by Pure Storage and NetApp to store data that must remain private and under their control. They can provision dedicated servers with whatever OS they desire and connect it to their cloud providers directly, bypassing the public internet. They can configure their network to optimize performance and lower data egress costs. All this infrastructure is fully managed, automated, billed as you go and manageable via an API and an intuitive user interface.