What is a Data Platform? Definition and Emerging Technologies
In today’s information age, businesses are burdened with massive amounts of available data. Almost all business activities such as advertising and marketing strategies, sales, accounting, running systems, processes, and the use of infrastructures generate massive amounts of data for businesses.
While businesses have different systems that track their data such as customer relationship management (CRM) software that tracks and integrates all customers’ data, there is a need for an independent system to collect, connect, and harmonize all its existing data.
Organizations need data platforms to properly store their data and make it accessible to other systems and users. Without using data platforms to bring all data together, your company’s data can become an unmanageable monster.
It might be easier to understand what a data platform is, by describing a bit of what it isn’t. A data platform isn’t just a copy of your existing data onto some servers in the cloud. That would be a backup system or a data lake.
Below, we will explore the A-Z of data platforms. What does a data platform mean and what benefits does it bring to your organization? What are the qualities you should look for when choosing one? What are the leading data platforms? What is the future of data platforms?
What is a Data Platform?
A data platform is simply an integrated solution used for end-to-end data processing. It is a platform where data located in various data sources are compiled, accessed, controlled, and delivered to users or data applications in need of it for strategic business purposes.
Typically, data platforms are used to process very large data sets. A true data platform manages a wide variety of data across an enterprise. It does not just consider only the data used for security, privacy, and compliance but also IT data, technical operations data, and every single data set your business generates from its operations.
Recently, modern data platforms have been an important tool used by companies in their data management operations. Companies in all industries make use of data platforms to get information that is usable and profitable to them.
There are also data platforms designed for specific sectors. For instance, there are telecommunication data platforms, customer data platforms, as well as data platforms optimized for healthcare, finance, the public sector, and manufacturing, among many other industries. This makes data platforms all the more beneficial and exploitable by companies who see tremendous value in them.
A data platform gives an organization a chance to properly learn and improve on its activities through comprehensive data analysis.
Benefits of a Data Platform
In contemporary times, data has been increasingly generated and in a wider variety. A lot of companies have seen a lack of solutions to manage the increasing stream of diverse data sets generated from various sources. Data platforms offer companies these solutions and a lot more.
Some of the benefits you could obtain from a data platform include real-time data availability, adequate data governance, data centralization, and data security.
1. Real-Time Data Availability
A data platform is usually connected directly to a company's database or data sources. This means that data is always compiled, processed, and stored in real-time.
Authorized staff in a company always have access to the right and most updated data at any time with a data platform available. This is achieved without any delays in processing that may be caused by large volumes of data or data requests.
2. Adequate Data Governance
A data platform allows businesses to strategically and properly manage how their data is handled. Specifications can be easily made about what data sets are collected, who has access, and compliance with data protection and privacy regulations, such as GDPR.
3. Data Centralization
The most apparent benefit of a data platform is the centralization of data collected from different sources on a single platform. Data from multiple sources are brought together, making companies achieve more streamlined data management activities and effective decision-making.
Data from these multiple sources are collectively processed by a data platform to provide more accurate key performance metrics (KPI) like customer acquisition cost (CAC), customer lifetime value (CLV), and click-to-purchase rate, among others.
4. Data Security
A top-tier data platform offers you an adequate and modern data security framework. All your data is stored on a centralized platform with a single point of access. You easily control who has access to the data stored in your data platform.
Asides from all these, data platforms also provide scheduling dashboards, reports, proactive alerts, and eliminate unnecessary solutions for strategic data sourcing, document tracking, and performance metrics administration.
Types of Data Platforms
The types of data platforms are generally differentiated and determined by their structure and the purpose they are used for. They include enterprise data platforms (EDP), modern data platforms, cloud data platforms, big data analytics platforms, and customer data platforms.
1. Enterprise Data Platform (EDP)
An Enterprise Data Platform (EDP) is the most basic type of data platform that provides an organization centralized access to its data assets. As business administrators wish to exercise control over enterprise data, EDPs typically exist on-premise or in a hybrid environment.
EDPs also make use of more common data sources and processes. For instance, an EDP may have a structure that includes Online Transaction Processing (OLTP) databases, data warehouses, and a data lake. EDPs also make use of basic tools and processes for data acquisition, preparation, and analytical reporting.
2. Modern Data Platform
A modern data platform is an evolved or advanced form of an enterprise data platform. With modern data platforms, you enjoy a broader and more flexible set of capabilities in addition to what EDPs offer.
A lot of companies have had difficulties dealing with the newer and more diverse data generated in contemporary times. Modern data platforms were born out of this necessity and have solutions that are sufficient enough for these newer varieties of data sets.
With this, for instance, a modern data platform enables continual individual data processing in real-time coupled with the more traditional batch processing offered by EDPs.
You also enjoy the processing of structured, semi-structured, and even unstructured data at a massive scale as well as the performance of complex operations like Natural Language Processing (NLP) and AI/ML application development.
Modern Data Platforms typically, but not always, use cloud technologies. This provides businesses with more affordable cost models, scalability, and flexible service management.
3. Cloud Data Platform
A cloud data platform includes every type of data platform built and optimized fully with cloud computing technologies and data stores. It offers companies a whole lot of solutions and advantages over every other type of alternative data platform.
Companies may enjoy unlimited cloud object storage, SaaS services for managed relational and NoSQL databases, and simultaneous processing through MPP data warehouses. Spark clusters, Analytics Notebooks, message queues, and middleware are also elements of a cloud data platform.
Cloud computing capabilities are part of the most popular and extra solutions which modern data platforms implement for data processing.
Coupled with traditional enterprise data platforms and other advanced solutions for Enterprise Resource Planning (ERP), supply chain management, Customer Relationship Management (CRM), and finance data stores, modern data platforms could be upgraded with the integration of cloud computing capabilities.
One of the most influential impacts of cloud data platforms is the presence of SaaS providers. Cloud data platforms make use of relational and non-relational databases entirely managed as a service. These activities include the management and maintenance of software, infrastructure, patching, high availability, scalability, and backup.
Companies can store and enjoy access to huge volumes of very diverse data sets and leave the management and processing of these data sets to providers.
4. Big Data Analytics Platform
Also called a data analytics platform, a big data analytics platform is one specifically optimized or designed for data analytics purposes.
Data analytics platforms are typically integrated into enterprise, modern, or customer data platforms. Functionalities also go beyond just running traditional SQL operations on structured data like EDPs.
The information generated from data analytics platforms is used in conjunction with operational data generated from these other types of data platforms.
A data analytics platform combines services and features that enable you to execute complex analytics processes and queries on massive amounts of dynamic data sets. These combined data sets are analyzed and given meaningful contexts that help businesses easily make accurate decisions with them.
With data analytics platforms, a wide variety of big data tools and utilities are centered in one place and often provide businesses with seamless scalability, availability, security, and performance.
Data analytics platforms are also often offered as Data-as-a-Service (DaaS), forming part of a cloud suite or a SaaS solution.
5. Customer Data Platform
A Customer Data Platform (CDP) is concerned with customer-related data. Unlike traditional CRMs that make use of only data you provide them with, a customer data platform provides more of a “360-degree customer view”. These data platforms are optimized to compile, store, and process both the customer data you have and customer data you do not have from multiple sources.
With a customer data platform, data is sourced from multiple streams including CRMs, social media, emails, websites, digital ads, eCommerce platforms, and others.
The aggregated data from these multiple sources provides companies with a complete user profile, more complete than the ” 360° customer view” which CRMs claim to give. This complete customer data can then be used for effective and accurate promotional campaigns and marketing.
Modern Data Architecture: Elements of a Data Platform
A Modern Data Architecture (MDA) specifies how data is collected, cleansed, stored, processed, and made available for companies to use. It consists of various characteristic elements common to modern data platforms.
Some of the characteristic elements that make up a modern data architecture include extended user power, hybrid architecture, storage of diverse data sets, scalable data pipelines, expandable architecture, automation, and single-layer security.
1. Extended User Power
A modern data architecture places end users on a high pedestal and gives you extended capabilities in your data management activities.
Rather than being limited to the management of a predetermined data set from limited sources, you get to provide your own data, data sources, and data processing channel.
2. Hybrid Architecture
Modern data platforms provide you with a hybrid architecture: an on-premise system that gives you total and dedicated control over your data and a cloud system that provides you with scalability, flexibility, higher processing power, high availability, and security.
3. Storage Of Diverse Data Sets
One major characteristic of a modern data platform is a virtual data storage medium that handles and processes data in diverse formats and workloads.
A modern data platform supports a wide range of data storage formats for operational databases, data lakes, and data warehouses. With these, immediate real-time interactions, processing of unstructured data, and processing of structured datasets required for analytics are well taken care of.
4. Scalable Data Pipelines
Modern data platforms are flexible as their data architecture makes use of scalable pipelines. These pipelines are optimized to effectively handle the ever-changing data processing scene; from batch ingestion of data from traditional sources to stream processing of individual data elements for real-time and high-velocity information gathering.
5. Expandable Architecture
A modern data platform is characterized by an architecture frequently based on open-source technology. Users can utilize the extendable architecture to build their data applications seamlessly from their personal standard set of compatible components.
The extensible architecture of a modern data platform also helps companies with effective data analytics. Users get to develop personal dashboards and reports based on flexible technologies. Existing and compatible analytics applications that are preferred by companies are integrated into the data platform for a more streamlined workflow.
With this, modern data platforms also enable the development of future-based applications encompassing AI and ML logic.
Modern data architecture is also characterized by automation. For data platforms, automation is important for a couple of purposes such as easy infrastructure management and seamless data management activities.
Your data architecture elements like servers, backups, storage, and load balancers are easily created and compiled. Standard templates also guide this automation process in the creation of important data pipelines, workspaces, and functions.
7. Single-Layer Security
Another characteristic element of a modern data architecture is the single security layer for stronger data security. An enterprise-wide identity provider or a role-based authorization process can be implemented to dictate access to stored data. Data is also protected through compliance with regulatory standards.
How to Choose the Right Data Platform?
With a lot of data platforms in the market today, choosing the right data platform for your business remains a very difficult task. However, there are few considerations you can take to put yourself on the right track.
1. Evaluate Your Personal Needs
Every company has peculiar business needs, peculiar resources to spare, and exists in peculiar business settings. Taking note of important factors helps you determine the right data platform for your company.
Before choosing a data platform, consider the specific security and compliance requirements in your industry, costs of software licensing models, and the functions you want to maintain in your data structure, among others.
Determining all these help you know if your data is best managed on-site, through a cloud provider, or a combination of both. You also have an idea of which data platform has enough features and solutions to handle your particular needs and data structure.
2. Scalability And Flexibility Are Important
For growing companies, scalability is always an important element to consider while running a business. Ensure the data platform you choose can meet up with modern data processing needs and is adaptable to the growth of your stored data. Stored data will always grow and you want to use a data platform that offers you the right level of scalability and flexibility.
The growing popularity of cloud-based data platforms is owed to their seamless capabilities in scaling up or scaling down data architecture as needs change.
A data platform with a flexible data architecture is important. A chosen data platform should be able to serve multiple uses, be characterized by a robust ecosystem of applications, and accommodate the addition of new functions.
3. Skill Requirement
Managing data, especially when data is managed to achieve a particular goal, is not the easiest task to undertake. However, while a lot of top data platforms help make this task a little less difficult, others still keep the data management process as tasking as ever.
With this, you must consider the amount of technical ability required to operate the data platform.
Consider how easy it is to deploy and configure data platform architecture elements and the learning curve required. Does the data still need to be analyzed by an employee in your organization before they are actionable? These are important considerations to make to avoid putting your data management activities on hold.
4. Security And Compliance
With business data, security is very important. Data platforms make use of both your internal company data and customer data, so making sure that these are kept away from unauthorized external access is important. Data breaches put your company and customers at risk.
Ensure that the data platform you choose has a robust, built-in security framework or tools that can be integrated into your existing security solutions. One way of identifying a data platform with a sufficient data security framework is if it has compliance certificates.
Commonly, regulatory bodies in a country or within an industry require that a data platform adheres to frameworks and guidelines. ISO and HIPAA certifications are compliance certificates to look out for.
5. Look Out For Automations
Manually analyzing a large amount of data is an almost impossible task, even for the most dedicated analysts. Data platforms help with data storage and processing, but automated data processing is always a better option.
A lot of data platforms have implemented advanced machine learning (ML) and artificial intelligence (AI) technologies. With automation, you have easy access to data-driven insights with minimal work from your end. Look out for data platforms that offer you the automatic services you need for your data management needs.
Leading Data Platforms
Best Data Platform with the Most Comprehensive Lists of Analytics Solutions
Looker is a data platform that exploits its partnership with Google Cloud’s data analytics platform to provide you with advanced analytics and insights into your data. Its modern Business Intelligence (BI) and analytics feature serve you dashboards with real-time data processing. This gives you a more in-depth, continual, and updated analysis of your data for more precise data reporting.
With Looker, you can increase your toolset by integrating more data sources and external platforms into your infrastructure. You get to manage new and relevant data and also unify your teams across various platforms for more effective and collaborative data-informed decisions.
Looker also allows you to create and integrate custom applications and processes into your data platform architecture. You can meet the unique needs of your business and have access to analytics solutions optimized for different uses, from retail to healthcare to other industries.
Leading Data Platform with Integrations for Extended Scalability
Splunk is a Data-to-Everything platform. It gives you every tool you need for all your data processing needs in the form of purpose-built solutions driven by Artificial Intelligence (AI) and machine learning.
With Splunk, you enjoy real-time data processing solutions for the prediction, identification, and solution of any problems within your business. You also enjoy a dashboard that gives you seamless investigative and collaborative capabilities as well as intuitive visualizations of your stored data.
Splunk is also one of the most flexible data platforms in the market. Its various purpose-built solutions scale as your data size and organization grow. Alongside its wide range of partners and services, the data platform provides you with long-term solutions for your long-term data processing needs.
Best Data Platform with Great Focus on Data Security
Snowflake is a SaaS and cloud data platform that provides you with secure, seamless, and governed access to your data. Your data is seamlessly stored and managed across various public cloud servers.
Snowflake serves as a sufficient platform for every type of data user, from data consumers to data providers to service providers that rely on data.
As a modern data platform, Snowflake allows you to unify your data warehouses, data lakes, and other data sources. Its architecture and framework also comply with various data privacy regulations such as GDPR and CCPA.
You benefit from a wide range of built-in cloud data security solutions and measures. These include continual encryption of data at all times (when it is being transferred and stored).
Alongside its various certifications from different regulatory bodies, Snowflake is also FedRAMP Moderate authorized.
Best Open-Source Data Platform with Very Flexible Architecture
Pivotal is now a part of the VMware Tanzu host of products and serves as an open-source data platform presenting you with advanced science and machine learning capabilities to process your data. These capabilities are provided to you through SQL on Apache Hadoop® and distributed in-memory processing features.
Pivotal is a completely open-source platform that gives you the agility and rapid application scaling capabilities you need for your ever-growing data structure.
Best Data Platform for Managing Extremely Large Amounts of Data
Cloudera is a multi-function cloud data platform that provides you with enterprise-level data security and governance capabilities.
Also implementing Apache’s Hadoop feature, Cloudera helps you handle massive amounts of data of up to 50 petabytes. Cloudera’s DataFlow feature, formally called Hortonworks’ DataFlow, helps you analyze and prioritize data in real-time.
6. SAP HANA
Leading Data Platform with Extended Cloud-Data Processing Features
SAP HANA offers you a wide range of cloud-data processing solutions to properly manage all your company data. You have access to a database and data management capabilities that allow you to manage data from all sources, gain real-time insight, and run custom applications.
The SAP Data Intelligence Cloud feature serves as a comprehensive data management solution that connects and transforms your disorganized data assets into actionable business insights.
With its SAP Data Warehouse and Analytics solutions, you get access to advanced analytics. You enjoy all-in-one analytics capabilities implemented through business intelligence (BI), planning, and predictive technology.
What is the Future of Data Platforms?
With the continual development of new technology as time goes by, data platforms are expected to handle data sets of greater variety and volume. This is, perhaps, the main determinant of how the future of data platforms plays out.
New technologies are characterized by more data in more formats and data platforms will have to evolve as these technologies emerge.
Although this is already materializing, data platforms also have to increase the integration of machine learning and artificial intelligence into their architecture. These are the major technologies that seem to define future developments.
Therefore, going with the trend as soon as possible allows data platforms to proactively assist organizations with their data-related goals. AI and machine learning allow companies to easily implement data for enhanced effectiveness and productivity.
Data platforms have to do all these while ensuring that a wide range of users; from data scientists to business managers can bring and make use of real-time data to make important decisions.
An inevitable requirement for data platforms in the future is to give users the solutions needed to comprehensively investigate, monitor, analyze data, and make effective decisions based on generated insights.
Data Platform FAQ
A data platform is an integrated technology solution that allows data sets located in a database to be collected, governed, accessed, and delivered to end-users, data applications, or other technologies for strategic purposes from a centralized hub.
Big data was created in the 90s when data generated by humans started to grow exponentially. The rate of growth in the volume of data generated has gotten to the extent where all data is now technically big data.
Big data platform refers to IT platforms that combine the capabilities of many big data tools and applications into a single solution. It provides resources for effective data management and warehousing while reducing data latency.
What differentiates a big data platform from a traditional data platform is not just the ability to process large volume data. Big data platforms are also able to deal with a wider variety of generated data at a much higher velocity. Traditional data platforms are very much outdated and businesses that still make use of them are bound to miss out on lots of data analytics features and potentials.
The advantages of an enterprise data platform center around a combination of features that replace point solutions previously used to provide data services. It can specify, incorporate, retrieve, and store data for both internal applications and external communications to increase productivity and make smart strategic business decisions way faster.
An enterprise data platform provides a centralized point of access to data sources by breaking down data silos so that users can work from an integrated data hub efficiently. It provides real-time access to critical enterprise-wide information to allow analysis and informed decision-making.
Enterprise data platforms consist of the underlying database engines like Hadoop that can process data that scale up to petabytes. It also consists of a data assembly framework that enables data engineers from IT and other areas of the business to create data sets for business consumption.
Another advantage of the enterprise data platform is that it possesses the ability to support semi-structured and unstructured data using its built-in JSON capability.
Enterprise data platforms are also able to power business users with trusted one-click insights. Once the platform is installed, the data can be more easily used to get application-independent reporting, enterprise analytics, and integrate external data.
To understand modern data architecture, you first have to understand modern data architecture. Data architecture is a set of rules, policies, and models that determine what kind of data gets captured and how it is used, processed, and stored within a database system.
The traditional goal of data architecture was to build a data warehouse but modern data architecture is much more than that.