Building a Data Analytics Platform: A Comprehensive Guide

Building a data analytics platform is what helps businesses make data-driven, informed decisions. The Market Research Future report states that the data analytics market is expected to grow as high as $303.4 billion in 2030, and its influence on businesses will only increase in the future.

In this article, you’ll find out more about building analytics software, its main components, and how to actually build a data analytics platform to increase your business efficiency.

data analytics platform

What is a Data Analytics Platform, and How Does It Work?

A data analytics platform (DAP) is designed to gather and analyze data, providing valuable insights to users. The process of data analysis can be divided into three main steps: data collection, data processing, and report generation.

Here’s a concise overview of how a data analytics platform operates:

1. Data Collection: The DAP acquires data from various sources such as databases, APIs, or data lakes.
2. Data Processing: The platform processes this data by transforming, filtering, and organizing it according to predefined criteria.
3. Report Generation: The DAP then produces reports using built-in reporting and data visualization tools.

Key Components of a Data Analytics Platform

The features of a data analytics platform can vary depending on its application, but there are several essential components that should be included:

1. Data Ingestion

   To analyze data, it must first be gathered and centralized. This involves setting up a data ingestion system to collect information from diverse sources. Common methods for data ingestion include:

   • APIs: These can pull single objects or batches of data in formats like JSON or XML from third-party sources such as CRMs, ERPs, or social media platforms.
   • Webhooks: These deliver individual data objects or metadata.
   • SFTP: This method handles data in various formats including JSON, XML, and CSV.

2. Data Pipelines

   After collecting data, it must be processed using data pipelines. These pipelines handle the tasks of transforming, filtering, grouping, and aggregating data. They also manage data storage for future use and transfer to other services. To ensure smooth operation, data pipelines typically use message brokers and orchestration tools.

3. Data Storage

   Proper data storage is critical for a data analytics platform. While generic databases like PostgreSQL or MySQL can be used, specialized storage solutions such as Cassandra or Amazon Redshift may offer better performance tailored to specific use cases.

4. Reporting

   A data analytics platform presents data through various reporting formats such as charts, graphs, and diagrams. Tools like Amazon QuickSight and Tableau are popular for generating these reports.

5. Data Export

   Similar to data ingestion, exporting data from a data analytics platform can be done via APIs, webhooks, or file servers. Depending on the data’s format and volume, the appropriate tool can be selected for exporting. Webhooks and APIs handle data transfers to and from the DAP, while file servers manage large data volumes.

How to Build a Data Analytics Platform

Creating your own data analytics platform involves several stages in the software development lifecycle:

1. Discovery Phase

   This phase, typically lasting a few weeks, includes a business analyst, project manager, UI/UX designer, and technical experts such as software architects and testers. During this phase, stakeholders clarify the product vision, set development requirements, create a prototype, and estimate the timeline and budget for the DAP.

   Key outcomes of the discovery phase include:

   • A clear understanding of the product requirements.
   • Validation of the business idea and alignment with market demand.
   • A defined technological approach for implementation.

2. App Architecture and Design

   With requirements in place, a software architect designs the logic and structure of the data analytics platform. Concurrently, a UI/UX designer develops the platform’s visual design. Once these elements are finalized, development can commence.

3. MVP Development

   The next step is to create a minimum viable product (MVP), which represents the initial version of the data analytics platform. The MVP includes core features that address the primary needs of your target users. Developing an MVP involves both backend and frontend development, along with QA testing to ensure a high-quality user experience. The MVP allows for feedback collection and improvement of the DAP.

4. Maintenance and Support

   After deploying the MVP and addressing any issues, ongoing maintenance and support are crucial. This involves responding to user feedback, fixing bugs, and providing post-launch support to address any questions or problems that arise.

Bottom Line

Building a data analytics platform enables your organization to effectively store, process, and analyze large volumes of data, leading to optimized business processes and increased efficiency. When developing a DAP, it’s important to assess the market, clearly define your objectives, and focus on user-friendliness to ensure the platform meets user needs and is easy to use.