How to Create a Data Analytics Platform Like Tableau

Feb 18, 2022·8 min read

Entrepreneurs need a data analytics platform (DAP) to make data-driven business decisions. If you have an idea for building a DAP, we can guide you through this process.

Contents:

Data analytics is a powerful tool for entrepreneurs who want to make data-driven business decisions. According to a recent Market Research Future report, the data analytics market is expected to grow from $22.9 million in 2019 to $132.9 million in 2026. The market is expanding, so if you want to build a data analytics platform and win some market share, this article is for you. Read on to find out:

  • what a data analytics platform is and how it works
  • what an analytics platform consists of
  • how to build a data analytics platform
  • how much it costs to build analytics software

What is a data analytics platform and how does it work?

A data analytics platform (DAP) is software that gathers and analyzes data to provide valuable insights for users. While data analytics platforms operate through complicated processes, we can simplify the whole analysis process into three main steps: data collection, data processing, and report generation. This is how it looks:

  1. Analytics software collects data from different sources such as databases, APIs, and data lakes
  2. The platform processes the gathered data by transforming, filtering, and grouping it in accordance with set requirements
  3. The platform generates reports using built-in reporting and data visualization tools

This is a very simplified explanation of how a data analytics platform works. In the next section, we elaborate on the components of data analytics software.

What are the components of a data analytics platform?

No matter what industry you build a data analytics platform for, it should include must-have blocks that ensure its proper operation. Let’s have a quick look at them.

data analytics platform blocks

Data ingestion

Before analyzing any data, it’s necessary to get it in one place. To collect data from different sources, you should build a data ingestion block. Some of the methods we can use to ingest different types of data are webhooks, APIs, and file servers.

  • APIs are used to collect either a single object or a batch of objects in JSON or XML format from connected third-party services such as CRMs, ERPs, and social media platforms.
  • Webhooks provide a single data object or some metadata.
  • SFTP servers are used to ingest data in JSON, XML, CSV, and other formats.

Data pipelines

Once the necessary data is gathered, it has to be processed, and that’s what happens in data pipelines. This block of a data analytics platform receives data, transforms it, filters it, groups it, and aggregates operations on it. In data pipelines, data is also saved for future processing and exporting to other services. To organize the proper functioning of data pipelines, it’s necessary to use message brokers and orchestration tools.

Message brokers are architectural designs for validating, transforming, and routing messages between applications or services as parts of an application. For an analytics platform, we can use such message brokers as SQS, RabbitMQ, Apache Kafka, and Amazon Kinesis.

Orchestration tools are used to centralize data pipeline management. They invoke each step at the right stage of the process and connect the data flow between steps, forming a complete picture of data pipeline execution and storing this metadata for future analysis. For a data analytics platform, you can choose Apache Airflow or AWS Step Functions as orchestration tools.

If your analytics platform needs a few simple pipelines, an external message broker and custom orchestration are enough. If it has many pipelines, you’ll have to use external services: both message brokers and orchestration tools. The number of pipelines depends on the complexity of the data processing steps.

Data storage

Storing data is the heart of the analytics system, so data storage should be carefully chosen. In the simplest cases, you can use a general-purpose relational database like PostgreSQL or MySQL. But when you have a lot of data, it’s better to use a solution tailored to your particular use case. Some of the data storage tools we can use for a data analytics platform are Cassandra, Hadoop Hive, and Amazon Redshift.

Reporting

Reports are an essential part of a data analytics platform. When creating reports, tons of raw data is turned into a visually appealing — and, more importantly, understandable — format that allows platform users to make data-driven decisions. Reports in a data analytics platform can be presented in numerous forms such as charts, diagrams, graphs, bubble charts, and other visuals.

Two popular tools for reporting in a data analytics platform are Amazon QuickSight and Tableau.

Amazon QuickSight is used to build visualizations and dashboards as well as to enable machine learning. It supports connectivity with all databases and warehouses that exist as managed solutions on the AWS platform. However, Amazon QuickSight doesn’t offer many visualization options, so if you need a lot of visualizations, you might consider Tableau for this purpose.

Tableau offers a wide range of visualizations that you can implement into your data analytics platform. It also allows you to use extract, transform, and load (ETL) capabilities. However, Tableau is quite costly, and you need to keep the cost in mind when choosing a reporting tool for your platform.

Data export

Just like data ingestion, data export is performed with the help of APIs, webhooks, or file servers. Different tools are used depending on the types and formats of data to be exported. Webhooks paired with APIs are used to pull and push data to and from your system, while file servers can export large amounts of data.

How to build a data analytics platform

To create a data analytics platform from scratch, you should go through several stages of the software development lifecycle (SDLC).

Discovery phase

The discovery phase is the basis for a successful software product. By starting software development with a discovery phase, you can:

  • get a clear understanding of the product you want to build
  • validate your business idea and make sure your product will meet market demand
  • find the best way to implement your idea in terms of technology

The discovery phase usually takes a couple of weeks and requires the participation of a business analyst, a project manager, a UI/UX designer, and a couple of technical specialists (usually a software architect and a tester). These professionals help stakeholders clearly define the product vision, set software development requirements, build a product prototype, and approximately estimate the time and cost to build a data analytics platform.

App architecture and design

Once stakeholders and the software development team have the list of requirements for the data analytics platform, it’s time for a software architect to work on the app’s architecture and logic. A software architect has deep technical knowledge, understands dependencies between technologies, and can offer the best solutions for a particular application.

A UI/UX designer does their part at this stage too. It’s important to understand how your data analytics system will look before development begins. You should pay close attention to your app’s design at this stage and make sure that you like the UI and UX. If you miss some details during the design phase and want to change the UI or UX later, it will take more time and money.

Once the architecture and UI/UX design are ready, development can start.

MVP development

Building a software product, be it a marketplace or a data analytics platform, starts with building a minimum viable product (MVP). An MVP is the first version of your product that includes all must-have features to meet your target users’ basic needs.

An MVP is a basic product whose creation involves backend development, frontend development, and quality assurance tests to provide users with the experience they expect. An MVP allows stakeholders to validate their idea and make sure their product is in demand, has product–market fit, and meets the needs of the target audience. With a public MVP, stakeholders can gather user feedback and find out what needs to be improved to make the analytics platform better.

Maintenance and support

An MVP of a data analytics platform is only the beginning of the software development journey. In fact, software development can be a lifelong process — take Facebook as an example. Although the initial version of Facebook was released in 2004, it continues to be developed. And you definitely don’t want to stop after your MVP release. The world of technology develops fast, and you have to stay on top to keep up with the times and compete in the fast-changing market.

Cost to build an analytics platform from scratch

The cost to build a data analytics system depends on numerous factors. By knowing what they are, you can find out an approximate software development cost.

factors that influence DAP development cost

Tools and technologies

Whatever block or feature you need to build for your data analytics platform, there are dozens of tools software engineers can use to do it. These tools differ in their functionality, scalability, security, and price.

While there are some standard programming languages that software engineers use for frontend and backend development, tools for specific functionality (and their costs) may vary dramatically. For example, at Clockwise Software, we use the React, Vue, and Angular JavaScript libraries for frontend development. For backend development, we choose Node.js as an engine for reliable and powerful applications.

As for the tools to provide data visualizations inside your platform, it’s possible to build the necessary functionality with Amazon QuickSight or Tableau. Depending on the tool you choose, you’ll pay not only for the time developers spend on incorporating visualization functionality but also for the use of a particular service.

Scope of work and deadline

If you build a data analytics system from scratch, you’ll probably need to go through all the stages of the software development lifecycle that an outsourcing IT company can provide. These include a discovery phase, UI/UX design, MVP development, and maintenance and support. Specialists involved in these stages include a business analyst, project manager, UI/UX designer, software architect, software engineer(s), and a QA engineer.

Depending on the number of features your data analytics platform has, its complexity, and your desired time to market, you might need more or fewer software engineers to work on your project. Consequently, the more specialists you need to be involved in the development of your data analytics platform and the more time they need to build your software, the more expensive the development will be.

Development team’s location

This is the major factor that impacts the development cost. Hourly rates in countries that provide IT outsourcing services vary dramatically. For example, rates for Indian software developers start at $15 per hour, while American engineers may charge up to $250 an hour. When choosing an outsourcing company for developing your analytics system, don’t be fooled by costs that are too low or too high. By choosing a software development company with low rates, you risk facing problems such as missed deadlines, miscommunication, and lots of rework. At the same time, paying $200+ per hour doesn’t always guarantee the highest quality.

You might want to find a company in a country with a reasonable price-to-quality ratio. Ukraine is an outsourcing destination with attractive hourly rates and high development quality.

Conclusion

Data analytics will be popular among businesses for many years. Building a data analytics platform is a great opportunity to enter this growing market.

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