Choosing a Data Analytics Platform – What to Consider Before You Commit

Your organization should invest significantly in selecting the best data analytics platform. Making the right tool choice will help you meet consumer expectations while achieving your business objectives.

Before using a data analytics platform in your company, consider a data analytics platform comparison. Several tools with various capabilities, such as predictive analytics and data visualization, are included in a comprehensive data analytics platform. It seeks to transform data into knowledge that might advance your company.


The right size BI platform is essential for various reasons, including performance, security, and availability. A clunky data management system is more likely to lead to costly downtime, poor data quality, and even data loss. For the most part, a well-designed data architecture will provide a unified view of your organization’s critical data. It will allow you to make informed decisions about how to serve your customers best. The key to success is to ensure that all stakeholders are involved in the decision-making process and that they understand the importance of their role in ensuring a successful data science deployment. The best way to do this is to create a multi-disciplinary team, bringing together people with different skills and interests in an environment that promotes collaboration. The resulting collaboration will produce data insights that can be used to optimize operations and drive the bottom line.


The scalability of a data analytics platform can be an important consideration for businesses that want to grow. It shows that a system can handle growth and expand production or customer service.

Scalability is an essential quality for software systems, enabling them to adapt to both precedented and unprecedented increases in workload. It also determines how the database can respond to new users joining or integrating into the system.

It can take many forms, but generally, scalability refers to the ability of an application or network, or algorithm to move to a higher capacity or status when required. It can also refer to the ability of a system or network to adjust its structure and performance to accommodate changes in user demand or market needs.

The two most common scalability models are horizontal scaling (scaling out) and vertical scaling (scaling up). Horizontal scalability allows you to add or remove nodes without affecting the overall performance of your system. However, it increases initial costs and can be difficult to maintain.


Reliability is one of the most important factors when choosing a data analytics platform. This is because it can impact how well your organization performs.

A reliable data analytics platform can deliver accurate results that can be replicated consistently across multiple experiments. This is also referred to as internal consistency.

In addition to reliability, a data analytics platform should be able to resist external factors that could change the outcome of your research. These include changing variables such as the number of participants or their age.

Reliability is usually assessed by calculating the test-retest correlation. This scatterplot shows how similar your results are between the two tests.

Reliability is also a good way to assess how well your research will stand up to time. This is especially important in science because you might need to repeat your studies several times.


A data analytics platform should be flexible enough to accommodate your needs now and into the future. This means it should be able to interoperate with your data warehouse and ETL tools, as well as your database and storage infrastructure.

It should also scale up and grow with your business over time. This is especially true if your organization plans to add new employees or expand its reach.

The right analytics solution should also be easy to use and allow technical and non-technical users to analyze data. The platform should support an interactive SQL editor, a notebook environment for advanced analytics and visualization, and collaboration tools for less tech-savvy users.

The platform should offer a variety of pre-built dashboards and reporting templates. It should also support easy-to-use machine learning algorithms that generate best-practice visualizations and narratives based on data insights.


When choosing a data analytics platform, you should consider the initial and ongoing maintenance costs. These include software upgrades, bug fixes, feature requests, performance optimization as data grows, and fixing data quality issues.

The price of a data analytics system also depends on how much data it can handle and the number of users using it. For example, a company with eight million daily queries would need a large, expensive data analytics platform.

In addition to data storage, a data analytics platform needs tools for processing and exporting data. These tools include APIs, webhooks, and file servers.

After processing the data, a platform will need reports that present all of the information in a form that is easy to understand. These reports can be in charts, diagrams, graphs, and other visuals.