How do you select the best data analysis tools for your business?

To choose the best data analysis tools for your business, consider your data type: structured or unstructured. For structured data, Excel, SQL, or Power BI are suitable, while unstructured data benefits from Python, R, or Tableau. Also, evaluate scalability, ease of use, integration, cost, advanced analytics, security, support, customization, and your team's skillset to tailor tools to your business needs.

The amount of financial and time costs considered Volume and type of data The level of knowledge and mastery of the designer team Ability level of end users The level of training of end users

I completely agree with Paulina, honestly, you must know your data first, we could be offering a huge list of applications etc. Focus on your data, with a combination of budget, see what features will help your company and finally try demos, try as much as you can to see which one is better. then you can move forward with the right tool

Consider factors such as data volume, complexity, and user expertise. Then, research and evaluate data analysis tools on the market, paying attention to features, scalability, integration capabilities, and cost to find the solution that best matches your requirements. Finally, pilot and test the selected tools with real data and user feedback before making a final decision to ensure they effectively meet your business's analytical demands.

Well, the company I work for sell Qlik, so I can show the clients Qlik's magic. ???????????? But it always dépend on budget, #users, requirements and what better fits it all. It is all about value.

Although you might not have large volumes of data to deal with initially, consider the velocity of the data, or how fast data is being generated. Even if you have small data that you typically process using Excel, an increased velocity in the data will amount to large volumes over the medium and long term that will require more sophisticated storage and processing capabilities. A company's needs can be addressed in multiple ways and have implications that require collaboration from multiple and diverse teams across the organization.

In addition, if you have bigger datasets you can implement scripts in R/python/bash to extract key features for downstream analyses and finally scale up going to Snakemake or Nextflow.

Tools like PowerQuery can help process structured and unstructured data, and help keep up with clean and accurate reporting for a specific business unit (BU) or an isolated sales report. When trying to have a 360-degree view of customers, data from multiple BUs need to be stored in a central repository, say a data lake, and joined together in a Cloud Database Management Systems (CDBMS), like Databricks, Snowflake, or AWS, to make it available for data scientists and analysts, who will work together to draw insights from the data. Analysts will create BI dashboards (Tableau, PowerBi, etc.) to uncover and share insights with decision-makers and stakeholders for activation.

Some tools are easier to learn than others. Knowing how to use Excel and PowerPoint can go a long way when pitching ideas to the C-suite. When it comes to backing ideas with data, having advanced analytics capabilities means having a 360-degree view of customers and using big volumes of data to create visually compelling BI dashboards that tell a story that decision-makers can get behind. Tableau and PowerBI are the two big players in the Business Intelligence field, and their price is a clear decision factor for many organizations. In the end, it's more about the data and the individual behind the tool than about the technology. Both tools have great capabilities and require similar, yet not identical skill sets.

I'm surprised that the original article excludes what I believe should be among the top drivers for this decision: access to partners and talent. Even the best tool, assuming one exists, will flop if you lack the people to drive its success. Your access to the market is crucial. Whether you're choosing Qlik, Tableau, Databricks, Fabric, Snowflake, GCP, or even a simple Postgres/Python/ChartJS setup, the decision only becomes challenging should you have equal access to skilled operators or those willing to learn.

For large organizations with varied types of datasets and BI needs, consider making multiple analysis tools available. Analysts should be able to choose the appropriate tool - cost permitting - depending on the need and application. When determining cost, though, don't forget about learning and training costs for new tools.

Lets not forget that to analyse high amount of data first we need a data management plan and enough storing capacity. And to perform analyses we may need higher computacional resources in both RAM and data transfer speed than a normal laptop. This, in many cases, implies relying in a cloud platform like Oracle OCI or AWS or a computing cluster with the added cost of setting, maintaining and performing computing task it will generate.

Every organization is different, but their needs are similar when it comes to data analysis, but their pricing will vary greatly. The best tool to use will be the one that fulfills all needs in the organization and that also comes at a price point that the organization can absorb. In the end, the analysis will only be as good as the data behind it. It is the talented individuals behind those tools that will make the tech stack work, not the technology itself.

The data analysis tool selection process can vary based on business size, industry, and specific requirements: Scenario 1: Large E-commerce Enterprise Tool: Power BI or Tableau or Qlik Reasoning: Any of these tools have powerful visualization capabilities to help analyze vast amounts of customer data, providing actionable insights for strategic decision-making. Scenario 2: Tech Startup with Data Science Focus Tool: Python with Pandas and NumPy Reasoning: The startup leverages the flexibility and advanced analytics capabilities of Python for in-depth data analysis, machine learning, and statistical modeling.