- Which data and analytic tools are the best?
- What is the best BI solution: Tableau, Power BI, QlikSense, Looker,…?
- What is the best data architecture: DWH, data lake, data mesh,…?
- What is the best data analytics tool: Knime, SPSS R, Python,…?
- What is the best database solution: Postgres, Mysql, MS SQL, Oracle, …?
- What is the best…
I hear these questions a lot. People often spend a lot of time and effort trying to choose the best tool or technology, but this can be a mistake. It’s true that like buying a car, choosing the right tool or technology is an important decision and should be done wisely. But it’s important to understand the difference between “right” and “best.” The right tool or technology is the one that meets your specific needs and requirements, while the best tool or technology is the one that is generally considered the best by the industry or the market. The two are not the same thing.
Let’s look into two different cases:
The student wants to become a data analyst and is choosing which analysis tool to learn
The scope of data and analytics tools is vast, ranging from Excel to Python and from no-code to pure programming. The importance of choosing the right tool cannot be overstated, as the skills you learn will directly shape your career and the opportunities available to you. For example, proficiency in Excel is likely to lead to job opportunities in the banking industry, while proficiency in Python could open the door to a job at a tech startup. So, the question is, which tool is the best in terms of guaranteeing a good job and good pay?
This question is quite wrong. The first problem with choosing a data and analytics tool is that technology, tools, and methods are constantly changing. When I started my career in analytics, many of the tools that are commonly used today didn’t even exist. For example, just five years ago, Hadoop was all the rage, but today, it’s hardly mentioned.
The second problem is that if you base your choice of a tool on the tool itself, rather than the problems you want to solve, you may end up limiting your options. For example, if you choose to learn Python, you are likely to end up working on algorithmic automation problems like machine learning and data pipelines. But it’s unlikely that you would end up doing data visualization with Python.
So, what can we tell a young student who is struggling to choose what to learn?
First, it’s important to find out what drives and interests you, so don’t be afraid to experiment. Start by choosing the problems you want to solve and let that guide your choice of tools. Don’t go too deep into just one tool; try a few different things and see what you like and what feels right for you. Additionally, it’s a good idea to research the companies you would like to work for and see what technical stack they are using. This can help you make an informed decision about which tools to learn.
A company is choosing the BI tool
A company was struggling with reporting, using a lot of Excel spreadsheets and dealing with slow and inaccurate data that was negatively impacting their business decisions. A vendor came in and sold them on the idea that a BI tool would solve all their problems, providing good data quality, automated real-time data, and vastly improved decision-making. The company bought into this idea and started a tender process to select the best BI tool. They included all the major platforms, such as Power BI, Tableau, and Qlik Sense, in their scope.
After four months of meetings, demos, and committees, they chose Qlik Sense as the best tool. They signed a deal with the vendor, set up the necessary environments, implemented one dashboard, and provided training to their internal analysts. The BI tool selection process followed all the best practices and was considered a success.
But a year later, people across the organization were still using Excel. What went wrong? Is it possible that the company made a bad decision in choosing the tool? More likely that they falsely believed that a BI tool could solve all their problems. In reality, their data quality issues were coming from their ERP system, the slow data was due to an inefficient accounting process, and the different versions of the truth were a result of different teams producing the same reports. Additionally, the business users were happy using Excel and may not have seen the value in the new BI tool.
In this case, any tool choice would have resulted in the same outcome. The company focused on choosing the BEST tool, but they should have started with data management practices instead. A BI tool can only be effective if the data it’s working with is clean and accurate, and if the people using it are trained and engaged.
Summary
In conclusion, choosing the best data and analytics tools, technologies, and solutions can be a challenging task, which sometimes is not needed at all. It’s important to understand that the right tool or technology for you may not be the best tool or technology overall, as the best tool or technology will depend on your specific needs and requirements. Additionally, it’s important to understand that a tool or technology alone cannot solve all your problems. Data management practices, training, and user engagement are also critical factors in the success of a BI solution or data analytics project. It’s worth taking the time to carefully research and consider your options, and to seek expert advice if necessary, in order to make an informed decision that will help you achieve your goals.