When all you have is a hammer, everything looks like a nail: choosing the right tool for the job | Cardinal Path Blog
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When all you have is a hammer, everything looks like a nail: choosing the right tool for the job

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A few weeks ago, a coworker asked me for some help with a data cleaning task. I consider him to be one of the best Tableau users in our office, and someone I frequent when I get stuck on any Tableau problem. I, on the other hand, am much more comfortable solving problems in the statistical programming language R.

He described what he needed help with: changing underscores to hyphens, changing uppercase to lowercase, and mapping ‘messy’ values with typos to the ‘correct’ values. I started by visualizing the code that I would write for this using R, starting from the functions I would typically use, to the order of the logic. I then asked him what tool he had used to clean the data previously. He answered, “Tableau.”

When all you have is a hammer, everything looks like a nail. His preferred hammer is Tableau, my preferred hammer is R. In this case, the data cleaning was a sort of a one-off task, so the tools were pretty interchangeable. I have another coworker who I’m sure would use Analytics Canvas to solve such a problem, others that would use Excel- there are a plethora of different tools available to choose from.

However, there are many situations where one tool wins as the ‘right’ tool for a job, and using the right tool can lead to extreme time saving. Here are three considerations for evaluating tools:

Which step of the analysis process is it for? I.e. What are you trying to do or solve for?

This is the most obvious question. You don’t just want a hammer for the sake of owning a hammer, you want a hammer to, say, put up some nails to hang a painting. If you are looking for a tool at all, you probably already have a problem you are trying to solve.

At what stage of the analysis process does your need fit into: data cleaning, model building, analysis, visualization…? What specific problems does it need to be able to solve, and what are the specific integrations that will allow it to make your job easier?

Once you have defined the problem at hand, you move onto vendor selection. For example, once you decide that you need a tool for attribution, then you must go through the vendor selection process (some questions to ask potential attribution providers can be found here). You must then determine which platform or programming language will best suit your needs and skill set.

Clearly defining what you are trying to solve helps you determine what type of tool you need, and then you have to dig deeper into which provider.

 

 Is it a one-time task, or will it repeat?

 

If the task only needs to run one time, it is usually best to stick to the tools you already have access to and know how to use.  You won’t benefit by spending lots of time learning a new tool, automating the task, and so on, unless it is going to run multiple times.

However, if the task has to be done on a regular basis, it might be worthwhile to assess new tools and invest in training on how to use the new tool, or bringing in a fellow coworker who already knows how to use it. Setting up the task to run more automatically can save time and money in the long run.

 Is the dataset large, or is it small?

If the dataset is relatively small, executing the task manually may be quicker than setting up the logic to run it programmatically. However, when working with very large data sets, you will likely have to select a tool that can accommodate it. For example, you can only open a CSV with less than 1,048,576 rows in Excel, so if you have more data than that you will have to find a different tool to work with.

Further, some tasks can take a very long time to run when working with big data. Will you need to run it on a server to get the task to run in a reasonable amount of time?

Consider the size of the data you’re working with when selecting a tool.

 

Conclusion

The main point of all of this is that there are so many tools in use for analytics, data visualization, data processing and so on. Sometimes, they can be interchangeable, and oftentimes, the time spent learning how to use the ‘better’ tool will negate any of the actual benefits of using the new and improved tool. However, often the ‘better’ tool is overwhelmingly superior since learning it or bringing in someone who already knows it can be a huge benefit to the organization.

So, rather than just picking up your favorite hammer, first take the time to do a quick assessment of which tool is best for the job at hand, and always be open to learning what other tools your coworkers are using to solve their problems so you can continue to expand your own toolbox.

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