Different Tools for Different Tasks

I am often asked a version of the following question: “What data visualization product do you recommend?” My response is always the same: “That depends on what you do with data.” Tools differ significantly in their intentions, strengths, and weaknesses. No one tool does everything well. Truth be told, most tools do relatively little well.

I’m always taken by surprise when the folks who ask me for a recommendation fail to understand that I can’t recommend a tool without first understanding what they do with data. A fellow emailed this week to request a tool recommendation, and when I asked him to describe what he does with data, he responded by describing the general nature of the data that he works with (medical device quality data) and the amount of data that he typically accesses (“around 10k entries…across multiple product lines”). He didn’t actually answer my question, did he? I think this was, in part, because he and many others like him don’t think of what they do with data as consisting of different types of tasks. This is a fundamental oversight.

The nature of your data (marketing, sales, healthcare, education, etc.) has little bearing on the tool that’s needed. Even the quantity of data has relatively little effect on my tool recommendations unless you’re dealing with excessively large data sets. What you do with the data—the tasks that you perform and the purposes for which you perform them—is what matters most.

Your work might involve tasks that are somewhat unique to you, which should be taken into account when selecting a tool, but you also perform general categories of tasks that should be considered. Here are a few of those general categories:

  • Exploratory data analysis (Exploring data in a free-form manner, getting to know it in general, from multiple perspectives, and asking many questions to understand it)
  • Rapid performance monitoring (Maintaining awareness of what’s currently going on as reflected in a specific set of data to fulfill a particular role)
  • A routine set of specific analytical tasks (Analyzing the data in the same specific ways again and again)
  • Production report development (Preparing reports that will be used by others to lookup data that’s needed to do their jobs)
  • Dashboard development (Developing displays that others can use to rapidly monitor performance)
  • Presentation preparation (Preparing displays of data that will be presented in meetings or in custom reports)
  • Customized analytical application development (Developing applications that others will use to analyze data in the same specific ways again and again)

Tools that do a good job of supporting exploratory data analysis usually do a poor job of supporting the development of production reports and dashboards, which require fine control over the positioning and sizing of objects. Tools that provide the most flexibility and control often do so by using a programming interface, which cannot support the fluid interaction with data that is required for exploratory data analysis. Every tool specializes in what it can do well, assuming it can do anything well.

In addition to the types of tasks that we perform, we must also consider the level of sophistication to which we peform them. For example, of you engage in exploratory data analysis, the tool that I recommend would vary significantly depending on the depth of your data analysis skills. For instance, I wouldn’t recommend a complex statistical analysis product such as SAS JMP if you’re untrained in statistics, just as I wouldn’t recommend a general purpose tool such as Tableau Software if you’re well trained in statistics, except for performing statistically lightweight tasks.

Apart from the tasks that we perform and the level of skill with which we perform them, we must also consider the size of our wallet. Some products require a significant investment to get started, while others can be purchased for an individual user at little cost or even downloaded for free.

So, what tool do I recommend? It depends. Finding the right tool begins with a clear understanting of what you need to do with data and with your ability to do it.

Take care,

5 Comments on “Different Tools for Different Tasks”

By Marc. February 20th, 2018 at 5:51 am

Love this. So true.

By Jason Mack. February 20th, 2018 at 8:45 am

Excellent post Steve! I’ve found that enterprises tend to fall into the trap of assuming there is a one-size-fits-all tool when use cases with data can vary quite a bit when you get into the ‘what do you do with data?’ question. Successful IT organizations take a portfolio-style approach to tools, supporting multiple that may appear to overlap but in reality serve specific variations of usage.

By Nigel Hawtin. February 21st, 2018 at 4:35 am

Agree Steve. As with so many of those questions when it comes to datavisualisation and information graphics ‘it depends’…

By Pete. May 8th, 2018 at 4:05 am

Hi Steve,
I agree with you, the most important point it’s the purpose.
But is there a tool that can “do well” the last 3 categories of your post?

By Stephen Few. May 8th, 2018 at 7:24 am

Hi Pete,

There are many tools that can be used to develop dashboards, but none that do the job particularly well. The same is true of the other two categories that you mentioned: presentation preparation and analytical application development. In general, software applications of all types tend to be designed to work adequately at best. Almost from the beginning we’ve given software companies permission to develop mediocre products. In fact, many software companies misuse the Pareto Principle (a.k.a., the 80/20 rule) to argue that they should only strive to achieve 80% of what’s needed, for the remaining 20% isn’t worth the effort. Pareto would roll over in his grave if he heard people using his observation–originally that 80% of the property in Italy was owned by 20% of the people–as an excuse for “good enough” development. If we held software companies accountable, rather than accepting their low standards, products would improve.

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