The 4 Important Things about Analyzing Data Part 1: The Importance of Providing Many ‘Obvious’ Results

In the past few years, mass accumulation of data has increased. Meanwhile, the distributed parallel processing of the data has matured. Although the original focus was on the analysis back end (platforms) to support the accumulation of data and the efficiency of the batches, I believe that the importance of the “data,” the “analysis,” and that of the data analysts who are in charge of it has finally come to be understood.

I think that while this is gratifying for the analysts, at the same time it also puts a great amount of pressure on them.

One of the stress factors analysts might feel is pressure to meet the expectations of the people who will act on the results, such as managers or customers (i.e., decision-makers), who are likely to think that with the massive quantities of data collected the analysts will be able to solve many problems.

However, in reality, regardless of how much data is collected, it is incredibly difficult to immediately discover and deliver excellent results such that they would improve management at once. On the contrary, with larger amounts of data, various factors become intricately intertwined to the point that no result is produced at all.

Of course, the analysts cannot ignore the pressure of their mission to “use data to continuously provide improvement results critical for management.” In this blog series, I would like to talk about the four things I believe are important regarding the everyday role of analysts and what kind of results they should provide.

Number 1: The Importance of Providing “Obvious” Results

From the professional standpoint of the analyst, it is not acceptable to say that “there were no significant results” or that “there were only obvious results.” Consequently, some analysts may constantly be stressed by the thought that they have to make some new discovery.

However, it is extremely important to do precisely the obvious and to provide many “obvious” results. I believe that in order to come up with meaningful or new discoveries, the main prerequisite is “obvious” results. It is important to be aware of this when analyzing.

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Is Obvious Actually that Obvious?

To begin with, for whom are those “obvious results” obvious? Is what an analyst considers obvious the same as what a decision-maker considers obvious? In my experience, half of what analysts think of as obvious is not in fact obvious for decision-makers, and vice versa. In actuality, there are many instances in which an analyst may feel bad for only providing obvious results but the reaction was, “Hmm, that’s interesting and unexpected!” or the opposite situation of proudly showing new results yet the reactions were “that’s expected” or “of course it is like that.”

In other words, there are many instances when results that seem like no big deal to analysts actually provide valuable information to the decision-makers. However, due to the fact that analysts tend to not actually pay attention to or report such results, those results that are in fact important to decision-makers tend not to be released.

That is why it is very important to “provide many obvious results.” As to what to concretely do, it is beneficial to release as much data as possible, with many easy-to-understand tables and graphs. Also, repeatedly share the results with the decision-makers; in doing so, the expectations of the partner – and the results meaningful to him/her – gradually become clearer and clearer. I believe that is the first step toward providing meaningful and significant results.

It is truly important to give really obvious results. Although one might think that the obvious results – in other words, the results that decision-makers expect –  are meaningless, this is not the case. It is very meaningful to validate the thoughts and assumptions of decision-makers with numerical values and graphs. The sense of security and confidence given when those thoughts and assumptions are correct and strictly verified is stronger than one could imagine. Moreover, this will continue to steadily increase the confidence in making the further decisions. It is also the job of analysts to provide a sense of security to our partners.

“Repeating the Obvious” Produces New Discoveries

I am convinced that new discoveries are first made through this “repeating the obvious” cycle. Thus, the inspiration and assumptions of decision-makers are backed by what was previously large amounts of obvious results.

Rather than stressing over pressure to uncover significant discoveries in data analysis, isn’t it more significant to continuously share small amounts of seemingly “obvious” results in easy-to-understand ways? New discoveries and critical management insights may emerge from the repetition and steady stream of data.

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Taka Inoue
Taka Inoue
Taka Inoue is Chief Data Scientist at Treasure Data. He began his career as one half data engineer (he founded the MongoDB User Group Japan) and one half statistician at a gaming studio. Taka's deep expertise of "real world" data science is a boon to our customers.
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