Good morning, it’s まさきん.
I’ve heard the question come up again and again: is data analysis or web analytics really such a specialized job?
Having done this work for a long time myself, what I’ve come to feel is that what the job really needs isn’t advanced statistics or machine learning knowledge — it’s something else entirely.
Analysts Should Be Translators
Data analysis tends to get pictured as an extremely specialized field. But personally, I don’t see it that way — echoing something I wrote after reading the book Competing on Analytics a while back.
Sure, there are people who can pull sharp business insights out of advanced analysis, or who can carry a project from analysis through design and implementation. But I don’t think there are that many people like that.
What most workplaces actually need, I think, is the ability to translate the specialized language of data into the language of business. It’s not enough to just look at the numbers — the job is to convert what they mean into something the people around you can actually act on.
I think that same sense of “translating” applies outside of work too. Take your monthly phone bill, for example — just looking at the total doesn’t tell you much on its own.
Clicking this opens the Rakuten login page. Once you log in, you’ll see the campaign details.
The Danger of Ending Up as Just a “Handy Lookup Machine”
There’s a trap I’ve kept an eye on for a long time, one that analytics organizations easily fall into: the analytics team ending up as nothing more than a “lookup desk.”
Requests like “can you pull this number” or “can you show me the trend over this period” are certainly meaningful work. But if that’s all a team ever does, it drifts away from the role it’s actually meant to play: helping people make decisions. If you just pass along whatever numbers get requested, the analytics team’s value never rises above being a handy lookup service.
That said, I don’t think this stage deserves to be dismissed outright. As a first step, there’s nothing wrong with being an organization that just steadily handles lookup requests. Trust only builds up by continuing to surface even small pieces of visible information and delivering them to the people around you. Keep the destination in view, but keep stacking up whatever you can do today — I think that’s about the right posture to have.
User-Facing or Infrastructure-Facing: Which Side Do You Lean Toward?
I feel like data-related work splits broadly into two directions. One is the side that uses the numbers once they come out. The other is the side that builds — collecting and maintaining the data itself.
On the using side, that spreads out into things like CRM-style analysis, user education, and recommendation design. On the building side, it covers data requirements design, implementation, and maintaining the data infrastructure itself.
When it comes to career planning, which side you plant your feet on changes quite a bit about which skills you should be building. I felt a similar tension when I was thinking about the job of product manager a while back, and honestly, I still haven’t fully worked out which side I should be planting my feet on either. Having started on the user-facing side and gone back and forth between design, implementation, and strategy ever since, I genuinely still carry both a desire to build up more strength on the infrastructure side and some uncertainty about where I currently stand.
In the Age of Generative AI, Translation Skill Matters More Than Ever
These days, generative AI has made tasks like writing SQL or building dashboards much easier than they used to be.
A discussion I read recently framed this shift as a move from “analytical power” to “the power to find a winning angle.” The reasoning goes like this: now that AI can handle routine work like processing data, building visualizations, and choosing models, more weight falls on the ability to identify the right KPIs and, more fundamentally, on the hypothesis-building work of deciding what question to ask in the first place. Another discussion described the data scientist’s role expanding in three directions: bridging knowledge across different fields, framing strategic questions, and acting as the storyteller who connects analysis results to management decisions.
In other words, the weight on “what to ask” and “how to interpret the numbers you get and connect them to the business” is, if anything, only growing. The smarter the tools get, the more I feel like the gap between people who can frame good questions and people who can translate the answers turns directly into a gap in results. Whether a team ends up as a mere lookup machine or manages to work its way into actual decision-making as a translator comes down, I think, to exactly this.
If I had to train this skill in actual practice, the habit I try to keep is putting into words what I want the person looking at the numbers to do next, before I even start the analysis. Rather than pulling the numbers first and figuring out the interpretation afterward, I try to hold a hypothesis about the conclusion I want to land on before I sit down with the data. Just changing that order, on its own, makes a real difference in the quality of what comes out the other end as a translator.
You see the phrase “democratization of analysis” a lot these days too. Generative AI has increased the number of people who can query data on their own without specialized knowledge, which means the rarity of “the ability to look things up” — something that used to belong only to the analytics team — has gone down. And precisely because of that, what stands out more now, in my experience, is the added value of the translator: how you interpret the answers you get and connect them to the next action.
Now that pretty much everyone on the ground can touch data to some degree, I also feel like the role of breaking down technical terms into plain language is increasingly being called for all over the company, not just within the analytics team. The era where it was enough for only the analytics team to act as translator may be gradually coming to an end.
Wrap-up
Technology and tools change with the times. But the essence of standing between data and business as a “translator” doesn’t seem to have changed much at all.
Come to think of it, I realize I don’t actually look closely at the numbers in my own life all that often. I think I’ll start by going through my own monthly phone bill by hand.
Clicking this opens the Rakuten login page. Once you log in, you’ll see the campaign details.
This article contains affiliate advertising. If you sign up for a product or service through a link on this site, we may receive compensation from the partner company. The site operator is also an employee of Rakuten Group and may receive compensation through an employee referral program. The content and opinions here are based on the operator’s own experience and research regardless of any advertising relationship, but please read with the above in mind. See the Disclaimer & Affiliate Disclosure for details. Disclaimer & Affiliate Disclosure
This article includes machine-translated content. Please check the official Rakuten Mobile multilingual page for exact terms.
If you have concerns about Rakuten Mobile coverage or signal quality, you can consult through the official signal improvement request form .
