Wednesday, January 29, 2014

Looking for Your Lens: 3 Tips on How to Be a Great Analyst

The other day as I was walking to work, all of a sudden, "pop!" one of the lenses in my glasses decided to free itself from the prison of its metal frame and take flight.

Well, damn.

The sidewalk was wet and partially covered in snow, and also with little islands of ice here and there. Finding a transparent piece of glass was not going to be easy.

So there I was, wandering about a small patch of sidewalk next to Toronto City Hall, squatting on my haunches, peering down at the sidewalk and awkwardly searching for my special missing piece of glass. I was not optimistic about my chances.

Most people walked on by and paid me no notice, but one kind soul, a woman with black, curly hair, stopped to help.

"Did you lose something?" she asked.

"Yeah," I said, defeated, and held up my half empty black frames.

"Can I help?" she kindly offered. "I'm good at this sort of thing."

"I guess," I said, having already given up on restoring my headgear to completeness.

We scoured the sidewalk while urban passerby gave the occasional puzzled look, hurrying along.

"Ah!" she said, and amazingly, picked up my lens which she had located. It had been hiding on a small patch of snow near a planter.

"WOW!" I was genuinely impressed. "You are good at this. Thanks so much."

"No problem," she said. "have a great day!" and then promptly disappeared down the street, leaving me standing there on the sidewalk, bewildered.

That single small episode, a tiny vignette of a single life in a giant city amongst millions of others, was quite profound for me. This was because it got me thinking about two things: one, the kindness of strangers, and the other, of course, what I am always thinking about - the business of doing analysis.

Because as it turns out, those few statements that kind stranger made are equally important in being a great analyst.

"Did you lose something?"

A problem that a lot of analysts deal with on a regular basis is one of communication. The business, the stakeholder, the client, whoever it may be, comes to the analyst for help. They want to find out something about their business because they have data, and it's the job of the analyst to turn that data (information) into insights (knowledge).

But here's the problem - you can't find something if you don't know what you're looking for.

Just as the kind passerby wouldn't have been able to help me find my missing lens if she didn't know what to look for, if you don't know what kinds of insights you want to pull out of the data you have, then you won't be able to find what you're looking for either.

"We want to know how our people are connecting with our brand."

It is the job of the analyst to turn these (often vague) desires of the business into specific questions that can be answered by analyzing data.

What people? (everyone, purchasers only, Boomers, Gen X, Gen Y, single mothers between the ages of 22 and 32 in urban centers?) What does connecting with the brand mean? (viewing an ad, purchase, visits to the website, app downloads, posts on social media, all of the above?)

So remember that a very large part of the job of the analyst is communication - not just about data - but working with others to determine exactly what it is they want to know. Once you know that, you can determine how to best do analysis to find the answers that are being sought after - hiding in plain sight in the data, like a piece of glass on a snowy patch of sidewalk.

"Can I help?"

Here's something I think that a lot of analytical-type thinkers (this author included) often need to be reminded of: you can't know everything. Even if you really, really want to. I'm sorry but you just can't.

And that's why once you know what it is you're looking for, and what you need, you'll need to ask for help (and that's okay, that's why we have meetings!). Sometimes the mere process of tracking down the data is a considerable task in itself. Sometimes no one really has a great overall understanding of a how a really large, complicated system works - that kind of knowledge is often very distributed. These sorts of situations may require the help of many others in your company (or another business, vendor or client) who all have varying knowledge bases and skill sets.

It's the job of the analyst to connect with the people they need to, get the data that they need, and do analysis to find the answers which are desired. Also if you're a good analyst, you'll probably provide some kind of context around the impact (i.e. business implications) of your answer, and what parties would need to be involved to make take the most beneficial actions as a result.

So even if you're a data rock star don't ever be afraid to ask for help; and conversely don't hesitate to let others know who should help them too.

"I'm good at this sort of thing."

Getting the analysis done requires not only not being afraid of asking for help, but also knowing the strengths and weaknesses of yourself, your team, and any others you may be working with.

It's hard, but in my opinion, it takes a bigger person to be honest and admit when they are out of their depth than to say they can do something they clearly cannot.

When you're out of your depth you have three options, which are really just three different ways of finishing the statement I'm not an expert. And they go something like this: I'm not an expert....
  1.  "... so I'm not going to do it because: I don't know how / wouldn't be able to figure it out / it's not in my job description."
  2.  "... but I can: learn quickly / give it a try / do my best / become one in 5 days."
  3.  "... but I know <colleague> is and could: provide context to the problem / definitely help do it / teach us how."
And the difference between answer #1 and the last two is what separates the office drones from the thought leaders, the reporting monkeys from the truly great analysts, and the unsuccessful from the successful in the world of data.

As I noted in the section above, you should never be afraid to ask for help, because there are going to be others out there that are better at things than you, and if you're good you'll recognize this fact and both of you will benefit. Hey, you might even learn something too, so next time you will be the expert.

Just remember that you can do analysis without crunching every number personally. You can work in data science without building the predictive model all by yourself. And you can work with data without writing every line of code alone. No analyst is an island.

"No problem! Have a great day!"

I hope that my little story and these points will help, or at least help you think, about the business of working with data and doing analysis, and what it means to be a great analyst.

This last point is perhaps equally, or even more  important, than the others - always be kind to the people you work with; always make it look easy, no matter how hard it was; and always be happy to help. That, above all, is what will make you a truly great analyst.

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