things change when you have children

I just watched a video of a short chat between Nancy Duarte and Garr Reynolds about creativity and story. In it, Garr talks briefly about how having children has influenced his view and approach: "Things change when you have children."



Yes, they do.

And will continue to for me, as my husband and I are excitedly expecting baby #2 this summer!

(When you've got one who is this cute, how can you help but do it again?!?)

failure in design(er)

Yesterday evening, our recently purchased, lovely new couch arrived. Or, rather, the large boxes that contained our recently purchased, lovely new couch arrived. Suddenly, it was very clear what we gave up by not springing for "white glove delivery".

Not to fear, though. It came with instructions. My husband and I can both read and follow instructions.

Right?

Easier said than done, it turns out. These were certainly not the worst assembly instructions that I've ever seen, but they left a lot to be desired. Perhaps a very lucky or clever individual could get it right the first time (we were neither of those, as it turns out). But you'd have to know which details were important to pay attention to.

We had several false starts, turning the diagram round and round to say: Ah, now I get it! Wait, no, now the one frame piece is too long. Oh, now I see the problem. Oops, no, now the holes don't line up. After several such instances, we recognized that the bars in the frame are not equidistant apart (and it matters which two are closest together), we realized that two of the frame bars had four holes each and the third had two holes each and that the relative positioning of the bars with respect to one another is important, we learned that FX1 and GX1 are in fact not interchangeable (even though at the top they're shown with FX1 clearly on the left and GX1 on the right, but then below are less prominently switched).

Now that we've assembled the couch correctly (finally), we could do it again without breaking a sweat. We know exactly which are the important parts in the diagram to pay our attention. But why was it so difficult the first time around?

I'm in the middle of a book I'm enjoying, The Design of Everyday Things. In it, Donald Norman asserts that when you have trouble with things, you shouldn't blame yourself (even though that tends to be people's natural tendency). Rather, it's the fault of the design and you should blame the designer. While this book focuses mainly on product design, I think many of the insights are true in the data visualization space as well. In this case, the corollary is clear: if you are struggling to understand a visual representation of data, don't blame yourself; blame the designer. Odds are, they didn't adequately take your needs as the audience into account in their design process. For those designing visual displays of information, this is a reminder to always keep your audience in mind, for, as Donal Norman says, "well-designed objects are easy to interpret and understand."

I unabashedly blame the designer of the instruction diagram for our difficulty assembling something that could have easily been straightforward. If the designer had thought about the intended users and leveraged affordances to make it clear which details were important and should be paid attention to, my husband and I would have had a much less frustrating process assembling our (now truly lovely) couch.

What design issues cause you frustration? What can we learn from this to apply in the world of data viz?

people analytics

Happy new year! Before I get to the meat of this post, bear with me through a quick plug for my upcoming public storytelling with data workshops in Boston, DC, Seattle, Chicago, and San Francisco. Details can be found here. Please help me spread the word!

Now back to our regularly scheduled programming...

Up until relatively recently, my day job was in People Analytics at Google. My career has been (and continues to be) focused on helping people make sense of, understand, and act based on numbers and analytics. Applying these skills in the people space over the past six years was a fascinating adventure.

People Analytics is an analytics team that is embedded in Google's Human Resources organization, where the goal is to help ensure that people decisions made at Google - decisions about employees or future employees - are data driven. Personally, I credit this role and my managers and team for really allowing me to use people analytics to hone my data viz and storytelling with data skills, gain a better understanding of the science behind data visualization, and give me the opportunity and autonomy to build and teach a course on data viz there, which ultimately paved the path to where I am today.

But I stray off track. Let's get back to the topic of people analytics. Because of the time I spent in this area (and Google's reputation in this space as a thought-leader), I periodically gets calls from the press asking for details. Recently, a reporter from the Wall Street Journal reached out to discuss "big data and how it's used in human resources". It turns out that they mostly wanted me to talk about some proprietary Google projects that I declined to comment on (unfortunately, I can't share some of the really interesting ground-breaking work), but I did sketch out some notes when I was thinking about the topic, that I thought I'd share here for those who may be interested.

Cole's [somewhat random] thoughts on People Analytics
Employees are a precious resource at any organization. Data can help you to make better decisions when it comes to these precious resources. Broadly, I think about People Analytics in terms of the different stages of the employee lifecycle:

• Hiring: getting the right people in the door.
• While they're there it's about making them as effective as possible and creating an environment and opportunities that optimize efficiency and impact (performance management, career development, rewards, employee sentiment).
• Attrition: getting ahead of it so you can retain those you want to keep and push out those you don't (as appropriate).

You can put data to each of the above spaces to make smarter decisions. In the early stages of people analytics, much of it is descriptive: understanding what things look like currently and identifying gaps between that and where you want to be. As you move up the value chain, you can get into some really interesting predictive spaces to try to understand how things will look in the future and what levers you can pull to impact that.

There are a number of challenges when it comes to leveraging the people analytics space. I'll outline my view on two of the big ones:

• Marrying what is often many disparate data sources into a single holistic view of the employee that can be aggregated up and looked at through different lenses so that the info is available to the right person at the right time to take action. This becomes even more challenging when you start to think about external data sources (e.g. Twitter, LinkedIn) that could be integrated for improved insight. In the early stages of people analytics, the first goal is to understand where you're at currently, which often takes the form of reports. Over time, these may be replaced by dashboards that "push" data out to internal stakeholders. Once the current state is known, analysts' time is freed up to focus on the more interesting questions and custom analysis to be able to drive data-informed decisions. 
• Finding the right balance (and organizational appetite) between data-driven and considering the context. Many struggle to make the data make sense - taking the organizational and business context into account when it comes to interpreting and using the data. Most companies have a wealth of qualitative data - things that HR business partners or managers know that will never be adequately captured in hard numbers. There's also a wealth of information in text data that's typically largely untapped (resumes, interview notes, employee surveys, performance reviews, exit interviews). Being able to marry all of this together will provide the most robust view, but is easier said than done (and may sometimes be more than is needed, anyway). It's about figuring out when to lean a little more in one direction vs. the other to create buy-in and build the best solution for a given situation.

If this space sounds fascinating (it is), you can check out Google's open roles here. Increasingly, other companies are devoting brains to this area as well; search openings by querying People Analytics or HR Analytics.