Walk into any startup office and you’ll see almost the exact same thing: a bunch of big televisions showing real-time dashboards. Traffic, tweets, leads, sales, revenue, application performance, support cases, satisfaction, A/B test results, open rates; you name it, there’s a real-time dashboard for it.
Robert Caro nailed it in a recent Gothamist interview:
[Gothamist] There’s something called Chartbeat — it shows you how many people are reading a specific article in any given moment, and how long they spend on that article. That’s called “engagement time.” We have a giant flatscreen on the wall that displays it, a lot of publications do.
[Caro] What you just said is the worst thing I ever heard. [Laughs]
What’s the point of that dashboard?
I do a lot of reporting: on operations, on support, on usage, on finances, on marketing, and on every other topic that matters to a business. Whenever I consider a new piece of reporting, I ask myself one question: what’s the point? What’s the action or decision that this reporting is intended to impact? When someone consumes it, what can they do about it? Can they make a decision? Can they go do something personally or ask someone to do something? If there’s nothing that can be done in response to a report, does it need to be reported in that manner?
Most real-time dashboards fail to pass this usefulness test. Knowing how many visitors you have on your site right now or what the open rate was for the email that just went out doesn’t generally enable you to do anything. In a SaaS business, knowing what today’s revenue is doesn’t really enable you to do anything either: revenue today is the consequence of a sales and retention cycle that started long ago.
There are cases where real-time dashboards are invaluable. Knowing whether database response time is higher right now than it was a few minutes ago is incredibly useful when your site is slow, and we use real-time dashboards extensively for solving availability and performance problems at Basecamp.
Perhaps real-time dashboards aren’t that useful, but if they aren’t a lot of work to set up, what’s the harm? Isn’t faster data better data?
The problem comes when you look at a real-time dashboard: no matter how much you try to train yourself, you’re going to react to the data that you just saw. You might not realize that you’re reacting to it, but you absolutely are.
Almost every metric is noisy. Active users being down 3% from yesterday could be the start of a longer trend, but it’s much more likely that it’s just noise in the data. When you see that 3% decrease on a real-time dashboard, however, the panic starts to set in: what did we do wrong? Anything you were thinking about gets thrown out the window, because now you’re reacting to something that looks urgent, but really isn’t important.
I’ve seen many cases of people looking at real-time A/B test results and judging the experiment after an hour or two. No matter how much labeling you do to point out that the results are meaningless at that scale, humans will still draw conclusions from them. In our case, and for virtually every online business, daily updated results are more than adequate for making decisions, so there’s only downside to real-time A/B test results: the risk of making a decision off insufficient data and that decision turning out to be the wrong one.
We recently scaled back and de-emphasized the use of a bunch of metrics relating to our support team. We found that a focus on average customer happiness scores, response time, and case volume made it hard to give each individual customer the attention they deserved, and caused a ton of unnecessary stress. Kristin explained our motivation well:
We’re attempting to change our relationship with Smiley and metrics so that our focus is more on each individual customer and less on any sense of competition with ourselves and/or each other. Smiley leads us to focus on the vocal minority (about 20%) of customers who leave a rating. The customer we’re currently working with should have 100% of our attention, so we shouldn’t be worried about quickly getting rid of them to move on to the next one or focusing on the customer as a potential Smile instead of as a person who needs help.
The next time you feel the urge to look at Smiley and/or Dash, get up and take a break. Make some tea. Eat some cheese popcorn. Pet an animal. Stretch.
I’m really proud of the support team for their evolving relationship with the use of metrics. We got a lot of value out of rigorously analyzing our support caseload to figure out the right level of staffing, scheduling, and address root causes, but we can do all of those things without real-time reporting. Knowing when not to look at a piece of data is just as important as knowing when to look.
Make reporting great again
How can you make reporting less stressful and more useful? Try a few of these simple changes:
- Change the timeframe. Instead of looking at the last day of data, look at the last week or month. Maybe there’s a bigger seasonal trend that will help to contextualize today’s data.
- Move upstream. Instead of reporting something like daily revenue, which is the output of every step of your funnel, report on the actual underlying drivers that you can impact.
- Contextualize. Instead of showing an absolute metric, show a percentage change or a comparison to last week or last month.
- Convert dashboards to alerts. Computers are great at sending emails according to defined conditions, so let them do that. Don’t rely on checking a real-time dashboard to detect that something isn’t right; define your criteria and let an automated system tell you when you need to take a deeper look.
I get it: real-time reporting is fun. It’s something shiny to put up in your lobby, and it fills you with lots of little bits of trivia to drop at a moment’s notice. But that comes at a cost, and too many people embrace real-time reporting without thinking through the consequences.
A paean to slow data
Eschewing real-time dashboards is just one part of what I like to call a “slow data” approach to data science. I’m not talking about free-range histograms or artisinal Poisson distributions, but about taking the time to really understand the problem you’re solving, the data you’re using, and the implications of the results. My profession spends most of its time talking about statistical methods and visualization, and very little time talking about the actual business problems or impacts of the work. Fortunately, I mostly just do arithmetic, make very simple charts, and avoid making real-time dashboards, so I have lots of time to think about the problem we’re trying to solve.
I’d encourage you to give this slower approach to data science a shot in your organization too. Next time you think about making a real-time dashboard, ask a deeper question about the underlying problem instead. I guarantee you’ll find more value from that.