We are probably the most tracked and measured people that ever walked the planet. And if you listen to the internet-of-things people, it’s actually only going to explode from here on it. Yet, awash in a sea of data are we really tuned into what this does for us.
Now don’t get me wrong. I’m not slamming data at all. For me Math has always been Art, with a capital A. I believe modelling is akin to imagination and visualization. And gaming is actually working a probability. After all, statistics was initially born in the casinos of Monte Carlo and on the shipping docs of London. So contrary to what normal people think, I believe it’s actually fun.
For me data has to be data for a reason. That is what turns data into its bigger sibling – analytics.
If like me, you love the art of analytics, and you understand the term vanity metric, then this post may not add value for you. If sometimes you feel you are drowning in this sea of data around you, and would like to hear more about what is the difference between data and analytics, please read on.
Some of my influencers in terms of taking this new view of an old art come from books like Lean Analytics and The Tipping Point. Some of it comes from things heard at meetups that are too varied to quote. Some of it comes from always having analytics in some form a part of my work – performance testing of products, SLA measurements, performance quotas and margins, business forecasting and performance benchmarks…
If you read some of my metrics posts, then you see the odd statement thrown in like: I believe tracking steps trod is a vanity metric, that doesn’t really help you meet weight-loss goals.
In Lean Analytics they state that a good metric has these characteristics:
- A good metric is comparative
- A good metric is understandable
- A good metric is a ratio or rate
- A good metric changes the way you behave
To me, knowing how many steps I walked today only satisfies the understandable characteristic. Though it might change my behaviour momentarily, it’s not actually a sustainable push towards fitness and weight loss.
Though this is a consumer type example that is easy for everyone to understand, the bigger reason we want to look at data and analytics is to learn how it can impact positively how we do business.
Data collection that is just information gathering or tracking purposes is just that data. The transformation of this data into a metric with the above characteristics provides us with insights that can impact how we do things.
Metrics should always be book ended by two things. Firstly, the reason we are collecting the underlying data in the first place. Our concrete business goals of what we want to achieve, the bars that we want to be measured and rewarded by, or the behaviours we want to change. Secondly, the actions that we decide to take after we draw an insight from the metrics.
As an example, let’s say that our business goal is that we want to shorten the sales cycle that it takes to close a larger deal.
We might want to look at the data we have collected around Opportunities that we have closed in the recent past and decide on a few metrics to slice the data up a bit.
If we were to simply look at the average time it has taken us to close deals in the past year, and then create a goal to bring it down 10% this year then that isn’t really an effective metric. It a lot like the steps trod.
We might want to create a report that slices the data in terms of Industry. If we do a comparative metric of the time it takes to close a deal by Industry type, then it might point us towards the types of deals that take longer to close. If we want to focus a goal on shortening our sales cycle, then it might influence us to seek out more leads of the industry type that takes less time to close. And make a business decision to not go after companies that we know will take up time and resources to close a sale.
Another popular report might be to look at the performance of an Account Manager. Are some Account Manager’s consistently taking a shorter time to close sales? (This is not always a fair measure taking independently, without thinking of the other factors like size of account, complexity of deals, industry, etc.) Metrics around performance of sales people can help us identify the high performers and those that are struggling. We might want to make some business decisions about reassigning account responsibilities, training or coaching opportunities for those struggling, and supporting the high performers to do more of the same.
Or we might do a ratio of size of deal and time to close, and learn that the larger deals do in fact take longer, but have a bigger payout. Then we might let go of our goal of closing faster and go for the bigger payout. At the same time, if we also track a metric that is a ratio of revenue vs cost of sales by sale size bands, then we might see that these large deals are actually less profitable… then we will want to keep to our goal of trying to shorten the sales cycle to see a future change in the profitability ratio for larger deals.
The point is just staring at a list of data that tells us the length of time of deals we closed and how much revenue we made is just data. Look at ratios and rates that tell us what kind of deals close sooner, are more profitable, and who is closing them faster, is much more meaningful.