I always count myself lucky to have worked in 3 different sized companies; global investment banks, scrappy startups and the mid-size companies who want to eat up market share without sacrificing some of velocity as they grow.
I love working on products/business models. It’s a constant reminder that no matter how much people think they know, our intuition is often wrong; but we can maximise the chances of success by making decisions based on concrete evidence.
We All Have Growing Pains
During the growth of your company (define growth how you want: team size, revenue,# of users, # of paying customers, etc) you will see the workforce change, early hires complaining they no longer know everyone on a first name basis, technical debt and decision making slowing down🤦.
It’s no surprise that small closely aligned teams start to deliver mixed messages (checkout how Google moves with velocity!).
The point is, it can really be difficult for people to keep a shared understanding of what’s going on across various communication channels.
There’s a lot of bias that goes into decision making and a lot of experienced people feel like they can make the right call once they engage with decision makers and the key players. That’s completely fine, but whilst confirmation bias can feed your ego, it leaves you confused when it comes to knowing what variables play a part in the success.
Thankfully, we can validate our assumptions with evidence.
Insights should drive action
What game are we playing and how do we keep score?
In a recent project I got asked to help someone bring aggregated data from multiple sources and I simply asked, What is the decision are you’re trying to make with this?
This really hit home the fact that the insights we gather needed to drive action and I also recommended that she formed her hypothesis first.
Although it’s the hardest pill to swallow, I find that this forces people to ask themselves if they are willing to change in the face of evidence.
For those of us who want to be able to cut through the noise, swerve past people’s ego’s, and dodge the politics, we can focus on helping our teams make decisions that are based on concrete evidence.
As much as there is appetite for Data Science and other forms of modelling I get the feeling that a lot of companies are not yet ready to invest in making it a reality and also don’t have the talent, infrastructure and culture to make use of it. Besides, is predictive modelling necessary for your type of business?
If you don’t measure it, you can’t improve it
The first thing to do is figure out what questions you want to be able to answer and think about what sources of data you have available to you now. If you are transacting already, then this information exists — it’s all about getting it in a way that’s readible (I’m a visual person, so I like having vizzes)
Data doesn’t always have to be BIG-DATA
Unfortunately, I can’t prescribe a specific solution because every companies data environment is different but I would start with these 5 steps.
- Reaffirm your strategy and how it ties back with your mission
- Identify the question(s) you would like to be able to answer
- Form a hypothesis (given what has happened in the past, what is true and how can we use that insight to better understand our business or our product)
- Look at the data that is currently available (less effort, it’s quick wins and helps you identify unknown known’s and known unknown’s)
- Extract and analyse raw data (this is an art more than a science). Consider who will be reading it and present it in a way that drives action
Some of the tools
These tools are often found in the data, reporting and analysis landscape used by big companies who have made the commitment for a year or more.
Other subscription based tools recommended typically used by small companies: