If you are a data person by which I mean you use data or facilitate data to others for their use so that others can make “data-informed” decisions on an almost daily basis then you might have come across many scenarios where data is just overhead and sometimes using data even might be an overkill.
So imagine a situation where a person who is not aware of many aspects of data and how it can be utilised how much overwhelming it can be for them, right?
Business, product and engineering all, need to come together to get the right information from data, which means essentially tons of meetings, documentation and finally, the intuition-based decision is taken, which means why to use data in the first place.
Here are the top 6 excuses I have seen why not to use data — and simple solutions to get moving.
Excuse #1: “There is too much data”.
Solution: Start with the questions, ask for the directions.
The №1 reason I think people don’t like using data is that it can be too much to handle. The numbers are too vague and don’t make sense and whatever type of analysis we are doing be it comparative, correlation or impact is not usable. To avoid all this confusion always starts with what we are trying to solve, funnelize your data or branch it out as per your solution.
Always remember to take 2 steps back after taking 1 step inside.
Excuse #2: “I don’t know the correct way to handle my data”.
Solution: There is no such thing as the correct way, it’s a creative field driven by your competencies.
Depending on the organisation, product, teams, data volume and their use of data in their work as well as the type of problems we are trying to solve data handling can differ. The only solution is to increase our competencies and delve deep into product and data. Don’t get stuck into analysis paralysis.
Trending Bot Articles:
Excuse #3: “There is no data which I can actually use.”
Solution: Upgrade data tracking.
Things can get pretty dark, pretty quick while working with data as there was a lot of data to start with but after a bit of observing,slicing and dicing relevant data is very less, required data points are not getting tracked or the sample is too small to draw an inference from. There is no quick fix to this problem and the only solution is to have a collaboration from data, product and engineering teams to upgrade your data tracking.
Excuse #4: “It takes too much time to do the analysis”.
Solution: Define the problem, create frameworks.
This is actually a problem of the outcome-based mindset of stakeholders rather than having process-based. Instead of changing the mindset, we can divide our analysis as per usages like on a daily basis, weekly or monthly basis and create our automation paths accordingly around that for faster responses.
I personally have created comparative, impact and correlation analysis frameworks which comes in very handy after I have defined the problem and created modules as per my inputs to the framework.
Excuse #5: “I don’t have time for this, I know my product and consumers.”
Solution: What you need is an expert.
This is where most of the upper-level management lies, they know everything about their users since they are with them since the first day. But I would like to highlight while starting your product journey intuition is the right way to go, but while scaling intuition does not work and the only logical step is to use data to analyse the users and their behaviour to focus on the right thing moving forward.