I have a “good feeling” about this !


Staring at data is a big part of my job – but it’s very rare that data alone gives me direction on what to do next . Data needs to be put into the context of what I feel (and what others feel) and then some decision gets made . So in reality – I am not really data driven , I am more “data enabled” when it comes to my decision making process .

What I feel – perhaps what can be called my intuition – is based on my past experience . So I often wonder how useful it will be to depend on intuition when it comes to decisions about future . That led me to think about my feelings a little more – and that led me to three (overlapping) possibilities on why I decide to go forward with some decision

1. I like and trust the people who will execute on it

This doesn’t happen unless I know them really well . And amongst the people I know – only a few fall into this category when I think about it more . With such people , I feel strongly that they are so driven that they will make it happen irrespective of challenges I can anticipate . The reality unfortunately is that my success rate is only that of a coin toss . While some data comes into play – it’s really not data driven or data enabled if I am honest about it . I will however add that when everything else is “iffy” – I trust my judgement of people and make bets on it . In such cases – at least so far – it’s been better than coin toss odds for success .

2. I understand it from first principles

These usually turn out to be my best decisions – I understand the problem well from the ground up , and consequently I have a framework to evaluate solutions . All the examples I can think of have ended well – but I am sure there is some bias in my thinking, so let’s say 80% success rate . I can use data to validate my assumptions and mental models – so these are data enabled decisions .

3. I can see the potential tweaks needed to make it work

These are usually things like redesigning the process , having a different leader for the team , resetting the business case etc . I think this is where experience comes in handy – because it’s essentially pattern recognition that is helping me . To increase my odds, I also tap into my network for their experience once I figure out the pattern . Interestingly , this is the category where historic data comes in handy . Quite often – it’s staring at data that gives me a starting hypothesis on what needs to be tweaked .

The time dimension

I try hard to be thoughtful about the decisions I make that have large and/or long term impact . That needs time to deliberate . I conserve my time, energy and brainpower to make such decisions by routinely delegating whatever I can to my team . But even then – a third of the time , I will have to make snap judgments with limited time to deliberate .

As I look back at examples of such decisions – I see an interesting trend . When I have delegated and conserved my time and energy – my snap judgments generally turn out to be ok more often than not .

What is the net net ?

I am convinced that we don’t really need human decision making if it’s purely data driven – such decisions should be automated ( with manual over rides and other precautions on ethics/security etc taken care of ) . Humans (generally) should only have to care about data enabled decisions .

What’s the weakest link here ?

There are two ways to think about data enabled decision making . One is using data to find answers to questions you defined . The other is defining questions based on data . The former is largely a solved problem already . The latter is what keeps us employed 🙂

Published by Vijay Vijayasankar

Son/Husband/Dad/Dog Lover/Engineer. Follow me on twitter @vijayasankarv. These blogs are all my personal views - and not in way related to my employer or past employers

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