Normalisation

I’ve always thought, and still think, that I do not have an edge in my function in the company.

I do not have a masters.

I do not have an engineering degree.

I do not have much training on statistics, beyond junior college math and one statistics module I took in university.

I’m basically a minority, to a large extent, in terms of education background and to a smaller extent, nationality and cultural background.

Sometimes when people start talking about algorithms, my mind goes blank because I do not understand what the heck is going on.

I take a longer time now to pick up new software to run data, compared to my younger days and to my counterparts at work (I think).

Increasingly our jobs require us to multi-faceted and multi-skilled. We need to be able to understand big data. We need to be insightful. We need to link the dots. We need to be famous for something.

The course work over the past weekend really blew my mind.

It was so difficult for me to do.

I was supposed to simulate a marketing campaign on digital via search to drive sales of cameras.

With each round of simulation, I got more data that told me how well each component performed.

After three rounds, my results only improved marginally and the only area where my campaign was strong in, was ROI. 😂

Revenue and profit were so-so.

I wonder if I’d ever be able to brain this complex world of digital marketing.

Today, one of my juniors in the company approached me. She was facing some challenges trying to present and explain normalised data to her team.

How to explain normalisation? She asked.

I broke it down.

1. Normalization is done to remove biases in the data so you understand what is truly differentiated.

2. There are 2 types of biases we need to remove: I) Higher scores due to bigger brand effect and II) Respondent bias towards the attributes.

She told me this was the clearest explanation she’s heard. She’d asked around to find out how others in our functions explain it and she’d also studied the methodology in detail.. but it was very hard to explain in layman’s terms.

I was surprised, because I genuinely thought that XX or YY, ie the really statistically and technically savvy folks in the function, would be able to do it way better than me.

I learned about normalisation in my earlier days in the organisation. It was hard ploughing through the methodology and explanation, but I finally managed to understand it in my own way, similar to how I figured out factorisation in secondary school by drawing imaginary arrows. 😂

I guess it takes a layman to explain things in layman’s terms! 😂😂

It’s little things like this that make me question my abilities a little less.

It’s just so hard to find a breakthrough.

Heading home to my yoghurt-loving girls!

Last night, Clarissa very thoughtfully stuffed a piece of tissue paper into Allie’s singlet to function as a bib, and also fed her the yoghurt.. while Allie had fun trying to feed herself and smearing yoghurt everywhere.

Hehe I love my yoghurt monsters!