Why we should continue believing that every company needs to leverage on data analytics

For someone who had a glimpse of the peril and promise of data analytics, I still have hope and aspiration that companies within public and private sectors in Malaysia should believe in the benefits of using data analytics to help solve their business problems and making it a priority. After returning from the US thinking that Malaysia is still far behind, I was not entirely correct. There is awareness certainly. More and more companies are jumping in the bandwagon to try and do it. The word “big data”, “analytics”, “machine learning” or “AI” pops up in multiple pages of the public listed annual reports.

However, I have to say that sometimes my belief/faith in the future of this too sometimes got tested. Firstly, because of the quality of the data and the state of data infrastructure that the companies have require a lot of massaging and improvement. Secondly, the lack of talent in this area. Thirdly and most importantly, lack of belief or buy in from the CEOs/senior management to enforce the execution and thus provide the right support/environment.

Hence, we need more advocates by the leaders in Malaysia, especially from the ones who believe in the promise of data analytics, as rightly captured by the article from the recent Future of Work conference. You can read it here. Mad at myself for not attending that conference.


Resistance to new tech

A colleague of mine shared this great article about how workers resist new tech at the office. The article painted the reality on why is it so difficult for some companies to innovate. I personally could relate to it as I see some resemblance in some of the people in my organization, across all levels – juniors, mid-management, senior management.

While some of them do appreciate new technology, they want to take the time to understand first before they can use it or encourage other people to use it. I thought that’s fair. However, in the midst of your day-to-day work, when will you ever have the time or make time to learn how to use the new tools. And as a result, does the organization be on a stand still and wait for everyone to learn how to use it before we can enforce them to use it?

I don’t think so. That’s the recipe of being left behind.

AI in Microsoft PowerPoint

Just came back from my fil’s 65th surprise birthday party. Phew, what an eventful day! Husband and I actually spent half a day producing a slideshow video for him. It was intense. We were afraid that we were not able to finish it on time. But we made it nonetheless!

Thanks to Microsoft PowerPoint. I wanted to use Windows Story Remix (previously known as Windows Movie Maker which I’m very familiar with) but the software kept crashing on me. Thank god I decided to use Microsoft PowerPoint. And I was really blown by the advancement in Microsoft’s Office Intelligence service. I could easily insert 5 pictures on 1 slide and Microsoft PowerPoint would recommend a few designs! I didn’t need to arrange the pictures myself to make a collage. It also recommended how I can position the title of each slide. Very very impressive.

This is the power of AI! Of course there’s still a lot of improvement to be made – for example, it doesn’t know what to recommend when I combine both pictures and videos in one slide. But still..when more people use it, it will remember and then know what to recommend. You should try it!

Here’s a picture of me and my family at the birthday party.

I guess you can never go wrong with Black and Gold!

Ghost Work behind the Future of Work

If you are into AI and stuffs, you can hardly dismiss the term “Future of Work”. It’s a concern (some feels it’s rational, some feels it’s irrational) on how automation could sweep away some, if not all, of the low-middle class jobs. This topic itself requires a series of dedicated posts but I just want to highlight a sub-topic related to this because I’m seeing more and more headlines on this – the Ghost Work, in laymen terms, the invisible labor.

Have you ever wondered how much of manual work is required before you have sufficient data to wrangle? Have you ever wondered the amount of labor work is being poured to digitalise data? You need humans to capture images in old albums before you can upload the pictures online, you need humans to label each image (whether this image is a cat or not, for example), you basically need humans to perform lots of labelling, classifying and much more before you can perform the sexy work which involves machine learning, deep learning, data science etc.

Some of the articles highlighting this:

  1. Inmates in Finland are training AI as part of prison labor
  2. A white-collar sweatshop: Google Assistant contractors allege wage theft
  3. The AI gig economy is coming for you 

There’s also a book dedicated to this topic called “Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass”.

Yes, there are jobs that will be replaced by automation. But before we get to the stead state of automation, we need a lot of labor work that are mineal, i.e. does not require a lot of mental thought, if fact probably nil. This type of jobs will still be around, the question is who is willing to do it if we are all emphasizing the sexy work.

Automation on MarketWatch

I was reading the news on the financial markets and clicked on the news by MarketWatch. As I was reading the market update, I then suddenly realized that all the market performance inserted in between the words, is LIVE.


The above is just a screenshot to give you a flavour of what I’m talking about. But if you were to read it when the market’s opened, the figures actually changed automatically and instantaneously. You can read the whole news here. That is cool. But I wonder who or how does the text change if say for example the movements turn from negative to positive (i.e. from red to green), because the narrative has to change too. Is that automated too?

What project to choose for machine learning

It’s interesting how Andrew Ng explained in a simple manner on what machine learning can do and cannot do in his “AI for Everyone” course that I’m currently taking. I think it helps me at least to think of the projects that can be tested for machine learning.

Rather than listing down all the problems we have (in an organization), think of the activities/tasks that do not require you to think by more than 1 second to do/decide. This is called ‘simple concept’ tasks which do not need a lot of mental thought especially the ones that you are currently doing manually. This can be replaced by computers – machine learning can help cut down your manual work. Provided, you have enough data to supply – in terms of volume, richness and completeness (i.e. there’s input and output).

Try it and start cracking your head to list down all the relevant tasks.

By doing so, we can start doing pilot projects and assess whether it’s feasible to continue in a bigger scale. The idea is to execute multiple projects in a year. According to Andrew, implementing 1 AI project in 1 year is extremely long. We need to do more than that to speed up our learning process.

AI for everyone on Coursera

Early last month I mentioned about “AI for Everyone” course on Coursera, taught by Andrew Ng. I advocated to do it, but I myself haven’t signed up since, until today. So I just did.

After I signed up, this message appeared (see bottom right of the picture below):


What a clever way to get me started immediately. So I watched the first video, i.e. the introduction. To my surprise, Coursera platform has improved tremendously, the last time I took a course on Coursera was a few years ago (didn’t finish it and I can’t even remember the name of the course). Here’s what I found extremely useful, so far:

  1. Transcript of each video is readily available, below each video. So if you miss some of the words the lecturer said, you can just read the transcript, instead of replaying the video multiple times.
  2. You can also easily save any parts of the video as part of note taking. And you can easily replay the saved parts and the transcript is automatically downloaded as well. So you can choose to replay or read the transcript. All you need to do is just press the “SAVE NOTE” button. See below.


The notes will appear as below:


See how Coursera has made our lives easy just to encourage us to learn (and use their platform)?


Do you care that Google knows what you buy?

Do you know that Google knows what you buy? Or rather, Google tracks what you buy. Not entirely everything because some purchases were made by cash, some by online banking, some by credit card, as long as the receipts don’t get sent to your email, Google won’t be able to capture those spending. So it’s only receipts, invoices or any type of purchase acknowledgments that are emailed to you will be tracked by Google.

Click here to see your purchase history to see what Google knows about what you buy:


Here’s a screenshot of my purchase history:

Google gets this because I bought the ebooks via amazon.com and all receipts were sent to my gmail.

According to this article by CNBC,  Google told them that customers/users can turn off the tracking entirely but it’s not as straightforward as it seems and when CNBC tried to do it, there was no such option.

Do I care if Google knows what I buy? For the time being, no as I’m not getting any negative side effects. If anything, I feel that page of Purchases on my Gmail account is a useful summary for me.

Facebook opening data up will pave the way for other corporations to follow suit in a more legal and ethical manner

“Facebook will open its data up to academics to see how it impacts elections”

The headline above seen in MIT Technology Review twitter feed definitely caught my attention as it was timely and related to my post yesterday.

So last week Facebook announced the first researchers who will have access to Facebook’s privacy-protected data as part of its role to promote independent research on social media’s role on elections. You can read the announcement here. Basically, Facebook wants to correct the world’s perceptions on them that their existence makes the world a better place, they do not misuse or allow third parties unknowingly misuse their biggest asset which is the users data.

I applaud this initiative, ignoring any political agenda behind it, if there is. This will actually set the foundation/framework on data sharing because Facebook aims to do it by “ensuring that privacy is preserved and information kept secure” and that it “acts in accordance with its legal and ethical obligations to the people who use their service”. Whatever they intend to do, they would not compromise people’s privacy. According to the announcement, Facebook has “consulted some of the country’s leading external privacy advisors and the Social Science One privacy committee for recommendations on how best to ensure the privacy of the data sets shared and have rigorously tested their infrastructure to make sure it is secure.

What’s interesting to me is they are building a process to remove personal identifiable information (“PII”) from the data set and specifically testing the application of differential privacy, an increasingly used innovative method of anonymising data which is a machine learning technique based on neural networks. In ODI’s report on Anonymisation and Open Data, differential privacy is defined as follows:

Differential privacy is a property of data systems that allows collection of aggregated statistics about a dataset but obfuscates individual records. When queried, a small amount of noise is added to the data such that if any one record were removed, the query result would stay the same. This means those using the data can never be entirely certain about any single person’s data.

If this is deemed successful, this will actually pave the way for other corporations specifically the traditional ones who are sitting on customers data to have the comfort of sharing privacy-protected data to external parties to harness the power of big data. The biggest challenge is to get the traditional lawyers, CEOs, senior management understand that anonymised data is NOT personal data.

Data Trust

Stumbled upon an article on FT on New Institutions are Needed for Digital Age which mentioned that “Open Data Institute and others are exploring data trusts — where control over data-sharing is transferred to an independent third party, legally bound to ensure its use for a defined purpose”. This then led me to a report published recently by Open Data Institute on data trusts which summarizes the framework and findings from their first in-depth study on role of data trusts.

While the concept/term may not be new to us, the interest/appetite is definitely growing among government and large corporations wanting to create more value through data sharing without worrying about privacy issues. Below is the screenshot of data trust framework for your easy reference.

data trust.PNG

Their definition of data trust is independent of technology architectures – centralized or decentralized platform, cloud hosting, blockchain or not doesn’t matter – as long as it is technically flexible to our changing needs.

If you are interested, you can read more about it here and the report here.

A Must Watch to Understand the Big Picture of AI


A Ted Talk by Kai-Fu Lee, the AI expert, also my new found “love” on geeks/experts which caused me to swing from my current read to his autobiography “My Journey into AI”. Read my previous post.

After you watch this video, you will understand better the importance of learning and embracing AI because AI will be embedded in our lives much more in the next 10-15 years or even 5 years depending on the level of sophistication. I feel that it needs to be in one of the syllabus of students curriculum.

How Today’s Readings Led Me to AI Superpowers

I love to reflect the journey on how I discovered things (which include ideas, theories, although most of the time people) that enlighten me, inspire me, engage me, which then led me or connect me to another dot, especially when I landed on someone successful that made me really curious about him/her, made me believe in him/her; essentially follow him/her online just to go through his/her mind/work/everyday lives. The latter sounds like I’m a stalker, but you get my point right? For example, how I ended up admiring Fred Wilson which then led me to starting this blog, how I ended up admiring Melinda Gates for her work on data and women empowerment, and many many more. I actually have a list.

Today, I discovered another person that will be in my “stalking” list. He is Kai-Fu Lee, a Taiwanese VC and most importantly an AI expert. In his capacity as an AI expert, he wrote a book called AI Superpowers where he focuses on how AI will save humanity and how humans can take advantage of AI instead of risking our jobs to AI.

So how I discovered him? It’s through The Algorithm newsletter I subscribed because I enjoy reading Karen Hao’s views (MIT Technology Review journalist). In this week’s newsletter she mentioned about how TikTok, the upcoming and rising social media platform (from China) is replacing our free will with algorithms. I got really curious about this famous app after learning that Andreessen Horowitz thinks this app is unique as it is the first AI consumer-based app. Even people in the US is crazy about it and it has 500m users already! I’m trying to understand the difference that this app offers vs. Other social media platforms which leverage on AI to curate what customers wants/needs. Apparently for TikTok, the product itself is based on AI. Anyway this is another story for another day.

From the newsletter, I clicked on Andreessen Horowitz’s blog to read about their take on TikTok and the rise of AI-based consumer apps. You can read it here. At the end of his post, the general partner of Andreessen Horowitz mentioned about the book called AI Superpowers, written by Kai-Fu Lee. Googled about it and found the author’s website and got immediately hooked.

Apart from his in-depth knowledge on AI, he also shared a quiz for us to take to see if our job is at risk of being replaced by AI and learn about our own human superpowers so that we can thrive in the future. I thought it was a great quiz and would highly recommend everyone to take it. Here’s my results:


The assessment about me is actually quite accurate I would say. And apparently it says I have a spontaneous personality which possesses characteristics that AI can’t imitate. See below.

100% agreed except for the communication part which I’m still working on it.

Anyway, that’s the story of how I came across AI superpowers and the author. It’s definitely going to be in my to-read list.