This TED talk really surprised me.
The content was great, but it was Brian Little’s delivery that really made me go “wow!”
So many times during the talk it felt I wasn’t listening to him talk on the subject of “personality” but rather his grandchildren. His passion was evident, and his joy contagious. I couldn’t help but give him a personal standing ovation at the end.
It is with this sort of passion that we should approach our careers; our lives.
I just received a mailer from Effective Altruism, via which I do a monthly donation to charity. The mailer asked me to rate from 1 to 10, with 1 being least likely and 10 being most, how likely I would be to recommend Effective Altruism to a friend. I gave it a 10.
And since we’re all friends here on edonn.com… I recommend Effective Altruism if you’re looking to make your charitable dollar do as much as it can.
Effective Altruism is an organisation that’s, in their own words: about answering one simple question: how can we use our resources to help others the most?
I first learned about them through a book called Doing Good Better (loved it; it absolutely changed the way I thought about giving – especially the part talking about the careers we ought to pick for maximum societal impact: should we pick the higher-paying career where we have little opportunity to positively impact society, e.g. an investment banker; or the lower-paying career where we can make a positive, direct impact on society, e.g. a social worker? The book argues that it is the former that we can do more good, if we direct the funds we earn to charitable causes).
Its basic premise is this: all charitable interventions should be scientifically tested to determine how effective they are, and money should only flow to those that are more effective.
The more good an intervention does for a given amount of money, the more effective it is deemed to be.
How much “good” an intervention does is determined by the amount of QALYs and WALYs. This is a very interesting concept that I’d not heard of before coming across Effective Altruism.
A QALY stands for “quality-adjusted life year”, defined as (from Wikipedia):
[A QALY] is a generic measure of disease burden, including both the quality and the quantity of life lived. It is used in economic evaluation to assess the value for money of medical interventions. One QALY equates to one year in perfect health.
A WALY, on the other hand, stands for “well-being adjusted life year” (from the US Institutes of Health website):
[A WALY] is a measure that combines life extension and health improvement in a single score, reflecting preferences around different types of health gain.
In essence, the amount of good relates to how much life and life improvement it brings. The benefit of of using QALYs and WALYs is that they are fungible, and are therefore able to act as very versatile measures of charitable intervention. A little like good old money.
For example, if you want to take up a new job, it’s extremely convenient to start thinking about the benefits in terms of money, even when some of the benefits are non-monetary. If you get more vacation time, how much more is an extra day of vacation worth to you? If the working hours are less, and you are planning to spend this extra time with your kids, how much more is this worth to you? And so on.
It helps us make apples-to-apples comparisons between two very disparate things, like deworming vs. microfinance.
Effective Altruism thus looks at the quality of all interventions, and aims to focus funds toward interventions that are the most effective. And though it may not be perfect, I find that it gives me peace of mind.
It allowed me to finally get past paralysis by analysis, making me comfortable with giving more money than before.
I still do give to random strangers on the street because it feels good; but for regular and systematic giving, the kind that I think will do far more good, this will be my avenue of choice.
And to those who ask: Is this “too scientific”? Shouldn’t giving be from the heart?
My answer is: No to the first question; and yes to the second.
The science and experimentation behind Effective Altruism helps to ensure accountability – charities that are deemed ineffective tend to be ineffective for very good reasons, and every dollar given to an ineffective charity is one less dollar given to a more effective one. Why should less effective charities, even those with the best of intentions, take money away from those that can do more good?
To be honest, I did have some concerns about how newer interventions or charities would be handled by them – many charities and interventions start out less effective than the most effective ones and need to be given a chance to grow and show their worth, and may eventually become as effective than the most effective ones or even more so. However, Effective Altruism does take care of some of that by having a dedicated allocation of their fund that looks at just these “promising charities”, which introduces a little bit of randomness into their portfolio of current strong performers.
On giving from the heart, to be honest I never really found a “logical” reason for giving, nor have I looked for one. Giving to me has always just been something we should do to be thankful we have what we have, that we are who we are.
Came across this question on Quora today: What are the most subtle ways to deceive people with statistics?
The answers are legendary. Going to be one for the bookmarks.
Scary stuff, but something I think we’re already deeply mired in: Physiognomy’s New Clothes (the new racism, courtesy of machine learning).
Reminds me of the book Weapons of Math Destruction, which also highlighted many important points about the problems with “runaway” algorithms, which not only face the danger of falling into a closed feedback loop (and thus feeding their native biases), but also where the builders of the algorithm are no longer around to ensure the algorithm’s still behaving according to theory and can no longer validate its results qualitatively.
What is more, having worked on many data science projects, I know how easy it is to build models that can be tweaked to say anything I want by just tweaking a couple of parameters.
And let’s just say that models that don’t quite agree with management’s decree don’t always see the light of day.
(Link to article above “Physiognomy’s New Clothes” via the wonderful Marginal Revolution blog, which also highlighted the fact that this was “neglected” — I personally am finding myself increasingly leaning toward the AI doomsayers. The more I know, the more I worry.)
The next few quarters for analytics in my company are, from my perspective, going to be game-changing, and I’m excited to say my team’s taking the lead on it: from machine learning and advanced visualisations to new ways of thinking about data, we’re currently taking the steps to get to what I call “the next phase of analytics”. We are a small team with big dreams.
But what I often get from friends (and some colleagues) when I tell them about the things my team is doing, though, are questions on how “big data” is playing a part in it. Specifically, how it figures in our plans for the next few quarters.
When I tell them it doesn’t, they look at me as if I just said I loved eating broccoli ice-cream: perplexed; a little disgusted; and mixed with a bit of pity on the side. (If you clicked on that link or you know that song, you might have guessed I’m doing that parenting thing.)
“Big data” simply doesn’t factor in those plans (yet). We have enough small data to worry about to even think about big data. And yet, to them small data is yesterday’s news. It’s as if small data doesn’t count; as if it’s nothing to get excited about.
But it does count. And to those who haven’t yet experienced the joys of wringing all the value out of small data, it is downright exciting.
Sure, big data has the potential to change the world, and in many cases it already has. But by and large most of the value of big data still lies in its potential.
Small data, on the other hand, has long shown its ability to change the world.
I love especially this little story from the book mind+machine by Marc Vollenwider:
Using just 800 bits of HR information, an investment bank saved USD 1 million every year, generating an ROI of several thousand percent. How? Banking analysts use a lot of expensive data from databases paid through individual seat licenses. After bonus time in January, the musical chairs game starts and many analyst teams join competitor institutions, at which point the seat license should be canceled. In this case, the process step simply did not happen, as nobody thought about sending the corresponding instructions to the database companies in time. Therefore, the bank kept unnecessarily paying about USD 1 million annually. Why 800 bits? Clearly, whether someone is employed (“1”) or not (“0”) is a binary piece of information called a “bit”. With 800 analysts, the bank had 800 bits of HR information. The anlaytics rule was almost embarrassingly simple: “If no longer employed, send email to terminate the seat license.” All that needed to happen was a simple search for changes in employment status int he employment information from HR.
The amazing thing about this use case is it just required some solid thinking, linking a bit of employment information with the database licenses.
Small data can have big impact.
So yes, I am excited about small data!
And no, big data won’t be part of our coming analytics revolution. (Yet.)
I lead a Sales Operations team, and one of our objectives for this year is to establish a “sales reporting governance structure”: to ensure that the right reports/tools get developed, with the right specifications, at the right time; and, perhaps most importantly, with the buy-in by the right people.
Essentially this governance structure looks at controlling the reporting life cycle (something like this report life cycle diagram) from when a report is dreamt up in the head of one of our business partners (our “internal customers”), through to when the report reaches its EOL (end-of-life) and can be stopped.
Though you may think this is somewhat dry work, let me assure you that it’s often anything but. Conversations can be excruciating quite colourful, particularly when it comes to prioritisation and negotiating timelines.
Take for example the following conversation between one of our business partners (BP) and us:
BP: “What do you mean you can only deliver it next Friday? I need it by Tuesday.”
Us: “Sure, that can be done, but we’ll need to stop work on the other three developments we’re working on for you that are due next Monday.”
BP: “No, you can’t stop work on those. I need those next Monday, and this one by next Tuesday.”
Us: “Sure, but we’ll have to exclude the new functionalities that you’d asked for.”
BP: “No, you can’t do that.”
Us:“I’m sorry but if you’re not able to budge on re-prioritising the other work, nor reducing the scope, there’s no way we can hit the timelines you’re asking for, especially when you’re asking for this so late in the game.”
BP: “I’m escalating this. You’ll hear from my manager.”
And so on.
In all fairness though, I have to say that in my experience most managers and senior colleagues (and anyone who has worked in, or closely with, IT) tend to understand that we have to satisfy ourselves with but 24 hours a day to do all we need to do.
These sort of escalations tend to end with “the manager” having a cordial chat with us and agreeing on a workable next step forward, none of which involves us engineering more time into the day.
Having a governance structure tends to minimise “unconstructive” conversations like those above, I think largely because of a mutual trust: the business partner trusts our verdict of whether something is possible or not impossible within a specific time frame, while we trust that they have thought carefully through their requests and won’t be changing or adding to them unnecessarily.
But the problem with establishing a governance structure is that it, well, needs to be established, which can be incredibly tricky to get going. It’s almost like an negotiating a peace deal, where both sides want the conflict to stop, but are worried what might happen the moment they lay down their arms — will the other side take advantage and strike when they are at their most vulnerable?
I will be the first to admit that it takes a leap of faith going from a world of “if I don’t shout loud enough, and often enough, nothing’s going to get done”, to one where we’re all amicably setting and agreeing on priorities, and where promised delivery deadlines are actually being met.
It also doesn’t help that from a developer’s side, without the benefit of having past projects to tune one’s intuition, accurately estimating project scope or determining deadlines is going to be difficult; often multiple iterations are necessary before this sort of “accuracy” is achieved. What this means is that early on, chances are good deadlines are going to be missed, which doesn’t help in building trust.
After a missed deadline or two, it’s all too easy to fall back into old patterns and proclaim that the process doesn’t work.
There will also be many, especially those more used to the “free-and-easy” days of yore, who will actively fight the change, citing that it creates too much red tape and jumping through hoops to get things done.
“We need to establish our reporting as soon as possible or we’ll just be flying blind — we can’t afford to go through this process!”
But the thing is, we often can’t afford not to.
When the number of development projects are small, I have to agree that the process, this “bureaucracy”, adds little value. We could simply get on a phone call, or write an e-mail, and agree among ourselves what needs to be done and when. If the requirement changes, no biggie, we simply tweak until its perfect – there’s sufficient slack in the system that will enable us to do just that.
But problems will occur when the number of projects starts to creep up, and more stakeholders are introduced.
The first problem is that due to the higher workload, the slack in the system that allowed for changes in between a development cycle will be gone. This means that changes or additions to the original requirement will likely have to be parked until development time opens up, which could be weeks down the road.
Business partners are not going to like that. “It’s a simple change for God’s sake!”
The thing is, no matter how small a change is, it’s going to be work. Somebody’s got to do it, and that means time out from other projects, which also have agreed timelines. If we focus on that change now, it risks jeopardising the timelines for every other project down the line.
If the change is important enough, then maybe we can take time out from another project and put it into executing the change. But it needs to be agreed by the team owning the other project. Which leads nicely to the second problem.
The second problem is that everyone will have their own agendas, and everyone’s pet project will be “of the highest priority”.
What happens when Team A, B, and C all have “high priority projects” that need to be done by next Monday, and development team only has the capacity to complete one or two? Without a proper process or governance structure, can we guarantee that the project of the highest priority for the business will be one that’s completed?
In the end, more time will be spent explaining to each of the stakeholders why their project was not completed; people will be upset, and the next time they’ll just be sure to shout all the louder, and all the more frequently. More time will be spent on meetings and e-mails, people “ensuring” this and that and never really ensuring anything at all. Estimated delivery dates will be given, but nobody would trust them because they know someone else coming in with an “urgent” request would likely take priority. If it’s “last in, first out”, why should I raise a request early only to be relegated down to the bottom of the delivery pile?
This just struck me as very analogous to the concept of bribery, which I was reminded of on my reading of the book Treasure Islands by Nicholas Shaxson:
Some argue that bribery is ‘efficient’ because it helps people get around bureaucratic obstacles, and get things done. Bribery is efficient in that very narrow sense. But consider whether a system plagued by bribery is efficient and the answer is the exact opposite. [Bribery undermines] the rules, systems, and institutions that promote the public good, and they undermine our faith in those rules.
Despite any short-term drawbacks, there are plenty of longer-term benefits, not least that of supporting stronger surrounding report development structures and a generally healthier culture.
Though setting up the governance structure thus far has been tough, with plenty of push-back and many of our business partners trying to circumvent the process we have established, I think it’s one of the most important things we can, and have ever attempted, to do.
Came across a very interesting and persuasive video on baseball via Kottke.org today. It’s a great example of what an interesting question, effective visualisation, and some statistical knowledge can do.
The question the video seeks to answer is the following: what would happen if baseball player Barry Bonds, who happened to play one of his greatest (if not the greatest) baseball seasons ever in 2004, played without a baseball bat?
I’m not a baseball fan, and frankly quite a number of the things that were mentioned in the video were lost on me. But I’m a fan of interesting statistics and great visualisations, and this definitely had both.
And despite having a few doubts at its conclusion (the results seem too good to be true – watch to the end!), it is convincing and definitely worth a watch if you’re either into baseball or statistical visualisations.
