Is Penelope Garcia a "Data Scientist"?

Yesterday, while watching an old episode of Criminal Minds, a question came to me: “Is Penelope Garcia a ‘Data Scientist’?” Some people have compared ‘data scientists’ to the CSI, looking at evidence, but I have not seen many ‘data scientists’ using microscopes and scalpels, rather they mainly rely on computers, data, structured or semi-structured...

For those who haven’t watched Criminal Minds, the TV series follows the exploits of the Behavioural Analysis Unit of the FBI, and they solve complicated crimes, from serial killers, kidnappers... through behavioural profiling mainly. Penelope Garcia is the one who uses computers, databases, video feeds... all sorts of data to help catch the criminals from her ‘office of supreme genius’.

She accesses databases, matches and intersects them, conducts searches, runs algorithms on all sorts of data including videos, photos and audio recordings. She is very adept at using huge volumes of data to narrow down the suspect list.

In her back-story, she is described as a hacker. 

I would say that of all the characters in Criminal Minds, she is the one who comes closest to the image many people have of a ‘Data Scientist’, someone who can do amazing things with data.

But she doesn’t work alone; the person she interacts the most with is Derek Morgan.

Derek Morgan is probably the most action oriented member of the BAU team. Often he is the one who provides direction to Penelope, how to use the vast amount of information at her disposal so she can come up with more usable, actionable information and sometimes even where to start looking.

In his back-story, he served in the bomb squad and as a former police officer before joining the BAU.

Not many people would see him as a “Data Scientist”; his knowledge is from his experience on the field. But without his input and the interaction with him, Penelope’s impact would be reduced.

A third member of the team could also be seen as a “Data Scientist”, Dr Spencer Reid. Yes, he has a PhD (more than one actually). His IQ is 187.

Dr Spencer Reid brings a profound knowledge of psychological/sociological theories and statistics/mathematics to the BAU. Using his deep knowledge of these fields and synthesising them, Dr Reid is able to quickly point the team into the most likely areas where the un-sub/suspect is to be found. He also helps Penelope Garcia use her hacking skills more effectively. However, he is quite technology unfriendly, preferring not to use a tablet or email even, favouring books, notebooks and letters.

Who do you think is the “Data Scientist” of the trio?

 If this was put to a vote, I guess most people would pick Penelope Garcia because of the computer skills, coming in second would be Dr Reid because of his theoretical knowledge and IQ, and third would be Derek Morgan.

Makes sense, right?

My view is that, together, they make a good “Data Science” team.

I go back to the Drew Conway inspired diagram; a “data scientist” is a combination of Hacking/Computer science skills (Penelope), Domain Knowledge (Derek) and Stats/Maths (Spencer):

The whole gamut of ‘data science’ functions need not be done by a single individual.

Very often, especially in the case of vendors, it is important to acknowledge that the perfect data scientist is a unicorn, and the solution lies in combining the skills of different people into an effective team. And of course, for the team to function, each team member needs to realise his/her role and respect the expertise of the others, seems to work very well in Criminal Minds.

2 different ways of implementing predictive maintenance could have avoided the issues at Boeing+RollsRoyce and SMRT+LTA

Two incidents are in the news on pieces of equipment breaking down at huge cost. In this world of “Big Data”, I really wonder how such incidents are still so prevalent. However, this post is not just predictive maintenance; what is interesting is that the 2 cases Boeing+Rolls Royce and SMRT+LTA respectively illustrate 2 different worlds and how 2 different approaches should be used to tackle them. Still I would have thought lessons would have been learnt by now. Come on people, predictive maintenance is not rocket science, neither should it be that expensive (the post script briefly addresses the latter issue).

First the case of Boeing+Rolls Royce. ANA, the Japanese airliner, is the largest operator of dreamliners; with 47 (https://en.wikipedia.org/wiki/Boeing_787_Dreamliner#Operators). However, ANA was planning to ground its whole fleet of dreamliners (https://www.theguardian.com/business/2016/sep/01/engine-failures-on-787-dreamliners-prompt-ana-to-refit-entire-fleet) and to replace around 100 Rolls Royce Engines over cracking and corrosion of engine blades. This issue has come to a head in the last couple of weeks (http://247wallst.com/aerospace-defense/2016/08/29/boeing-787-engine-troubles-ground-ana-flights/). 

Rolls Royce will conduct the replacement over 3 years, apparently over the course of normal maintenance (see this article from the Financial Times http://www.ft.com/cms/s/0/be030804-6ec7-11e6-9ac1-1055824ca907.html#axzz4IzJ9bn3a), and the number of flights ANA has cancelled is not as high as originally feared. But ironically, the retrofitting at ANA will take so long precisely because they are such good dreamliner customers.

What does that have to do with predictive maintenance? Well I am sure that Rolls Royce would have tested its blades thoroughly before delivering them for use in commercial aircraft; the responsibility is huge and I have no doubt Rolls Royce and Boeing are serious about safety. But sensors recording every aspect of a complex piece of engineering such as an aeroplane in flight, including the external sensors recording the conditions of the flight show a very different picture of the performance of the aircraft compared to the tests.

I do not know enough about aeroplane engines, but I do expect that if the blades are deteriorating, this should have an effect on fuel consumption, and proper models monitoring health indicators such as changes in fuel consumption given the conditions the aeroplane is operating in should have rung some alarm bells. 

It is not necessary to visually see cracks, they impede performance and this should have been captured and isolated (especially when one of the selling points of the aeroplane is precisely fuel-efficiency (http://travel.cnn.com/explorations/life/whats-so-special-about-Boeing-dreamliner-766616/)

It is even more so in the case of Rolls Royce where the aeroplanes using the engines are flying in vastly differing conditions. While individual airlines might have a smaller dataset, Rolls Royce should be providing predictive maintenance based on the performance of the engines worldwide, a bit like swift should be monitoring attacks against the system worldwide, something individual members would not be able to do as efficiently.( http://www.bloomberg.com/news/articles/2016-05-26/swift-hack-probe-expands-to-up-to-dozen-banks-beyond-bangladesh). 

This ability to pool data and run analysis and predictive maintenance global data it is something I would expect to be a value-added service from someone like Rolls Royce. (But then again, I would also have expected Boeing to be able to track its aeroplanes live, so we do not have issues like MH370 that is still missing http://www.bbc.com/news/world-asia-pacific-26544554 , but we have to wait until 2021 http://www.theverge.com/2016/3/9/11184544/un-flight-tracking-system-malaysia-airlines-mh370). 

A very simple example where a central body collects data to benefit all members is the case of the Credit Bureau in Singapore.

The second case of the lack of predictive maintenance is from SMRT+LTA in Singapore; today is the 4^th day of interrupted train systems (http://www.msn.com/en-sg/news/other/delays-hit-circle-line-for-the-4th-day-in-a-row-smrt-taking-manual-control-of-trains/ar-AAilpv9?li=AAaGkVj&ocid=spartandhp). 

This time, the issues doesn’t seem to be directly related to rails - so said a $10m inquiry (http://news.asiaone.com/news/transport/analysing-train-breakdowns-line-line)- or the trains themselves (http://theindependent.sg/smrt-secretly-shipping-35-prc-made-trains-back-to-china-for-repairs/), but with the communication between the control and the individual trains – the trains affected are driverless – every time the train loses contact with the control, it applies emergency braked, a safety feature. For now, the trains will be manually driven; man saves machine...

SMRT+LTA are saying that the issue is taking so long to resolve because they can only do checks and repairs when they system is shut down.

How could predictive maintenance have helped? Again, sensors are the answer. 

I assume that for driverless vehicles, it is critical to have sensors that are constantly feeding information into the system so it can perform safely. Even before we look at what the signals are, the strength of the signals should be crucial. 

May be there was some over confidence since you can surf at speeds of up to 1Gbps (http://news.asiaone.com/news/singapore/hetnet-trial-mrt-stations-seamless-speed-surfing), but simple monitoring of the quality and variability of the signal should be the basic information required to understand the reliability of the service.

It’s not just predictive maintenance, I think it is crucial to have an understanding of the systems and a ranking of the types of issues that can impact the system, and the collection of data should be done accordingly. You do not simply take “all the data” and “throw it in the machine” and expect to “get all the answers”, especially if you keep having new issues.

The SMRT and LTA have engineers; this human knowledge that they have should be put to good use to design proper monitoring and predictive maintenance so such issues don’t just keep on popping up over and over again. Catch them early so it is more feasible to intervene during down time, all at a minimal cost to the economy.

PS

The idea is that such technologies are terribly expensive. Actually they should not be; it’s all in how the solution is designed. One huge cost is driven by the need to “real-time”. How important is it for the information to be real time? I have had a few clients who wanted information real time, but who would only take action the next day, so effectively it doesn’t matter in terms of the timing of action taken if the information arrives by batch at end of day, or in real time. But it’s cool to be able to show stuff in real time. 

However, emergency activity that requires instant reactions should be on-board, for example the train applying emergency break when signal is lost. Predictive maintenance should alert you way in advance that you are likely to be having signal issues, so you have the time to solve the problem before it occurs.

Also, many of the sensors are already in place, it’s just that the systems were not designed to store the measurements. That need not be a total replacement, but a retro-fit could do in a pinch. And to add to this, storage is getting cheaper and cheaper.

Designing systems to capture the data needed for predictive maintenance (or any other type of analytics for that matter) is a huge topic. But for today, I’ll just say that it’s usually less expensive than thought if the focus is on the minimum requirements that would deliver the results required.