Analytics Innovation, Issue 7

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A N A L Y T I C S

ANALYTICS INNOVATION AI and the Future of Cinema /6

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Data Analytics Could Help Prevent Police Shootings

AI Needs Emotional Intelligence Or We Risk Annihilation

Everyday we see a new unarmed teen die at the hands of police, a problem which has led to riots across the US. The problem is complex, but analytics could be part of the solution /12

One of the major challenges facing the human race in the near future will be how we deal with AI. If we are to stand any chance, we’re going to need to imbue robots with a key human trait empathy /15

SEPT 2016 | #7

Turn Data into Competitive Advantage

Big Data & Analytics Innovation Summit November 16 & 17 2016 | London Speakers Include

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ISSUE 7

EDITOR’S LETTER Welcome to the 7th Edition of the Analytics Innovation Magazine

In a recent PwC survey of senior business leaders at 1800 large companies in North America and Europe, just 4% were classified as being ‘data elites’ and successfully using data to improve business performance. More than a quarter reported seeing ‘no or little benefit’ from their data initiatives. In another study of three-dozen companies who have put in place major analytics programs, EY found that a mere third met the objectives of their analytics initiatives over the long term.

The blame for this failure lies with leadership. For a data initiative to succeed, there needs to be a culture in place whereby data is at the heart of all decision making, and it is the responsibility of the C-suite to ensure that this is implemented. We talk to Joel Shapiro, Executive Director of the Program on Data Analytics at Kellogg’s School of Management at Northwestern University, about why they are so important later in this issue.

Ultimately, the problem is that there is still significant confusion in the C-suite around whose responsibility

analytics programs actually are, and a power struggle around ownership of the projects. And this is not just the case at corporations, it is also true in the public sphere. We have seen President Obama use analytics heavily from his first presidential campaign, applying it across all areas of government as the technology has advanced exponentially under his time in charge of the US. But will prospective presidents Donald Trump and Hillary Clinton continue his good work? We look at who would is the best candidate for driving government data initiatives later in this issue.

systems, and greater buy-in at an executive level. Using the wealth of data being collected is always going to be hard, the question is whether it is enough to be trying to be data driven, or whether complete overhaul of business structures if necessary for data initiatives to be successful.

The reality is that it is different for every organization, but as much understanding of the trends and processes as is possible will put decisions makers in the best possible position for knowing what strategy will work to see their data initiatives succeed, and which will not.

Also later in this magazine, we look at how the US police force is using analytics to try and put an end to the scourge of unarmed shootings.

The police is struggling to overcome many of the major obstacles that other government agencies suffer from, as well as large incumbent companies such as the major retail banks. The companies that have most successfully used data in their operations have been the major tech firms who have been set up to be data—driven from the very start, with fewer silos, legacy

As always, if you have any comment on the magazine or you want to submit an article, please contact me at jovenden@theiegroup.com

James Ovenden managing editor

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smarter faster better banking Big Data & Analytics for Banking Summit December 7 & 8 2016 | New York Speakers Include

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contents 6 | AI AND THE FUTURE OF CINEMA

Artificial Intelligence may seem totally incompatible with the creative process, yet many are exploring its uses in writing films - to varying degrees of success 10 | WILL THE NEXT PRESIDENT RUN OBAMA’S DATA INITIATIVES INTO THE GROUND?

The last decade has seen data analytics explode, and President Obama has put the science at the heart of government policy.

But will this continue under a new Commander-in-chief?

12 | DATA ANALYTICS COULD HELP END POLICE SHOOTINGS

Everyday we see a new unarmed teen die at the hands of police, a problem which has led to riots across the US. The problem is complex, but analytics could be part of the solution 15 | AI NEEDS EMOTIONAL INTELLIGENCE OR WE RISK ANNIHILATION

One of the major challenges facing the human race in the near future will be how we deal with AI. If we are to stand any chance, we’re going to need to imbue robots with a key human trait - empathy

18 | WHY ARE MORE COMPANIES NOT ADOPTING PRESCRIPTIVE ANALYTICS?

Organizations are now using predictive analytics more than ever, but more should be looking to take things further by adopting prescriptive analytics 20 | MAJOR MILITARY POWERS NEED TO INVEST IN ANALYTICS, NOT NUCLEAR BOMBS

The nuclear debate shows no sign of dying. With the UK’s Trident program up for renewal, we ask whether investment in analytics is now more beneficial to armies than a nuclear deterrent 22 | INTERVIEW WITH JOEL SHAPIRO, Executive Director of the Program on Data Analytics at Kellogg’s School of Management at Northwestern University

We sit down with Joel to discuss the future of analytics and the importance of having a data-driven culture WRITE FOR US

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| assistant editor george hill creative director chelsea carpenter | contributors meg rimmer, alex collis, alex lane, managing editor james ovenden david barton, olivia timson

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AI And The Future Of Cinema Some reviewers have called it dreamlike, but really it’s only interesting in that it has been created by AI

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James Ovenden, Managing Editor

Hollywood has been pumping out movies about AI for years, from Fritz Lang’s Metropolis in 1927 to Alex Garland’s Ex Machina in 2015.

Present throughout the majority of these is an undercurrent of fear and paranoia around the potential for AI to tear apart the fabric of society, a fear that many dismiss as bloody minded sensationalism. However, just because you’re paranoid, doesn’t mean they’re not after you. The negative impacts of AI that filmmakers have presented on screen likely seemed all too distant to them, taking the jobs of factory workers, not the creative brains that machines could never hope to emulate. Those that thought as much may find their complacency misplaced. AI is now being used to write scripts, and it seems that even the movie industry may not be as insulated from the coming AI tornado set to tear through the world of work after all.

Sunspring is a short sci-fi movie from the minds of director Oscar Sharp and AI researcher at New York University, Ross Goodwin, made for the annual film festival, Sci-Fi London. Although, to say it’s from their minds may be stretching it somewhat, as it was actually written by a machine called Benjamin, an LSTM (long short-term memory) recurrent neural network.

An LSTM recurrent neural network is a form of AI used most commonly for text recognition. Goodwin decided to use an LSTM algorithm because it has the ability to sample much longer strings of letters and can therefore predict whole paragraphs as opposed to sentence fragments. Most importantly, it can generate original sentences as opposed to merely copying and pasting.

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In order to train Benjamin to create the science fction masterpiece they were looking for, Goodwin fed a number of classic sci-fi screenplays from the 1980s and 90s into the network.

Benjamin then analyzed these, looking for patterns so it could teach itself which letters and words tended to occur together so it could imitate the structure of a screenplay, replete with stage directions, prompts, and dialogue. Sunspring stars Thomas Middleditch (Silicon Valley, Wolf of Wall Street) as a character called H, alongside Elisabeth Gray, who plays a character called H2. Humphrey Ker plays C, who seems to be the third wheel in a love triangle, although frankly the dialogue is so nonsensical that it’s impossible to tell. I would love to describe the plot, but it’s essentially nonsense. It starts strongly, with three characters - two men and one women - in what appears to be a hipster office. H, opens with the lines, ‘In a future with mass unemployment, young people are forced to sell blood,’ before pulling a book out of a drawer and thumbing through it. ‘It’s something I could do,’ he continues.

This is a strong declarative opening that sets up what could be an interesting premise. Maybe if they’d stopped using AI there, they could have seen it through, but unfortunately they persisted. One of the men spits out an eye ball at one stage and continues about his business as if nothing has happened, but other than that it’s essentially three people in a room spouting words seemingly at

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random. The chaotic nature of the dialogue stretches the use of intelligence in artificial intelligence to its limit. Sharp and Goodwin would probably have had more success if they’d just pulled words out of a hat. Some reviewers have called it dreamlike, but really it’s only interesting in that it has been created by AI. If it had been created by a human, the script would have been laughed at. One of the judges of the Sci-Fi London contest, Pat Cadigan, put it best, saying: ‘I’ll give them top marks if they promise never to do this again.’

Despite its flaws, it was still an extraordinary achievement, although there was still a significant amount of human input involved, from feeding in the scripts, to the actors interpreting the lines. To say Benjamin created a truly original screenplay alone would also be wrong, as it is still based on what other people have written. Theoretically, moving on from this, the project could be taken far further and likely create a far better script were the AI to learn from real world conversations and incorporate things it picks up in real life, as well as the formalities of script structure.

Any device with a microphone that picks up speech based commands can become what is essentially an intelligent discovery system that can be used to understand the workings of the human mind, so there is huge potential for AI to learn a sufficient amount about humans to create a script indistinguishable to that written by a person.

In his novel Galápagos, in which he wrote about a fictional financial crisis which he blamed on the human brain, Kurt Vonnegut wrote about ‘that mystifying enthusiasm a million years ago for turning over as many human activities as possible to machinery: What could that have been but yet another acknowledgement by people that their brains were no damn good?’ Could a machine gives us a coherent film that gives us an insight into humanity and can touch the audience in its searing beauty?

Machines’ ability to do everything better than us suggests that at one point in time, they likely will be. At the moment, however, the technology is still in its infancy, and it is likely to be many years before we see AI capable of producing an original screenplay that can really compete with one created by a person. As Benjamin himself noted of its own application of AI in an interview at the film festival: ‘It’s a bit sudden. I was thinking of the spirit of the men who found me and the children who were all manipulated and full of children. I was worried about my command. I was the scientist of the Holy Ghost.’

Image Credit: http://www.ign.com/videos/2015/01/23/ex-machina-killer-dance-moves-clip

Analytics Festival november 29 & 30 2016, Chicago

stages include Predictive Analytics Innovation Summit Business Intelligence Innovation Summit HR & Workforce Analytics Innovation Summit Social Media & Web Analytics Innovation Summit headliners

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The news that the City of Boston’s Analytics Manager Kelly Jin has taken up a yet-to-be-named data science position in the White House means that President Obama’s track record of scooping up the nation’s best talent continues. Jin will join White House Chief Data Scientist DJ Patil, Director of the White House Police Data Initiative Lynn Overmann and Senior Advisor for Health IT Policy Claudia Williams.

Will The Next President Run Obama’s Data Initiatives Into The Ground?

Olivia Timson, Marketing Analytics Expert

The Obama presidency, whatever its failings, has always shown itself committed to data. From his very first election campaign, when he tied John McCain in knots with his data-driven ground game, Obama never seems to have lost faith, with numerous datadriven initiatives implemented since to solve a number of the country’s problems. / 10

Perhaps the most important of these was the executive order making open and machine-readable data the new default for government information.

Signed on May 9, 2013, the order ensures that information about all government operations is readily available to anyone who needs it, something vital to ensuring an efficient and transparent government, while Image Credit: ChameleonsEye / Shutterstock.com

Digitally connected devices are capable of creating a connected society, and in a result, we may have cities and countries, where almost everything is digital

also enabling significant opportunities for innovation.

In the last month alone, the White House has announced two major data-led initiatives. These include a new program to try and reduce the local jail population with the ‘DataDriven Justice Initiative’ (DDJI). The US operates the largest prison system in the world. With just 5% of the global population, it accounts for almost 25% of all incarcerated individuals.

The DDJI will see the government partner with seven states and 60 municipalities to use algorithms to get any nonviolent offenders who don’t pose a risk to the community out of the overcrowded prison system.

Various federal agencies will work alongside private enterprises such as Amazon Web Services to help states and municipalities to help them improve their data-sharing and analytics efforts, helping to save vast amounts of taxpayer money and preventing people potentially turning violent in prison.

Another recent addition to the White House’s stable of data programs that Jin will be working on is President Obama’s Precision Medicine Initiative (PMI). A major part of PMI is the newly launched PMI Cohort project, which it was recently announced will receive $55 million from The National Institutes of Health in fiscal year 2016. The PMI Cohort Program aims to establish a database of 1 million or more US volunteers with the intention of bettering our ability to prevent and treat disease based on individual differences in lifestyle, environment and genetics. It will establish a database of the volunteers’ information, sequencing data and medical records and cross-referencing it with information about the patients’ cells, proteins, and metabolites, to establish what NIH Director Francis S. Collins, M.D., Ph.D said would be an ‘unprecedented resource for researchers working to understand all of the factors that influence health and disease.’

Image Credit: vector_brothers / Shutterstock.com

Big Data is a phenomena that has only really come into its own during Obama’s tenure, which makes it difficult to compare him to previous presidents or say what would have happened if, say, McCain had won in 2008. However, his reign is now coming to an end. Will his successor also realize the same potential of data to correct society’s ills?

Obama has already bequeathed much of his data staff to Clinton for her campaign, with Elan Kriegel, who ran data analytics for Obama, doing the same job for Clinton, and Obama pollster Joel Benenson now her chief strategist. She also has the enviable list of 20 million email addresses Obama used to target potential votes which she can now use with her own campaign materials. Given her acknowledgment of analytics as a useful tool, it is highly likely that she would continue to invest heavily, or at least continue Obama’s good work. Donald Trump, on the other hand, appears to have eschewed data in his campaign in favor of loudly shouting emotive, and, more often than not, factually incorrect slogans.

He has previously dismissed political data operations as ‘overrated,’ and his campaign last year rejected a pitch from analytics giant Cambridge because it believed that the company charges too much for what it provides, according to two operatives who worked with the campaign. Such an attitude does not bode well for data analytics in the eventuality that Trump were to win. In fact, it probably doesn’t bode well for any technology at all. If he wins without having used analytics, you can likely assume the whole data analytics practice will be swiftly assigned to the dust bin.

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data analytics coul d help end po l i c e s hooting s

In 2015, 990 people were shot dead by police in the US. Of these, 102 were unarmed black people.

It feels like every week a new shocking video appears online depicting police officers showing a callous disregard for life. This has resulted in widespread civil unrest, protests, and killings of policemen in the streets.

Ending this cycle of violence is to the benefit of everyone, and it could be that police have a new weapon in their attempts to, hopefully, ensure that the issue never raises its ugly head again.

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One tool that has long been at the police’s disposal that could be of benefit to this cause is data analytics.

Preventing crime before it happens has long been the aim of law enforcement, and a number of police forces are using the wealth of data around criminals and their activity to predict potential hotspots so that they can allocate resources accordingly. The LAPD, for example, has applied an algorithm used to predict aftershocks during earthquakes to crime, feeding it with crime data

Image Credit: Rena Schild / Shutterstock.com

Meg Rimmer, Data Commentator

to establish patterns. According to Rayid Ghani, director of the Center for Data Science and Public Policy at the University of Chicago, the police can also apply similar analytics techniques to preventing violent incidents of their own making,

stop reports, officer body camera footage, and community survey.

Such analysis could potentially have a profound impact on reducing the number of shootings, as many of them are the result of such traffic stops not being dealt with correctly.

Police departments collect a tremendous amount of data through complaints, dispatch logs, incident reports, and so forth. Ghani’s idea is to take this data and use it to help police departments predict which officers are at risk of adverse incidents. The current system we have is reactive, and it’s very clearly not working. Far better to focusing on, “Can I detect these things early? And if I can detect them early, can I direct intervention to them — training, counseling.”’

Among Stanford’s findings were that African American men are four times more likely to be searched than whites during a traffic stop. African Americans were also more likely to be handcuffed, even if they did not ultimately get arrested. Officers also brought up the subject of parole or probation more often when they stopped black people, and were far more likely to mention the reason for a stop to a white person. The report made specific recommendations for police agencies to consider, including increasing the amount of data collection, as well as better focusing efforts on altering the mindsets, policies, and systems in law enforcement that are the driving force behind racial abuse.

The most important thing with these algorithms, as it is with all of those being used to predict crimes, is the elimination of bias. Data mining looks for patterns in data. When it comes to crime, race is represented disproportionately in the data fed into a data-mining algorithm, which could lead the algorithm to infer that race is the determining factor, whereas it is actually poverty. It is the same when it comes to fighting police misconduct, as much of it goes unreported and there are many fake claims it is impossible to get clean data.

Eliminating such bias is vital if you’re going to get a real idea of the problem.

There have been a number of other initiatives that have looked to help combat police misconduct. The city of Indianapolis, for one, has partnered with Code for America to launch Project Comport — an open-data platform for sharing information on complaints and use of force incidents that should make the kind of analysis Ghani is talking about far easier to carry out. Stanford University has also carried out big data research into police conduct toward African Americans in traffic and pedestrian stops in Oakland, finding a huge disparity in the way they were treated compared to other candidates.

Stanford researchers analyzed 28,119 Image Credit: Rena Schild / Shutterstock.com

For a data-driven solution to work, the most important thing is that different police departments share their data and take a unified approach.

There are some 18,000 different law enforcement agencies in the US, and they need to be pooling as much data as possible to enable the best possible analysis. These sort of insights are also nothing if no action is taken. Oakland PD, for one, is implementing Stanford’s recommendations. Oakland Police Assistant Chief Paul Figueroa noted, ‘This report provides a roadmap forward for the Oakland Police Department and police agencies across the country. This critical work moves from data collection to action.

Oakland has already implemented many of the recommendations in the report and will move quickly to implement the remaining items.’

It’s clear there is a problem, and if anything is going to change, it’s vital that other departments follow Oakland PD’s example, and work together to ensure data is used to an optimum level.

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Emotional intelligence in machines may not be that far away Alex Collis, Analytics Writer

ai needs emotional intelligence or we risk annihilation There is a moment in 2004 film ‘I, Robot’ in which a robot is forced to choose between saving the life of a young girl and that of Will Smith’s significantly older protagonist. It chooses Will Smith, despite his protests, based on the percentage chances of either surviving. This may well have been the logical choice, but it is highly unlikely that a human would have made the same one. Such a difference aptly highlights the potential problems we may face with an AI that doesn’t abide by our value system, as we enter a world where its intelligence far outstrips that of ours.

The Turing test for intelligence in computers, which requires the computer to trick a human being

Image Credit: https://de.wikipedia.org/wiki/Datei:Wall_e_und_Eve.jpg

into believing it to be human too, has long been held up as the standard.

However, in 2001, researchers Selmer Bringsjord, Paul Bello and David Ferrucci proposed the Lovelace Test, which asks for a computer to create something, such as a story or poem. At the heart of this is getting AI to display empathy – the ability to understand and share the feelings of another. Only when AI displays empathy will it truly be able to trick a human. Even more than this, when machine intelligence does outstrip ours, losing control of an AI without empathy is far more likely to result in human extinction.

Fortunately, emotional intelligence in machines may not be that far away.

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As humans need emotional intelligence, so too does AI. It needs an ethical value system in place, preferably our ethical value system

And it is already being developed in the most unlikely of sources - a children’s toy.

In June of this year, JP Morgan led a $52.5 million investment round in San Francisco robotics startup, Anki.

Anki was founded in 2010 by three Carnegie Mellon Robotics Institute graduates. The company first made its name in 2013 when it brought out its robotic race cars, which Apple CEO Tim Cook liked so much, he invited the startup on stage during Apple’s 2013 developer conference.

Cozmo is its newest creation - Anki’s second over all. It is a $180 vehicular toy robot, the distinguishing feature of which is, without meaning to sound sappy, how adorable it is.

The robot views the world through a single camera contained in a slot

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designed to resemble a mouth. The camera, which runs at 15 frames per second, sends the footage it takes to your phone, where the information is processed and sent back to the robot.

The programs it runs on are extremely simple, with the software development kit purposefully designed to be basic enough that even the greenest coder can tweak the behavior of the toy robot, helping to develop a robot that not only recognizes faces and navigates new environments, but also mimic emotions.

Cozmo’s so-called ‘emotional engine’ is an entirely unique blend of computer-vision science, advanced robotics, deep character development, and machine-learning algorithms.

This emotion engine powers a wide range of different states the robot is capable of emulating, such as

happy, calm, confident, and excited. It creates these emotions by taking the big five personality traits - openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism - and using them like primary colors, mixing them to replicate a complex range of humanlike emotions.

Anki co-founder and president Hanns Tappeiner notes that, ‘in the very beginning, when we started working on the first version of [Anki] Drive, we realized that characters and personalities are a big deal.

The problem we had was that cars aren’t the best form factor to bring personalities out.’ So Anki kept the idea under wraps and toiled in secret on using AI and robotics to ‘bring a character to life which you would normally only see in movies.’

Many have warned of the risks we face from AI. These are not just tin foil hat wearing lunatics, but people like Elon Musk and Stephen Hawking

Such a robot has clear implications for children’s toys, fulfilling the fantasy of all kids that their favorite toy be a real friend. It also has tremendous implications for the future of AI, and what we need to do by the time we reach the singularity - when machine intelligence finally outstrips ours.

There are real dangers with this.

Many have warned of the risks we face from AI. These are not just tin foil hat wearing lunatics, but people like Elon Musk and Stephen Hawking.

It is imperative for AI to work for us and not against us, but the speed at which AI will evolve means that it will eventually be developing technologies we’ve only dreamed of in seconds.

The ease with which we could lose control of it is breathtaking, and, as philosopher Nick Bostrom says, ‘it is vital that when AI explodes, it is a controlled explosion.’

Emotional intelligence is a key component of human intelligence, and could well be the key to controlling the explosion. As humans need emotional intelligence, so too does AI. It needs an ethical value system

Image Credit: https://www.flickr.com/photos/codepo8/4894996392

in place, preferably our ethical value system. If AI systems are expected to make decisions or act on our behalf, they need to know themselves what they are and are not allowed to do.

We need to ensure that we imbue any AI with our value system before it becomes more intelligent than us so that it can be controlled. Futurist Ray Kurzweil, a leading AI scientist, said in an interview with Wired that once a machine understands that kind of complex natural language, it becomes, in effect, conscious, and said that he believes this moment to be in just 2029, when machines will have full ‘emotional intelligence, being funny, getting the joke, being sexy, being loving, understanding human emotion. That’s actually the most complex thing we do. That is what separates computers and humans today. I believe that gap will close by 2029.’ Anki is a very early step along this journey, but while it may just be a children’s toy, its principles could pave the way for a safer AI.

Why Are More Companies Not Adopting Prescriptive Analytics?

Alex Lane, Deputy Head of Analytics

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The use of prescriptive analytics is at its highest ever level, and Gartner predicts that the market will reach $1.1 billion by 2019 - 22% CAGR from 2014. This is clearly significant growth, yet it is still dwarfed by the use of predictive analytics, a technology that is surely of less use. To put the growth of prescriptive analytics into context, MarketsandMarkets has estimated that the global predictive analytics market will grow from $2.74 Billion in 2015 to $9.20 Billion by 2020 - CAGR of 27.4%.

Prescriptive analytics is the next stage of predictive analytics. It looks at the actions that can be taken as a result of the insights revealed by predictive analytics, analyzing current data sets for patterns and then evaluating what would be the outcome of the multiple scenarios that could be enacted based on the decisions made around leveraging the data. By doing so, it gives decision makers more information about the impact of each option based on specific key performance indicators, removing a

significant element of risk and leading to faster decisions.

Google’s driverless cars are a good example of prescriptive analytics at work. They must make multiple decisions about their next step based on predictions of future outcomes.

So, for example, when turning, the car must anticipate everything that a normal driver must anticipate pedestrians, traffic - and take the action (moving out into the road) based on the impact that decision will have.

While the disparity between the predictive analytics market size and that of prescriptive analytics is simply explained by the relative newness of the technology, it does not explain why growth would not be far higher in a technology that is so clearly more beneficial. Mick Hollison, CMO of sales-acceleration software company InsideSales. com, argues that, ‘Predictive by itself is not enough to keep up with the increasingly competitive landscape.

Prescriptive analytics provide intelligent recommendations for the optimal next steps for almost any application or business process to drive desired outcomes or accelerate results.’ Despite this, just 10% of organizations currently use some form of prescriptive analytics, according to Gartner, which will grow to 35% by 2020.

There are a number of possible reasons for the slow adoption. Many organizations are still skeptical of prescriptive analytics because it is still so unfamiliar. There is not

the understanding there is with predictive analytics, and the patternseeking technology and machine learning algorithms that enable it are perhaps more complicated.

There may also be some confusion as to what prescriptive analytics is actually bringing to the table. Many organizations have already invested heavily in predictive analytics over the last few years, and another wave of investments into a tool where they still feel human control is necessary - such as decision making - is likely to appear unwarranted. However, decision making processes need to change, as they are now required quicker than ever. Companies looking to implement prescriptive analytics need to ensure that employees understand that the system works for them as a complement to their work rather than a replacement. The reasoning behind the recommendations must also be made clear, as sometimes the logic behind machine-generated answers are not immediately obvious to humans.

Predictive analytics is still a highly useful tool, but companies that fail to take it to the next level are likely to lose competitive edge to those that do. Decision making processes need to be re-evaluated constantly with the pace of technological evolution now so fast, and prescriptive analytics are vital to this.

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Major Military Powers Need To Invest In Analytics, Not Nuclear Bombs Dealing with an enemy of this nature using nuclear weapons is like trying to get squeeze a pimple with a steam roller

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David Barton Head of Analytics

The closest the world has come to abolishing Nuclear Weapons since their invention was in 1986 at the Reykjavik conference, where President Ronald Reagan and Soviet leader Mikhail Gorbachev appeared to reach the same conclusion that it was in the best interest of the planet to be rid of them. The talks ultimately failed because Reagan wanted to keep the weapons in case of ‘some alien life form that was going to attack the Earth approaching on Halley’s Comet.’ The debate has raged ever since Reagan’s statement, with the majority making about as much sense.

The UK’s recent vote on renewing Trident - or at least ‘their own continued process of procuring the next generation of submarines which carry that weapon as part of the United Kingdom’s Continuous At Sea Deterrent (CASD)’, whatever that means - ended, as expected, with a decision in favor of doing so. A central argument against its renewal is the changing nature of the global threat, with nuclear weapons no longer seeming applicable. Terrorism is the primary threat facing major powers today, and this threat is agile, vicious, and resourceful. Dealing with an enemy of this nature using nuclear weapons is like trying to get squeeze a pimple with a steam roller. The solution needs to be as agile as the enemy and, in light of this, it is the far more prosaic data and analytics that are having the most impact.

Ronen Horowitz, former head of the Israel Security Agency’s IT unit, recently summed up the nature of the threat, saying, ‘We are looking for a needle in a haystack—very weak signals, when the enemy is highly sophisticated.’ Warfare is now focused on individuals rather than formations.

This ‘individualization of warfare’, as Colonel Glenn Voelz describes it, has been fueled by several key technical innovations over the last decade. These include advances in digital and telephone surveillance, precision targeting drone technology, and biometrics, all of which pull together a tremendous amount of data

from multiple streams of disparate, unstructured data for analysts that support both lethal and non-lethal targeting.

and police can freeze bank accounts and compel companies to hand over records of their communications whenever is necessary.

Analyzing this data is beyond the reach of humans, and the military is leveraging the same kinds of techniques being used in business to garner insights, including tools such as Distributed Common Ground System (DCGS) to, according to Colonel Voelz, ‘enable data integration and advanced network analysis.’

Another fundamental shift in the nature of warfare is the increasing number of so-called megacities - densely populated areas often over 100 million.

These cities present a huge challenge, essentially throwing more needles into the haystack and asking intelligence agents to find the sharpest one. These cities are, however, increasingly smart, with Internet of Things technology and have data-collecting sensors everywhere, revealing everything about its population. This data can essentially be used to map and model a city’s complete infrastructure and inhabitants within days, with any changes to this pinpointed real time as data streams are updated continuously.

The majority of press around data collection and its use in targeting has focused on the US, but all major military powers are heavily invested in the techniques. Ronen Horowitz claims that the Israeli military has already used it to hunt down and kill a number of enemies of the Israeli state - including several senior Hamas leaders killed during the Israeli incursion into Gaza Strip. In a recent interview, he claimed that, ‘I am telling you with certainty that quite a few [dead] terrorists are looking at us from the sky owing to Big Data capabilities.’

China too are investing heavily, with one engineer at China Electronics Technology, a producer of their military hardware, saying on record that their software was able to build ‘portraits of suspects by cross-referencing information from bank accounts, jobs, hobbies, consumption patterns, and footage from surveillance cameras’.

Any unusual behavior could be flagged up and investigated immediately,

In the West, as in the majority of the world, expectations about the conduct of war are such that modern militaries must find ways of accomplishing goals without targeting civilian populations and the infrastructure that supports them. Nuclear weapons are entirely at odds with this. The world needs smaller bombs that are better targeted, and it needs analytics to do the targeting. The debate around the negative impacts of gathering data are nuanced and finely balanced. The debate around the negative impacts of a nuclear holocaust are not so.

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Interview with Joel Shapiro;

Executive Director of the Program on Data Analytics at Kellogg's School of Management at Northwestern University

Joel Shapiro, JD, PhD, is clinical associate professor and Executive Director of the Program on Data Analytics at Kellogg's School of Management at Northwestern University.

Joel teaches graduate courses in decision analytics and policy analysis, with a strong focus on how to use data analytic solutions to solve real-life business problems. Prior to joining Kellogg, Joel served as Associate Dean of Academics at Northwestern University School of Professional Studies, leading the creation and growth of myriad on–ground and online programs. Joel holds a PhD in policy analysis from the Pardee RAND Graduate School, a JD from Northwestern University School of Law, and a BS in physics from the University of Michigan.

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We sat down with him ahead of his presentation at the Predictive Analytics Innovation Summit, taking place in Chicago this November 29-30

What first sparked your interest in analytics?

I’ve always been very quantitatively inclined, and loved trying to explain various day-to-day phenomena with math. After I majored in physics in college, I took a ‘quant-detour’ by going to law school and briefly working as an attorney. I was completely a fish out of water. One senior partner asked me to help assess how our clients would benefit from the big tobacco settlement in the late 90s. I created a cool probabilistic model that was really quite elegant, and – when I presented it to him – he swore and threw the memo at me. He wanted the ‘one right answer,’ and I knew that there was a range of possible answers. At that point, I knew I needed to find a field that embraced data and the uncertainty inherent in using data for decision-making. I ended up going back to school for a PhD in policy analysis and loved it – it wasn’t easy to go back to school after just completing three years of law school, but it turned out incredibly well for me.

What do you think is the most important thing a company should do to instill a datadriven culture?

They need to think really deeply about what problems they’re trying to solve and what questions they’re trying to answer. If a company doesn’t give sufficient thought to the specific goals they’re trying to accomplish, then they’re going to fail with analytics. You can’t just hire some data scientists and hope that they come up with great insight. The data scientists typically don’t know the business, and the business folks often don’t know the data science. They need to work well together, which means that they both have to know what they’re trying to answer and why.

Can analytics be automated?

Well, yes and no. In the right cases, data can be automatically generated and analyzed. But analytics is fundamentally about using analysis to do something differently. I am very skeptical of off-the-shelf analytics products that claim all you have to do is load in your data and it will spit out actionable insight. That’s really dangerous. Most businesses have unique processes, goals, and contexts that make the link from data to action fraught with nuance.

Analytics still rests fundamentally on good critical thinking skills – how to ask great questions and rigorously assess evidence that can lead to action. It’s hard for me to imagine how context-specific critical thinking could be automated - I think good data scientists will serve this role for a long time.

What will you be discussing in your presentation?

I’ll talk about my firm conviction that analytics is a leadership problem, not an IT problem, and not a data science problem. Sure, IT and data science give us important tools to collect, store, and analyze data. But analytics has value when it leads to action and change. And business leaders are the ones who implement business change. When an organization embraces analytics at the leadership level, the IT and data science jobs are more valuable to the company and a whole lot easier to do well. It’s entirely a win-win.

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