Big Data Innovation, Issue 27

T H E L E A D I N G V O I C E I N B I G D ATA I N N O VAT I O N

BIG DATA INNOVATION APR 2017 | #27

+

Have We Allowed Big Data To Become Too Powerful? /10

WHY THE INSURANCE INDUSTRY IS TURNING TO MACHINE LEARNING

WILL THE LATEST WIKILEAKS’ REVELATIONS SLOW DOWN IOT ADOPTION?

When we think about the insurance industry it doesn’t scream ‘high-tech’ but the truth is that machine learning and AI may well have a huge impact /18

With the leaking of documents showing how the CIA has been using IoT devices to spy on people, will it turn people off from adopting them in their own homes? /26

The Intersect of Data and Design

Data Visualization Summit

April 19 & 20, 2017 San Francisco

SPEAKERS INCLUDE

/2

+1 415 692 5426

rasterley@theiegroup.com

www.theinnovationenterprise.com

ISSUE 27

EDITOR’S LETTER Welcome to the 27th Edition of the Big Data Innovation Magazine

Data has been taking a beating over the past 12 months. We have seen it dragged through the mud on multiple occasions, from its questionable use in elections through to Google accidentally using it to advertise on controversial websites. When we look at what is generally creating the headlines around it, the context is almost always negative.

This could simply be down to negative headlines generally getting more clicks, but it has the potential to do real damage to something that has an almost unquantifiable potential for good in the world.

We have seen it being used to help with conservation and disaster relief, while on a very localised level, it helps people to use transport effectively and allows them to perform better in their job. The big issue that data has in terms of selling its image is simply that most of the uses for it go unnoticed because they aren’t meant to be noticed.

When somebody orders an Uber and it arrives within 5 minutes, they think ‘this app works well’ but there is little

appreciation of the underlying data that has allowed it to happen. When somebody has a suggested item on an e-commerce site, they think ‘I like that product’ but there is no thought around the development of data that has allowed an algorithm to pinpoint the perfect product from their previous activity.

It is the single biggest challenge that data faces - showing the impact it has on everybody’s life. The difficulty is that we’ve hidden it behind beautifully designed websites and apps, so people don’t see it. It is like the service corridors of hotels and restaurants; people don’t see them, but without them the establishments would not be able to work. The difficulty here is that we need to make it more obvious that data is having a positive impact on daily life.

People do not mind something that benefits them if they sacrifice something relatively small to access it, but if they cannot see the benefit because it is hidden behind something that looks nicer, then it is difficult for them to understand that data has driven the action they require. Uber

needs your location data, Amazon looks at your purchase history, Youtube analyses your viewing history, apps use your Facebook data so you can avoid creating a new account to login, but the underlying data is hidden from site.

It certainly wouldn’t be wise to suddenly reveal all of this to consumers, but as a community it needs to be made clearer that data isn’t just a hack or a mistake in advertising strategies, it’s the millions of things that people can do today that genuinely makes their lives a little easier and more connected.

George Hill managing editor

/3

Improve Patient Outcomes with Data Analytics

Big Data & Analytics in Healthcare Summit

May 15 & 16, 2017 Philadelphia

SPEAKERS INCLUDE

/4

+1 415 614 4191

jc@theiegroup.com

www.theinnovationenterprise.com

contents 6 | DATA VISUALIZATION IN A BREAKING NEWS ENVIRONMENT

22 | HOW DIFFICULT IS IT LEADING IN THE TIME OF BIG DATA?

We take a look at how the BBC uses data visualization in its new reporting by taking a look at a presentation by Emily Maguire, the BBC’s former Editorial UX Designer

Every leader today is expected to be a data expert, but is that fair and what exactly do you need to do to lead in a modern, data-driven environment?

10 | HAVE WE ALLOWED BIG DATA TO BECOME TOO POWERFUL?

26 | WILL THE LATEST WIKILEAKS’ REVELATIONS SLOW DOWN IOT ADOPTION?

There has been much talk about big data in recent months and how it has been used for nefarious acts, but is it right to say it has become too powerful in our society?

With the leaking of documents showing how the CIA has been using IoT devices to spy on people, will it turn people off from adopting them in their own homes?

14 | FROM PREVENTATIVE TO PREDICTIVE MAINTENANCE

The old adage of ‘if it ain’t broke, don’t fix it’ is no longer true and companies are increasingly turning to predictive maintenance to fix before they things break 18 | WHY THE INSURANCE INDUSTRY IS TURNING TO MACHINE LEARNING

When we think about the insurance industry it doesn’t scream ‘high-tech’, but the truth is that machine learning and AI may well have a huge impact

WRITE FOR US

Do you want to contribute to our next issue?

Contact: ghill@theiegroup.com for details ADVERTISING

For advertising opportunities contact: pyiakoumi@theiegroup.com for more information

managing editor george hill | assistant editor james ovenden contributors david barton, helen snelting, gabrielle morse

| creative director oliver godwin-brown

/5

Data Visualization In A Breaking News Environment

there are three main work streams where data visualization is used: live news, world service, and visual projects.

/6

Gabrielle Morse, Curator, Data Visualization Summit

The BBC is one of the world’s leading media and news outlets. Founded in 1922, the corporation operates across various divisions, including broadcast, world service, online journalism, and other projects. Often, both visual and online journalists must be capable of working under high pressure in a breaking news environment. Emily Maguire, the BBC’s former Editorial UX Designer was among the keynote speakers at the Data Visualisation Summit in London, where she shared with us how to successfully create a visual narrative using data and release it to tight deadlines.

The BBC’s Visual Journalism team consists of web developers, journalists, and designers. These people are responsible for telling stories by analyzing data, turning it into graphics and visual projects.

The team also ensures that stories are accessible and user-friendly on all kinds of screen sizes and digital platforms. Overall, there are three main work streams where data visualization is used: live news, world service, and visual projects.

Within live news, for example, diagrams are used to help readers understand complex stories and processes within the content. These may include a demonstration of the key parts of the human heart, how the clean air is circulated in a plane cabin, and other facts and processes.

Other data viz tools help to reveal patterns and allow for exploration of the data behind stories, for example: ‘When you need to compare China’s population to the world’s average since the introduction of the one-child policy in 1979,’ Emily shared.

Among the data viz tools used at the BBC, Emily mentions annotated images which act as an easy way to enhance the story page and can be produced quickly by a journalist. They also create two types of graphics for social media channels. One of /7

Unfortunately, the reality is that most of the breaking news stories are about tragedies,’ and this can often make it a challenge to create high-quality visual content in a timely manner.

the accident was highly unusual. As more official reports started to occur, it became possible to create more graphics about the flight and details of its fatal descent. Four hours after the crash, voice recordings and more flight data were discovered, allowing the crash team to analyze the spread and size of debris, and eventually, TV designers could create a 3D model of the crash site. A total of 144 people died in the incident. Based on the fact that victims were nationals of different countries, it had been requested to produce graphics of the crash site map in 20 languages.

‘This is the reality of working in live news. It can be really difficult to stay unemotional, but we have to focus on presenting the story in the best way possible,’ Emily concluded.

them contains bespoke illustrations and the other one is ‘templated’ and photography based. Both types of graphics demonstrate bite-size statistics which are shared daily across social networks, including Twitter, Facebook, and Instagram.

However, it’s not always the case that the visual journalism team has all the time in the world to produce visual material. When it comes to the breaking news environment, aside from text pieces, supporting graphics also have to appear promptly, but Emily says that: ‘Unfortunately, the reality is that most of the breaking news stories are about tragedies,’ and this can often make it a challenge to create high-quality visual content in a timely manner.’

To explain how challenging this may be, Emily used the Germanwings plane crash which happened in March 2015, as a case study, where the pilot ‘intentionally’ crashed the plane into the mountainside of the French Alps.

Emily said that as the news arrived, journalists and designers quickly discussed the key facts needed to /8

present the story. Among the first graphics created were the map images which illustrated where the plane was coming from and an approximate location of the crash. To do this, the visual team used a piece of software called Curious which allows them to create high-quality maps. Additionally, they used the image from Google Satellite to demonstrate the last known location of the plane in the Alps. Emily says: ‘Within minutes we received more information and again used the satellite data to develop close-ups.’

The team knew that the accident location was remote and completely inaccessible by road, so it was a challenge to come up with more visuals as the breaking news story continued to evolve: ‘Within an hour we received the news that the crash site had been located at an altitude of 2,000 metres and air safety inspectors were on their way to inspect it.’ For follow up graphics, the team used Google Earth again, to demonstrate how mountainous the region was. As the story evolved, more data appeared about the Germanwings’ spotless flight safety record, signalling that

Big Data Innovation Summit April 19 & 20, 2017 San Francisco

Cultivate the Data, Yield the Profit

CONTACT JORDAN

+1 415 614 4191 jc@theiegroup.com www.theinnovationenterprise.com

SPEAKERS INCLUDE

/9

Have We Allowed Big Data To Become Too Powerful?

Our society is at a crossroads: If ever more powerful algorithms would be controlled by a few decisionmakers and reduce our selfdetermination, we would fall back in a Feudalism 2.0 / 10

George Hill, Managing Editor

There has been a significant amount of hand-wringing over the use of big data for certain activities, from its monetization through to using it to direct specific messages at specific people. It has been blamed for the huge political polarization we see in Western society today and it has also been credited with some huge breakthroughs in terms of elections, but not always in a positive way.

We have seen several media outlets call it in to question, The Guardian ran an editorial called ‘The Guardian view on big data: the danger is less democracy’ and even Scientific American had a piece called ‘Will Democracy Survive Big Data and Artificial Intelligence?’. It is clear that big data is becoming more powerful, something we knew would happen, and will continue to happen, as more data is collected and the means in which it is analyzed becomes more powerful.

When discussing the use of targeted messaging The Guardian went as far as saying: ‘Our model of democracy is based on public campaigning followed by private voting. These developments threaten to turn this upside down, so that voting intentions are pretty much publicly known but the arguments that influence them are made in secret, concealed from the wider world where they might be contested.’

Scientific American perhaps went even further, saying: ‘Our society is at a crossroads: If ever more powerful algorithms would be controlled by a few decision-makers and reduce our self-determination, we would fall back in a Feudalism 2.0, as important historical achievements would be lost.’

Both essentially have the same message - big data is becoming too powerful.

It is difficult to argue with this, given what we have publicly seen in elections since 2008, that the use of data has had a profound impact on their results. Cambridge Analytica, a formerly unknown company for many outside of the big data sphere, has now become synonymous with the misuse of data for manipulation, both for the EU referendum in the UK and Donald Trump’s victory in the US. The company have been very vocal in their work too, with their CEO Alexander Nix telling Sky News that there are ‘close to 4 or 5 thousand data points

As Scientific American points out, we are at a crossroads, but unfortunately, it is one that is unlikely to lead anywhere except straight ahead

on every individual’ showing the depth of knowledge that they have exploited in their work. We also saw Obama targeting specific voters through data, utilizing complex algorithms to create targeted messages for specific voters and donors to help his campaign to two relatively comfortable victories.

However, as The Guardian rightly points out, there is a big difference between campaigning now and even 8 years ago. Where all campaigning was previously conducted relatively publicly, now it is done behind closed doors thanks to data. One of the most controversial uses in the most recent election was the Trump campaign’s attempts to suppress black voters who were overwhelming anti-Trump. In a BusinessWeek article, with authors Joshua Green and Sasha Issenberg talking directly to the Trump team, they found out that: ‘On Oct. 24, Trump’s team began placing spots on select African American radio stations. In San Antonio, a young staffer showed off a South Park-style animation he’d created of Clinton delivering the ‘super predator’ line (using audio from her original 1996 sound bite), as cartoon text popped up around her: ‘Hillary / 11

Thinks African Americans are Super Predators.’ The animation will be delivered to certain African American voters through Facebook ‘dark posts’ — nonpublic posts whose viewership the campaign controls so that, as [campaign digital guru Brad] Parscale puts it, ‘only the people we want to see it, see it.’ The aim is to depress Clinton’s vote total. ‘We know because we’ve modeled this,” says the official.

‘It will dramatically affect her ability to turn these people out.’’

It is clear that whoever is on the other end of this kind of campaign is going to struggle to retaliate, given they are unlikely to even know it’s happening.

As Scientific American points out, we are at a crossroads, but unfortunately, it is one that is unlikely to lead anywhere except straight ahead. The problem simply being that political campaigns want to be able to target specific voters and this is the best way to do it in a modern society. Therefore the people in power who need to make the decisions about controlling the way data is used in elections are the very ones who have benefitted most from it.

As data becomes even more powerful and the ability to manipulate people based on increasingly tightly defined attributes is commonplace, it is difficult to see how this use of data for campaigning can be stopped.

Most political campaigns have some in-house expertise in data, but the majority of the data work is simply outsourced to companies like Cambridge Analytica. These are the companies who could technically stop these kinds of things as those within the campaigns want to have the most powerful weapons, regardless of how they are being used. Even this is itself a difficult task given that it is not the job of the data company to know what the data is going to be used for, instead, its role is just to create the data.

We are therefore left with only a handful of institutions who could have a say over this, essentially minority / 12

parties in government and electoral commissions. The issue with the first is that they have very little power for something so big, we have seen across the houses in the US today that Democrats have very little say over policy, given that they cannot effectively oppose anything proposed by the Republican House or President, neither of whom are likely to try and pass a bill that helped them get where they are. Electoral commissions may have more of an impact and can certainly impose rules on campaigning methods and voter targeting, but they then have the dubious task of monitoring every message and seeing how and why it is being sent, all whilst not having any kind of data expertise in-house.

We have previously asked the question about whether big data will destroy democracy, and came to the conclusion that at the moment it isn’t. However, we didn’t look into the future as more data is collected and more power is used to analyze it. It is clear that the use of dark messaging combined with hyper-personalized targeting has had a profound impact on the electorate and election systems, especially when it comes to voter suppression, but how exactly can this be controlled and who exactly could do the controlling?

Premium online courses delivered by industry experts academy.theinnovationenterprise.com

Image Credit: Rena Schild / Shutterstock.com

/ 13

From Preventative To Predictive Maintenance Helen Snelting, Data Science Manager EMEA, TIBCO Software Preventative maintenance and planned maintenance are widely employed across many industry sectors. They are characterized by regular predetermined maintenance intervals or a component’s expected lifecycle. At these times, components are exchanged, which is why, for example, a vehicle’s cam belt is changed after it has completed a pre-determined mileage.

However, component failures don’t always run to well-ordered timetables; they are random, seem unpredictable, and can be extremely expensive, as well as inconvenient when they fail without warning.

It’s better then, to keep tabs on the condition of systems and components. The ‘early warning systems’, made possible by the constant monitoring of devices and machines in operation, can help to either avoid problems before they arise or take immediate action against those that have arisen. This concept of predictive maintenance takes the pro-active principle to new heights. / 14

If it ain’t broke, it will be A combination of monitoring systems, sensors, and controllers all conspire to simplify processes and anticipate problems as quickly as possible.

This allows for maintenance that is flexible and variable and can be shaped around the actual state of the equipment, in contrast to the rigidity of the old, tightly fixed maintenance intervals, which worked according to the timetable. In the example above, the cam belt would only be changed

at the point that it needed to be, no sooner – the capacity for calamity is considerable.

So, why fix the things that aren’t broken? Replacing components at the correct time reduces waste and is much more cost effective. Predictive maintenance, therefore, applies the old adage that ‘if it ain’t broke, don’t fix it’ and adds a new one: If it is going to break, fix it now!

To understand when things do need fixing, statistical predictive maintenance models are created. This is done by importing ‘predictors’, which are critical values gleaned from sensor data, process measurement data, and ambient data. These values are fed into an analytics tool which recognizes certain patterns, such as the symptoms of a machine that has failed in the past.

This concept of predictive maintenance takes the pro-active principle to new heights.

Pre-emptive problem-solving in action An excellent example of pre-emptive problem solving comes from the oil and gas industry, where the failure of a drilling system can potentially cost one million dollars per hour.

In oil and gas fields, the motor temperature, motor vibration and the delivery pressure of a pump are monitored in real-time so that anomalies are immediately noticeable. On the basis of the collected data (pressure, temperature, vibration), alert rules can also be created so that if, say, vibrations increase over a certain threshold in ten minutes, or temperatures rise or voltage drops, a ’high priority’ alarm is triggered.

The use of algorithms, to analyze data as it is collected, allows for ‘real time’ reporting, which in turn creates the possibility for timely interventions.

Sometimes the affirmative action is a result of management decisions and sometimes troubleshooting is decided using algorithms.

Using this technique, one US oil producer raised the ‘meantime-before-failure’ of its pumps by 1%, which was enough to save it $8m a year.

In this case, the predictive maintenance discipline can be applied to make better use of sensory data collected in drilling operations. Until recently this was not typically analyzed until after the drilling operations had finished, by which time it was too late to make money-saving interventions. / 15

Using this technique, one US oil producer raised the ‘meantime-before-failure’ of its pumps by 1%, which was enough to save it $8m a year.

Context is key The inevitable consequence of the increasing networking of machines is that preventative maintenance is playing a growing role in what has been dubbed ‘Industry 4.0’. If Industry 4.0 is defined by its regulation by machines and timely automation of events, it follows that the real-time analysis of data is the foundation of this. How else would the machines have the knowledge to make decisions?

It is crucial that data is processed intelligently and quickly since even ‘smart and fast data’ loses its value and validity over time. Efficient event processing and real-time analysis empowers users to act before this happens. However, information and knowledge can be rendered useless without context. In order to use Big Data analytics efficiently, data and data streams - not just historical ones must be used in the right context and correlations between individual data sets must be established.

By way of example, data tells you that a tomato is a fruit, but context tells you not to put it in a fruit salad.

By the same token, in a machine environment, it is a valuable skill to recognize the critical moments and patterns within the multitude of events, or production processes, and react both immediately and correctly. The context on instant decision-making

/ 16

can be arrived at automatically, by algorithmically controlled reaction to events, or by human governance in response to intelligence displayed on real-time dashboards. But of course, the best context is usually provided by a combination of both.

This is where analytics platforms can be used. It’s, or your own, established models and algorithms can be applied in real-time and to multiple scenarios, ranging from oil pumps to manufacturing equipment to aircraft engines. The models give reliable information about changes in failure rates on everything from previous warranty problems to product quality. Combined, they provide the most desired outcome; a world where accurately predicting failure is more efficient and effective.

Lead the Data Revolution

Chief Data Officer Summit

June 5 & 6, 2017 San Francisco

SPEAKERS INCLUDE

+1 415 692 5426

rasterley@theiegroup.com

www.theinnovationenterprise.com

/ 17

These new technologies are wide in scope, and many are already being deployed

/ 18

Why The Insurance Industry Is Turning To Machine Learning

David Barton, Head of Analytics, Innovation Enterprise

The insurance sector is not traditionally ahead of the curve when it comes to new technologies. In a recent survey by Willis Towers Watson (WLTW), 58% of senior executives in the sector acknowledged that they lag behind other financial services sectors1 in terms of technology adoption with digital technology a particular problem.

The most obvious areas it can be used include claims processing, underwriting, fraud detection, and customer service / 20

There are a number of reasons for this. They are an inherently risk-averse group because of the nature of their work. Implementing new technology is always a risk - particularly for insurance, which is besieged by regulations and requires a great deal of transparency - so it would be logical that they would be more cautious. But this is beginning to change, with VC investments in insuretech (technology based solutions for insurance) rising from $130 million in 2011 to $1.7 billion by the end of 2015, and it looks to be on an upward trajectory.

These new technologies are wide in scope, and many are already being deployed. Possibly the most important of these, though, is machine learning and AI. AI is tailor-made for the insurance industry because, as Adam Devine notes on VentureBeat, ‘adoption of AI and automation will be highest in regulated industries and those that must process thousands of transactions and customer requests daily.’

Japanese insurance company Fukoku Mutual Life Insurance, for one, has already laid off 34 employees and replaced them with an AI system that can calculate payouts to policyholders.

The system Fukoku Mutual Life Insurance uses is based on IBM’s Watson Explorer, which utilizes ’cognitive technology that replicates human thinking to ‘analyze and interpret all of your data, including unstructured text, images, audio and video.’ According to local newspaper Mainichi Shimbun, the technology allows the company to read tens of thousands of medical certificates and factor in the length of hospital stays, medical histories and any surgical procedures before calculating payouts.

Fukoku Mutual Life Insurance believes that they will be able to increase productivity 30% and make 140m yen worth of savings per year, all for only around 15m yen a year.

Machine learning and AI has a number of other applications in insurance.

The most obvious areas it can be used include claims processing, underwriting, fraud detection, and customer service. They can also look to add wider benefit to the business by analysing market dynamics, competitor activities and, customer trends, detecting patterns in the data to garner insights with unprecedented granularity and at a speed humans are simply incapable of.

Fraud Detection There are 350 cases of insurance fraud worth £3.6 million uncovered every day in the UK, while in the US fraudulent claims across all lines of insurance total some $80 billion a year.

This number is only going up, and the overall cost of investigation rising with it. Machine learning is able to auto-validate policies by ensuring that key facts from the claim marry to the policy and should be paid. Once validated, this information can then automatically be fed into the downstream payment system and money sent in a matter of minutes without any human involvement.

Underwriting Data is now being mined from a variety of sources that can help insurers build a fuller picture of their customers. Machine learning algorithms can analyze this wealth of information quickly and accurately, without being tainted by human bias, and help to offer more accurate prices. In health insurance, for example, data from wearable devices such as Fitbit can track a customer’s activity, while analysis of their social media may give a more accurate idea of somebody’s lifestyle choices than they are willing to share. This will likely punish those who are unhealthier than they say, but it will also reward those who live healthier lifestyles.

Customer Service According to a 2014 Capgemini survey, a mere 29% of customers are satisfied with their insurance providers’ services, globally. AI can help improve customer service in several ways. Firstly, by driving chatbots that customers can engage with during purchase and claims processes. The time it could save insurance agents in not having to answer routine questions or carry out basic admin tasks will also free them up to provide a better service.

Another immediate benefit is in the ability to better monitor and understand interactions between customers

and sales agents. They can analyze recordings to detect patterns in how customers respond to certain calls so that improvements can be made.

These recordings will also help to improve controls over mis-selling of products, which greatly aids compliance. They can do likewise with written documents. Captricity, for one, converts unstructured data from handwritten and faxed documents into structured data using AI that this can be mined to discover insights about customer.

Challenges

They are an inherently risk-averse group because of the nature of their work

In the WLTW survey, 42% of senior-level executives in the insurance industry cited the complex regulatory requirements as the largest barrier to adoption of digital solutions. Keeping an eye on regulatory changes is a pressing concern for insurers anyway, and regulatory bodies are usually well behind when it comes to embracing technological innovations so there is always a chance that, once adopted, changes will need to be made to keep pace.

There is also issues around what happens to the humans currently employed in insurance. A significant portion of any company’s costs is staff, and insurance is no different. Machine learning will replace many menial tasks at first, and more complex tasks later, potentially helping to save insurers millions of dollars. As we have already seen with Fukoku Mutual Life Insurance, this is an extremely attractive option and many are likely to take it.

Whether staff agree that it is a good thing is another matter, and senior management will have to be transparent or risk causing serious issues.

Ultimately, however, machine learning brings with it many benefits, and insurers will ultimately have to embrace it, as every industry will, in order to retain their competitive edge. However, they will have to exercise caution and make sure they invest appropriately, while also retaining key knowledge and skills among their staff and meticulously tracking any new regulations in case anything goes wrong. / 21

people who are likely to have been in leadership positions for decades are now forced to be data leaders.

/ 22

How Difficult Is It Leading In The Time Of Big Data?

George Hill, Managing Editor

/ 23

We have seen Marissa Mayer forfeit her bonus at Yahoo thanks to the reaction of the company to the huge data hack that exposed the details of more than 1 billion users. It was one of the worst examples of data security in history and the reaction to itt was equally terrible, so it seems like the least she should do. Instead of simply giving up her bonus, she has instead asked the board to distribute it amongst the staff at Yahoo, which is perhaps the sign of a good leader in many people’s eyes.

‘The issues that existed were therefore more about the environment of the company in both the reporting lines and HR elements

This has highlighted something that is an increasingly important amongst leaders today - that leadership and leading data roles have more or less the exact same remit.

The rise of big data has seen companies holding the information of millions of customers and users, with even historical institutions like banks and insurance companies quickly becoming the guardians of some of the most sensitive and important data in the world. It has meant that people who are likely to have been in leadership positions for decades are now forced to be data leaders.

When we look at the data hack at Yahoo, the person who was directly responsible was not Meyer or recently departed General Counsel Ronald Bell, it was instead their CDO and data security team.

Of course, one of the most damaging elements of the hack was the slow speed at which the company reacted.

This could have been down to Mayer, but in a post about the hack she claimed that she was not made aware of it until only a few days before the rest of the world did. This shows that firstly those directly responsible for the protection of data either did not know (and are not too good at their job) or tried to hide it (either being too scared

/ 24

to broach the subject or deliberately trying to cover it up) neither of which show poor data leadership, but poor leadership in general.

When it comes to company CEOs, very few are likely to have any kind of technical knowledge of data management, simply because to know it, you generally need to have come up through more of the technical side of the business, something which traditional companies have not tended to put into leadership positions. If we take Brian T. Moynihan, current CEO of Bank of America, he started as a lawyer and a banker, a natural leadership path that has led him to become CEO of one of the most important companies in the US. He has had almost no experience with the technical side of data security or database management. Yet, Bank of America hold some of the most sensitive data for their customers.

It therefore doesn’t come down to knowing how to deal with data as a leader, but instead how to lead a company full of people who are the best at what they do. This is what he has done, relying on people like Doug Hague and Ned Carroll, both data leaders within the company, to deal with the technical elements of the business that he is unlikely to completely understand himself.

The issue that Mayer and the Yahoo executives had is that they were either unapproachable when something bad happened within their data department or the people who they had working in the department were not willing to declare their mistake. The issues that existed were therefore more about the environment of the company in both the reporting lines and HR elements.

If there are weaknesses in either they can cause chaos, regardless of whether this is a data issue or one caused by any other department.

It shows that high level leadership when it comes to data is no different to high level leadership for PR, finance, or any other department. It is about creating the right environment, being approachable, and hiring the right people for the job. So make sure the guys who control your data are the best you can get, have a load of relevant experience, and have the guts to admit their mistakes. If you don’t you are likely to find that it won’t just be the data department suffering.

/ 25 Š 2017 NetApp, Inc. All rights reserved. No portions of this document may be reproduced without prior written consent of NetApp, Inc. NetApp and the NetApp logo are trademarks or registered trademarks of NetApp, Inc. in the United States and/or other countries. All other brands or products are trademarks or registered trademarks of their respective holders and should be treated as such.

Will The Latest WikiLeaks’ Revelations Slow Down IoT Adoption?

James Ovenden, Assistant Editor

The documents show that the developers tried to inject these tools into targeted computers without the owners’ awareness, using on-board cameras and microphones on smart televisions, automobiles, smartphones, laptops, and so forth to spy on people - even when they are offline

/ 26

I enter into agreements to hand over my data everyday in the knowledge that it will be used by a company for marketing

Predicting the rate of IoT adoption is a risky business. Guesses made earlier this decade were astronomically high.

IBM believed there would be some trillion connected devices by 2015, while others were little more conservative.

Ericsson, for one, estimated that there would be 50 billion by 2020, a number it has since reduced to 28 billion by 2021. These numbers may again have to be re-evaluated, with the latest WikiLeaks revelations around CIA hacking of connected device bringing IoT’s security vulnerabilities fully into the mainstream consciousness.

The tranche of files released by WikiLeaks this week describe CIA plans for malware and other tools that could be used to hack into some of the world’s most popular technology platforms. The documents show that the developers tried to inject these tools into targeted computers without the owners’ awareness, using on-board cameras and microphones on smart televisions, automobiles, smartphones, laptops, and so forth to spy on people - even when they are offline. One CIA program named ‘Weeping Angel’ provided the agency’s hackers with access to Samsung Smart TVs that allows the television’s built-in voice control microphone to be remotely enabled while keeping the appearance that the TV itself was switched off, called ‘Fake-Off mode.’

The alleged cyber-weapons are said to include malware that targets Windows, Android, iOS, OSX, and Linux computers as well as internet routers.

The files do not give details of who the prospective targets are, and it is not actually suggested that they will enable mass surveillance. Indeed, most will require a warrant and even physical access to the device itself, making them little different to a wiretap. According to Matt Blaze, a University of Pennsylvania computer scientist, ‘It’s unsurprising, and also somewhat reassuring, that these are tools that appear to be targeted at specific people’s (devices) by compromising the software on them — as opposed to tools that decrypt the encrypted traffic over the internet. The exploits appear to emphasize targeted

attacks, such as collecting keystrokes or silently activating a Samsung TV’s microphone while the set is turned off. In fact, many of the intrusion tools described in the documents are for delivery via ‘removable device’.’

That intelligence agencies have created tools to turn IoT devices into listening posts should surprise no one, especially those in information management who will be well aware of vulnerabilities. In fact, if the CIA hadn’t been exploring these options they would have been frankly irresponsible.

Anything with an internet connection can be hacked, and for an organization like the CIA to be behind the curve in terms of how it is done would be look ridiculous. The revelations are, admittedly, worrying in terms of the recklessness with which the operations were conducted. According to Edward Snowden, the CIA reports show the USG developing vulnerabilities in US products, then intentionally keeping the holes open. ‘Reckless beyond words’, tweets Snowden, arguing that ‘until closed, any hacker can use the security hole the CIA left open to break into any iPhone in the world.’

This does not paint the CIA in the best light, though their reputation was not exactly gleaming before. But the hack is likely to have far great repercussions for the IoT, as it exposes the general public to the many security issues previously only really discussed in relatively niche areas of the technology press. IoT is still a nascent technology that can ill afford a publicity disaster.

The depth of the damage inflicted here though, really depends on whether people actually care about privacy enough to decide against using such devices as a result.

The question of data privacy has been much discussed in recent years, but while many consumers see it as a major issue, it is certainly not clearcut. In a recent report by KPMG International, 55% of consumers surveyed globally said they had decided against buying something online due to privacy concerns. Fears around the government use of data seems to be divided along

/ 27

IBM believed there would be some trillion connected devices by 2015, while others were little more conservative

roughly the same lines. In a recent Pew survey, conducted in spring 2016 and released this January, 46% of respondents said the government should be able to access encrypted communications when investigating crimes. Just 44% said tech companies should be able to use encryption tools that are ‘unbreakable’ by law enforcement.

However, someone having access to your data is one thing. Even private messages, which many would feel to be more of a violation than, say, knowing your location, are still just text. The idea of being watched and listened to in your home, on the other hand, is another ball game entirely. Whether or not this is actually what the CIA is doing is essentially immaterial, this is what the headlines and the tweets will make many who see this news believe - government agents watching everyone through their televisions,

/ 28

‘Big Brother’ watching, 1984 come to life. This news really drums home that the prospect of achieving antiquated notions of privacy are to become a distant memory when the IoT lays down roots in our everyday lives. Lack of privacy is already becoming an accepted part of modern life. FBI director James Comey declared after the CIA disclosures that ‘There is no such thing as absolute privacy in America.’

Since smart tech has become so integrated into our society, it’s hard to take the only reasonable step you can if you want total privacy - to cut out smart devices and messaging apps.

It simply means sacrificing too great a part of our social life. There’s little getting away from it.

Ultimately, it comes down to whether or not people really care enough. I betray my privacy far too often voluntarily through social media, personal writing, and talking in public spaces, to really be able to say I worry about it

Editorial credit: Rena Schild / Shutterstock.com

without looking like a hypocrite. But I have control over these things. I enter into agreements to hand over my data everyday in the knowledge that it will be used by a company for marketing. I even send messages knowing they might be flagged up by some government agency if I say bomb too many times because I am aware this is useful in national security. The idea of someone listening in to conversations I have in my own home without my consent or good reason feels like far more of a violation, though. I also expect any companies to whom I give my data to do everything they possibly can to secure it. The issue is control and consent. What matters for the growth of IoT moving forward is that people feel like they have control over their data and that there is clarity from governments as to whether they will be able to hack it. These tools may be intended for enemies of the state, but who determines what this means?

they have left some pretty gaping holes. The CIA is not going to be the only organization hacking connected devices, there are likely to be some far more nefarious characters willing and able to do the same. And stopping them needn’t even be too complicated. Mark Zuckerberg showed last year when images of his Macbook with a bit of tape over the lens went viral, that it’s pretty easily achieved.

Even putting a cap over the camera lens of a smartTV would probably be enough to show that companies were thinking about security and showing you that you should be aware there are risks. J.R.R. Tolkien once wrote, ‘It does not do to leave a dragon out of your calculations — if you live near him.’ For IoT, cybersecurity is the dragon, and manufacturers need to think carefully about it or they risk being severely burned.

IoT manufacturers firstly needs to work on their security far more than they have, as it is clear that in the rush to get out devices and exploit the trend,

/ 29