Big Data Innovation, Issue 21

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BIG DATA INNOVATION MAR 2016 | #21

big data impacts on cancer research | 12

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The Key To Becoming A Data Driven Organization

5 Data Startups To Watch Out For In 2016

Every company today wants to make decisions based on data, but how do you make sure you do it properly? | 24

We take a look at some of the most exciting new data companies likely to make a mark this year | 6

Big Data Innovation Summit Speakers Include

San Francisco April 21 & 22 2016

+ 1 415 692 5426 rasterley@theiegroup.com www.theinnovationenterprise.com

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ISSUE 21

EDITOR’S LETTER Welcome to the 21st Edition of the Big Data Innovation Magazine

Data is evolving.

It is no longer the thing that geeks talk about in dark rooms before passing the information up to their superiors, or that scientists use in experiments - it has become one of the most important things on the planet. We have seen with recent news reports, from the seemingly never-ending-story of Julian Assange’s potential extradition, to Edward Snowden’s continued exile in Russia, that it is front page news.

However, it has been a far more subtle news story that has the potential to have the biggest impact on how we use, store and access data in the future. This has been the Maximillian Schrems v. Data Protection Commissioner (case C-362/14) in the Court of Justice of the EU. Here it was found that in the wake of the Edward Snowden revelations that the existing data sharing deal between the US and

EU (Safeharbour) did not sufficiently protect data being transferred from the EU to the U.S.

collect, store and access their data in the future. However, it is not the only big data news.

After discussions between the U.S and EU, the new treaty is likely to be called the EU-US Privacy Shield, which was announced in February, with further details slowly coming out about it. One of the key elements will be the way that companies holding a large amount of data store it and make sure it is not misused.

Away from this, but also having profound implications, is the ongoing saga of Apple refusing to unlock their iPhones and the FBI attempting to force them to do so. In essence it would mean Apple creating a security weakness in their device that could be exploited; however, the FBI argue that it would need to be a one time thing to look at one or two phones.

It will also provide greater protection for private citizens’ data from government use outlined in the Edward Snowden leak, following the ruling that the existing treaty didn’t do enough.

We do not currently know a huge amount about the lengths that companies and governments will need to go to, but one of the clearest elements is that it will have a profound impact on the way that companies in both the U.S and EU

Regardless of how both turn out, it looks like 2016 is turning into an incredibly important year for data protection and government use of data.

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

George Hill managing editor

big data innovation

Data Visualization Summit April 21 & 22, 2016 | San Francisco Speakers Include

+ 1 415 614 4194 jc@theiegroup.com www.theinnovationenterprise.com

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contents 6 | 5 DATA STARTUPS TO WATCH OUT FOR IN 2016

18 | IMPROVING PATIENT CARE WITH THE IOT

We take a look at some of the most exciting new data companies likely to make a mark this year

The Internet of Things is having a significant impact on the way that patients are cared for in and out of hospital

9 | 5 INCREDIBLY COSTLY BIG DATA MARKETING MISTAKES

20 | NURTURE BY NUMBERS

With the use of data in marketing activities increasing, the chances of a big mistake whilst using it are growing 12 | BIG DATA’S IMPACT ON CANCER RESEARCH

We look at big data’s potential in the treatment and diagnosis of the deadly disease

Nature or nurture or numbers?

We look at how the use of data and analytics could give our kids the best chance in life 24 | THE KEY TO BECOMING A DATA DRIVEN ORGANIZATION

Every company today wants to make decisions based on data, but how do you make sure you do it properly?

14 | DATA SCIENCE & DISABILITY

With the use of data changing the way we live our lives, it it giving those with disabilities a new lease of life

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managing editor george hill

| assistant editor charlie sammonds | creative director charlotte weyer

contributors kit fiber, gabrielle morse, chris pearson, hal finkelstein, rick delgado, shirley lam

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5 Data Startups To Watch Out For In 2016 James Ovendon Managing Editor

big data innovation

The tech industry has seen some eye-watering investment in recent years, with unicorns now being created on almost a weekly basis. Data analytics firms such as Palantir have been quick to pass the magic billion dollar mark, and with a number of other data companies having been successful with funding rounds over the last, it looks like 2016 will see the trend continue.

According to Forrester Research, the broad business intelligence software category is set to generate $21bn in total revenue this year, and there are a host of startups trying to take as much of that as they can. We’ve looked at 5 companies making the biggest impression on the market place.

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1:

2:

3:

Looker’s founders, Lloyd Tabb and Ben Porterfield, started the company with the aim of creating a tool for people to search structured data in a way more similar to the way people explore the web. They created a new language for database queries, an easier-to-use approach to SQL called LookML, and presents data in such a way that explains and curates it rather than simply answering queries, helping to bring data teams together with users. CEO Frank Bien explained that ‘people were building big data stores but applying old approaches’, a problem Looker attempted to correct by putting the application server on top of software so the user could ask it questions.

Looker CEO Frank Bien explained, ‘Everybody uses a different language to explain the same thing. This helps teams talk in the same vocabulary.’

Brainspace’s technology is designed to make sense of unstructured data, analyzing concepts and reading inbetween the lines of the vast amount of documents and data out there.

Founder Dave Copps explained that, ‘The challenge for us is to make it so usable, easy and so valuable that people want to come in. We want to build a system that learns about you and helps connect you with people and things all the time.’ One of their best publicized successes so far was their analysis of the Enron documents, which threw up patterns previously unseen by the lawyers and provided new evidence for the case.

These are the kinds of data sets that the tool is best suited to, and they have proved particularly popular with firms in the financial industry, with a client list that includes giants like Deloitte and KPMG.

London-based startup Qubit is a data analytics platform which facilitates online selling. CEO and co-founder Graham Cooke, said in a statement that, ‘The industry has been dogged by ineffective front-end point solutions. Now the market is quickly realizing that customer experience delivery is not a ‘front-end play’ but rather requires a large, integrated enterprise scale resource and a deep understanding of the customer. This is an area that Qubit invested in early on.

Our platform allows deeper customer understanding, better insights and faster action meaning our clients can give their customers more than their competitors can.’

Looker now has offices in London, San Francisco, and New York, and its workforce has roughly doubled from 100 at the start of 2015 to 200 in 2016. Looker has also tripled the number of companies on its customer list to 450, adding high-profile organizations like eBay and Intel.

Interest in the company from VCs has been tremendous, with the firm raising another $30m in a Series B funding last year, and another $48m in January of this year. The funding the company has raised will allow them to expand even more rapidly, and the aim is to almost double its customer list again to 800 by the end of the year.

Brainspace raised $5m in 2015, and launched another round of funding this year. Copps expects 100% growth in 2016, when he projects the company will cross the $10 million mark.

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Platfora ‘masks the complexity of Hadoop, making it easy for customers to understand all the facts in their business across events, actions, behaviors and time!’ Its client list already includes Disney, Opower, Sears, and the Washington Post.

Platfora currently has 150 employees and recently announced a $30m investment that CEO Jason Zintak says will see that double by the end of the year, as well as helping field expansion, sellers, customer success managers, trainers, and development efforts.

4. Qubit

Qubit recently closed a $40m Series C round led by Goldman Sachs, bringing the total amount of funding raised by the company to approximately $76m. Qubit is now aiming to more than double its engineering capability and continue its growth trajectory over the next year.

55.:Qubol e Qubole was started up by former Facebook engineers Ashish Thusoo and Joydeep Sen Sarma in 2011. It is a a cloud-based Big Data as a service developer that aims to simplify, speed and scale Big Data analytics workloads against data stored on AWS, Google or Azure clouds with on-demand elastic clusters in the cloud that act as an alternative to on-premises Hadoop clusters.

You can then scale the HaaS cluster up and down as needed, removing the necessity for a relatively static infrastructure in your own data centre. Its customers include Pinterest, and they recently raised $30m in a Series C funding round to expand its flagship product, Qubole Data Service, taking the total amount raised to $50m. big data innovation

Internet of Things Summit Speakers Include

San Francisco April 21 & 22, 2016

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

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5 Incredibly Costly Big Data

Marketing Mistakes Hal Finkelstein Marketing Manager, BDEX.com

Low-quality big data assets can lead to incredibly costly marketing mistakes.

Research by Experian indicates that low data quality has a direct impact on revenue for 88% of modern organizations. Average losses are approximately 12% of revenue.

For organizations who are shifting towards data-driven marketing and customer experiences, low-quality data can lead to costly mistakes.

How Bad is the Average Marketing Big Data?

Per eConsultancy, 22% of information on contacts, leads, and customers contains inaccuracies. Perhaps most concerning, the average organization’s quality index is headed in the wrong direction. 12 months ago, the average inaccuracy rate was just 17%. Incorrect data can have a real impact on your team’s ability to build segments, understand behavioral triggers and preferences.

In contrast, organizations with a high degree of data accuracy are more likely to appreciate: ● Efficiency ● Cost-Savings ● Customer Satisfaction ● Informed Decision-Making ● Protection of Brand Reputation Poor-quality or old customer data can lead to a series of costly marketing mistakes.

Join us as we review some devastating errors that can be directly attributed to inaccurate customer data. big data innovation

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Low conversion rates on programmatic advertising is a symptom, not a cause

1. Low Advertising Conversions

4. Mobile Neglect

Low conversion rates on programmatic advertising is a symptom, not a cause.

Poor click-throughs and conversions can be attributed to a lack of mobile advertising, poor segmentation, irrelevant data, or other factors.

However, far too many marketing teams fail to take appropriate action in response to low advertising conversions. Instead of working to improve the breadth or quality of data, they continue generating ads. Before running more ad campaigns, marketing teams should take appropriate action to ensure they can achieve better returns.

Far too many big data marketing strategies are focused on desktop advertising, email receipt, and experiences. In reality, consumer behavior demands mobile marketing.

As of 2015, adults now spend more time engaged with mobile devices than desktops, laptops, and other connected devices combined. There’s a good chance that, at least 50% of the time, your desktop-optimized advertising is consumed on mobile devices. This can lead to poor user experience (UX) and returns on investment.

2. Inconsistent Brand Experiences Without accurate or up-to-date data, your brand communications could send the message that you don’t know your customers. You may generate programmatic advertising for products your customers already own. You could send an email blast for baby products as their children are approaching preschool age. Marketers need to actively combat a brand experience that’s inconsistent with a customer’s needs and activities. If you miss the mark repeatedly, you’ll struggle to build customer loyalty and sales.

3. Poor Email Deliverability The average return on investment (ROI) for email marketing at mid-sized organizations is 246%. However, organizations have the potential to significantly exceed these benchmarks with appropriate timing, segmentation, and other big data-driven activities.

Email communications to outdated contact lists have the potential for a high bounce rate, or percentage of emails that are undeliverable.

Email segmentations that are vastly inaccurate could also increase your risk of being pinged as spam. In the mind of a consumer, spam is simply 'unsolicited bulk email.' If your messaging is irrelevant or feels too much like a mass communication, it’s likely unwelcome. big data innovation

5. Poor Verification Methodologies All too often, major brands go viral for all the wrong reasons. Poor data verification can lead to mistakes that are embarrassing, insulting, or even hurtful to their loyal customers.

OfficeMax sent coupons addressed to 'Mike Seay, daughter killed in car crash.' The addendum to the customer’s name was unfortunately true. The company ultimately issued a public apology to the customer.

Manual data verification processes are rarely effective in the big data age.

Fortunately, using a data management platform (DMP) or another tool to perform quality checking against 3rd party data can eliminate much of the risk of similar mistakes.

If your organization’s data quality is average or below average, you’re at risk for many of these expensive marketing mistakes. By taking the appropriate internal steps to improve your quality standards, you can improve the ROI and impact of your marketing efforts.

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Inform. Inspire. Innovate Looking to find new ideas or share your own?

channels.theinnovationenterprise.com big data innovation

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BIG DATA’S IMPACT ON

CANCER

RESEARCH

Rick Delgado Tech Writer

big data innovation

Considering one in four deaths in the U.S. is a result of cancer or cancer-related effects, researchers need all the help they can get

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Some researchers have been using IBM’s Watson artificial intelligence to prescribe the correct treatment for specific patients

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hough significant advancements have been made in the past decade, cancer continues to be a devastating disease many people throughout the world have no choice but to confront. Even if someone doesn’t develop cancer in their lifetime, someone close to them likely will at some point. It is this lingering fear and the stillhigh fatality rate that has led many researchers and physicians to spend their lives studying the disease and how to treat and eventually cure it. Considering one in four deaths in the U.S. is a result of cancer or cancer-related effects, researchers need all the help they can get. In that effort, they may be aided by a recent trend in technology - big data analytics tools. While many people may associate big data with business, it may prove to be a highly consequential player in the ongoing fight against cancer.

Medical professionals are excited by the possibilities big data provides, and the results so far have been promising.

The goal of [Cancerlinq] is to collect data from every cancer patient in the U.S

Big data involves taking large amounts of information, analyzing it, and finding hidden insights that would normally be very difficult to find using more traditional methods.

On the surface, this may sound like it would be difficult to apply to the field of medicine and more specifically cancer research, but it actually fits in quite nicely in several ways. Take genomic sequencing, for example. This process essentially maps out the entire genome of a specific individual. While this process can yield some interesting findings unrelated to cancer research, doctors and gene specialists have used it to analyze the genes of cancer patients in the hopes of discovering certain patterns that can shed light into how and why cancer develops in certain people and not others. The idea of sequencing somebody’s genes may sound expensive, but due to advances in technology and analytics, the process can be done for around a thousand dollars. This means more data for researchers to use as they try to pinpoint the root causes of cancer.

This genomic mapping is part of the work of many research institutions including CancerLinq. The goal of the organization is to collect data from every cancer patient in the U.S. Whilst the task is daunting, the insights gained could prove pivotal in finding new ways to treat cancer.

Through proper analysis, a specific pattern may emerge that can then be used by doctors to tell healthy patients if they may be at risk of developing cancer later in life. As any doctor will attest, early detection is key for survival, and stopping the problem before it has a chance to spread could reduce deaths by a significant amount.

But it’s not enough simply to analyze this unstructured data and pick out key data points. That data must also be applied to the correct treatment.

Some researchers have been using

IBM’s Watson artificial intelligence to prescribe the correct treatment for specific patients. Since every person is different, the same treatment may not always be as effective for everybody. Big data analytics helps researchers and physicians find the best treatments and drugs for each individual person. The data collected from these treatments can then be collected and shared with other doctors across the country to help match patients with treatments. With these improved methods for helping cancer patients, survival won’t just become a matter of percentages.

It is this effort to share big data that remains a vital component of all cancer research. If a breakthrough is made in one corner of the world, it needs to be shared with other medical professionals. Comparing results of different research efforts will make the big data analysis more accurate. That’s one of the ways Foundation Medicine is contributing to the fight against cancer. The group is collecting data on rare cancers, analyzing them within their enormous database, and sharing it with other doctors. Through a big data solutions comparison, this is far from an easy task, but it connects physicians with each other, giving them added resources to treat cancer patients, resources that weren’t available even a few years ago.

Much still needs to be done to make cancer go the way of polio, but there’s no denying that admirable progress has been made. Now that physicians and other medical professionals are using big data for cancer research, more knowledge has been gained into how the disease works and how it should be treated. We may be closer than we think to finally finding that elusive cure.

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As a result of the games, we are now seeing society becoming far more understanding and inclusive of the disabled community

Chris Pearson Partner, Big Cloud

2012 was a big year in Great Britain. The country was awash with national pride. Union Jacks proudly flying at full mast across most British streets and a feverish party atmosphere, as we hosted arguably the best ever Olympic Games. During the games, we witnessed the prominent rise and rightful public recognition of the athleticism of disabled athletes, competing in

big data innovation

the Paralympics and Special Olympics. Tanni GreyThompson, Ellie Simmonds and Lee Pearson are now just as likely to be debated as Britain’s best Olympians from those games, alongside Mo Farah and Bradley Wiggins, amongst friends down the pub. Whatever stigma previously existed was smashed to bits, as our heroic athletes brought home gold medal after gold medal.

15 As a result of the games, we are now seeing society becoming far more understanding and inclusive of the disabled community, but with the United Nations reporting that around 15% of the world’s population suffer with some type of disability, there is a growing feeling that we need to do more to help make the lives of people with disabilities easier.

Data science and artificial intelligence has come to the forefront of technology in the last few years, and several practitioners are taking a more philanthropic outlook on life, supporting people suffering with both physical and mental disabilities.

One of the areas where machine learning is playing a prominent role is the support of people suffering with Autism Spectrum Disorder (ASD), which is a condition suffered by approximately 1 in every 100 people, with men more likely to be diagnosed with the condition than women. It affects children at around age three, and results in the child having difficulty processing or engaging in human interaction or emotion, which can make integrating into groups of other children very difficult. However, the London Knowledge Lab was incredibly successful in introducing a group of ASD children to a virtual autonomous robot called Andy. The experiment found that the children interacted readily with Andy, listening intently, asking and answering questions far more freely than if it had been a human adult. Similarly, an artificial intelligence robot called Milo has been rolled out to over 50 schools in the United States to encourage children with ASD to interact more readily face to face, and in some cases, even permit physical contact from the robot, unthinkable for the majority of ASD sufferers. While there is no recognized cure for ASD at present, the experiment offers hope to the families of children and adults suffering with ASD, that they might one day be so comfortable conversing with artificial intelligence software that they grow in confidence in their interactions with other people.

Intelligent prosthetics is another area in which data science has been implemented in order to make the lives of disabled people easier.

For many amputees or people living without the use of one or more limbs, the options are very limited, and often very rudimentary,

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Google recently announced that they were looking to boost the accessibility of their smartphones and other devices for users who might have limited or no use of their sight, hearing or dexterity

focussing on bare essentials rather than everyday usability. However, Deka Research, headed up by Segway inventor Dean Kamen, have pioneered a prosthesis they call the ‘Luke’ arm (named in tribute after Luke Skywalker). The unique aspect of this piece of technology is that its modeled more closely on the internal workings of a human arm, with the mechanical equivalent of tendons, muscles and bones allowing the user a much more natural range of motion, closer to a human arm than to the hooks and claws that have been more prevalent in the last 20 years. The arm itself can be controlled in several ways: with microscopic nerve endings attached to the base of the arm, or controllers in the wearer’s shoes. The project was granted a significant financial boost by the US Army Research Office, but the prostheses are likely to still be incredibly expensive to produce on a mass scale, with some estimates at around the $50,000 mark. While this may not be the answer for everyone who needs a prosthetic, the range of movement and sophistication of technology on show here is a brilliant sign for the future, when similar technologies will be available for less cost, and potentially with even more uses.

A piece of technology that was brought to prominence by one of the world’s most famous scientists, Professor Stephen Hawking, the electronic augmentative and alternative communication system (AAC) has become integral to the lives of those people who are unable to speak due to conditions such as motor neurone disease or cerebral palsy. One of the latest innovations in this market, and still closely related to the ‘Equaliser’ device used

by Hawking from 1986 onwards, is the DynaVox EyeMax. This device uses Computer Vision techniques, via a front-facing camera to track the movements of the user’s eyes across the screen of commands, and can even be programmed to

big data innovation

use certain intonation in order for the speech to not be misconstrued.

It has the potential to use Natural Language Processing to give speech to a large number of people who may have lost the ability, but there are some limitations to the technology; each device must be individually programmed for the respective user, to include places of interest, names of friends and family and other unique information, and the cost of the devices is not often covered by health insurance. It is, however, expected that the inclusion of deep learning technology into the devices in future should reduce the time taken to program them, which could bring the cost of the devices down to a more affordable level.

It is not only specialist medical firms who have seen the value, both financially and philanthropically, of using data science technologies to improve the lives of disabled people. Google recently announced that they were looking to boost the accessibility of their smartphones and other devices for users who might have limited or no use of their sight, hearing or dexterity. One of the most exciting and unique developments is a Braille system for use with Google smartphones, which allows users to connect a Braille device to their phone via Bluetooth. In addition, Android systems can now be controlled by ‘switches’, not dissimilar to those used by Stephen Hawking in his wheelchair. This, according to Google, opens them up to a new group of users who previously would not have been able to access this technology, and allows users to use the same hardware as their friends rather than having something custom-made and potentially less appealing. There is a huge financial incentive to these developments, as both Switch Access and BrailleBack are free to download for Android, meaning that unlike many technologies designed for the disabled community, price

17 does not have to be a barrier. Overall, Google’s contribution has laid down a benchmark for other technology companies to follow their lead in producing affordable and easy-to-use platforms for those with disabilities, and we look forward to see who steps up next.

Finally, there is something that is definitely more at the ‘proof of concept’ stage right now, but shows enormous potential for the future. In 2011 Dr Dennis Hong, from Virginia Tech University’s RoMeLa Robotics and Mechanisms Laboratory, pioneered a car that was designed for blind or partially-sighted people to be able to drive independently. Using a system that combined machine learning software that was able to learn a predetermined route and sense obstacles and pedestrians, and a series of sensors packed into pressure-pad gloves, that would tell the driver where they should be going and if there was an impeding obstacle that needed to be avoided. During his TED talk in the same year, Dr Hong demonstrated the car with a blind person driving, doing a whole lap of a pre-determined course safely at the Daytona Speedway. It must be noted that there are a few reasons why this system could not be implemented immediately, least of which is that the route had to be meticulously entered into the car’s computer in order for the sensors to direct the driver, and this would not be feasible for every road. However, it shows enormous progress in the field of Data Science and Machine Learning, and a huge leap forward for the blind and partially sighted community as a whole.

There are several questions that must be addressed before the relationship between data science and disability can be labelled a success. Firstly, are the technological innovations that are beginning to emerge at an advanced enough stage to benefit those who are suffering from a disability now, or is the real breakthrough likely to come in 5-10 years? Similarly, are these options

all financially viable, not for the producer, but for the consumer, who might only have limited income as a result of their disability? Furthermore, are innovations offered by data science limited to making the lives of disabled people easier, or is there scope, for example, for deep learning to be used to identify the genes that cause certain disabilities, giving doctors the chance to study and limit the genes in the future?

The future for data science looks incredibly bright, as several innovations are already making marked differences to the lives of disabled people around the world.

The next step, whether that be into genetics research, deep learning integration or another field, is dependent on whether many of the major companies will look past the finance sheets and instead look to improve the lives of the many millions of disabled people all over the world. Let’s hope it doesn’t take until the next Olympic Games until they do.

One of the latest innovations in this market, and still closely related to the ‘Equaliser’ device used by Hawking from 1986 onwards, is the DynaVox EyeMax big data innovation

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Improving Patient Care With The IoT

Gabrielle Morse Conference Organiser, Internet of Things Summit

big data innovation

Away from being able to change the temperature in your house or restocking your fridge if you've run out of something, the Internet of Things (IoT) is largely unknown amongst the general population. The potential it has, though, is immeasurable, and we are undoubtedly going to be seeing the full effects of it in the coming years, when it will become considerably more prominent for every person.

19 However, we are already beginning to see its importance in probably the most important area of our lives right now; healthcare.

The use of the IoT in healthcare was put on the map by the work of the Michael J Fox foundation and Intel, who have begun to give sensor-filled wristbands to those suffering from Parkinson's Disease with the idea of collecting data about the disease 24 hours a day across millions of patients. This data is then fed back through the Cloud where it can be analyzed to help identify new potential treatments.

This project started in 2014 and is ongoing, but since its inception, the use of similar technologies across the world has expanded rapidly. The use of the IoT in healthcare has even been predicted by MarketResarch. com to be worth $117 billion by 2020. It is for this reason that AI and smart systems, like the IBM Watson, have come about, with the large number of sensors and vast amounts of data created being searchable and useable in a simple system.

One of the key issues with healthcare at the moment is that it is labor intensive and the smallest human error can have devastating consequences. The IoT has the potential to alleviate many of these pressures from the healthcare industry. Some hospitals have already begun to implement 'smart beds' that can detect whether or

not they are occupied. They can also help to support patients in the most comfortable position without the need for manual adjustment. This frees nurses and health practitioners from fairly mundane tasks and allows for both more practical treatments and meaningful face-time with patients.

Whilst the use of the IoT in hospitals is becoming more popular, arguably its use outside of the hospital setting is where it is going to have the most impact. Similar to the work done by Intel and the Michael J Fox foundation, the ability to track health metrics in a relaxed, home setting all the time makes diagnosis and monitoring more effective.

Not only this, but with the financial pressures on healthcare providers it allows them to care for patients without incurring the costs that come from having them in a hospital bed, estimated to be $1,700 daily. Often the reason for extended hospital stays is not for constant treatment, but simply for monitoring. With intelligent sensors feeding data back to doctors, it means the patient is in a more comfortable environment and the healthcare provider spends less money.

It may also help to avoid issues revolving around the use of pharmaceuticals, both allowing doctors to make sure that complete courses of medicines are taken, and that the drugs being used are genuine.

The size of the global counterfeit pharmaceuticals market is almost impossible to establish, but it is generally understood that the problem is increasing. Operation Pangea, Interpol's counterfeit pharma task force, seized 2.4 million fake medicines in 2011, and that number surged to 20.7 million in 2015. To help tackle this problem the FDA set out guidelines for RFID tags to be used on pharmaceuticals, helping to show where they came from and their authenticity. This information can be automatically scanned at every stage of the supply chain, showing the end user exactly where their medication has come from. If these were to work in the same way as we have previously seen with the RFID tags on bottles of spirits, each would have be unique and non-reproducible, making counterfeiting almost impossible.

Similarly, when it comes to the taking of medication, the IoT and smart sensors could help to make sure patients are actually taking theirs.

It is a technology being developed

by WuXi PharmaTech and TruTag Technologies, who are developing smart pills that know when they are taken, making sure that full courses are being taken and mitigating doctors and pharmaceutical companies from fraudulent court cases. Aside from the inconvenience of strung out cases, it will also drive down overhead costs, with these savings potentially being handed down to patients.

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There have been several developments made in the field of baby-centric apps, such as ‘Sproutling’, which works very much like a FitBit; measuring vital signs and allowing a parent to monitor their child in mind-boggling detail

Nurture by Numbers Kit Feber Partner, Big Cloud

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ver the past five years, Big Data has been used in many areas of twenty-first century life as a means of making daily processes simpler, quicker and improving outcomes. Whether it be the automotive industry, online shopping or professional sport, it is almost impossible to find an area which has not been affected by the rise of the Big Data phenomenon. However, one group which has been protected from the effects of this Big Data explosion is children, as many parents have, to this point, been unwilling to share their children’s personal medical data. However, with rising pressures brought on by increasing medical costs, competition for school and university places and an everwidening poverty gap, more and more organizations and individuals are turning to Big Data and Analytics to try and give their big data innovation

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children the upper hand. This is not a decision that has been taken lightly. It is a means of ensuring that each child is given the best chance of succeeding in life in a world where the odds are stacked against them.

A twenty-first century birth is far removed from a birth 30 or 40 years ago. From the moment that the foetus begins life inside the womb, it is regularly being monitored for its heart rate, its pulse, and increasingly importantly, whether it is developing any potential birth defects. When the baby is finally born, it almost instantly develops its own digital footprint, not only in its medical data, but through the many photographs that will be taken and uploaded to the web within hours of its birth. There have been several developments made in the field of baby-centric apps, such as ‘Sproutling’, which works very much like a FitBit; measuring vital signs and allowing a parent to monitor their child in mind-boggling detail.

Much of this data can be collected and then used to compare a child against others of the same age in the same country, allowing parents to know if their child is developing at the same rate as those around them.

With the spread of diseases and airborne pollution an ever-growing concern, many hospitals are following the example of Seattle Children’s Hospital and utilizing data to treat their young patients more efficiently. SCH partnered up with technology giant IBM to utilize massive volumes of their hospital and patient data which had previously gone unanalyzed, and improve care for its patients.

The hospital needed a consolidated platform on which they could view and collate patient data in order to find solutions more quickly. IBM’s PureData system is a dashboard that can track each patient and their respective data across every visit to the hospital, and can identify similarities in symptoms and treatments. This platform helped Seattle Children’s Hospital to cut query times by between 50 and 100 percent, meaning that more patients have been able to be treated in a much shorter space of time. In addition, the patient’s data, if they have chosen to pool it, can be used to try to find treatments for conditions such as meningitis, and these treatments can then be shared with other hospitals around the world.

Should children have a digital footprint on the internet before they are old enough to understand or agree to it?

In the early stages of any child’s life, there are two main areas which parents look forward to more than any other. One is the child taking its first tentative steps, and the other is when the child finally utters the first sounds which vaguely resemble human speech. MIT Professor and Twitter Chief Media Scientist, Deb Roy didn’t want to miss this moment at all. And, using his family as test subjects, he recorded over 90,000 hours of home video footage using fish-eye micro-cameras located in every room of his house. Dubbed ‘The Human Speechome Project’, he was able to monitor the sixmonth period it took for his son to go from saying ‘gaagaa’ to ‘water’.

He was also able to virtually map the house, and cross reference this information with where certain words were spoken most frequently, such as ‘bye’ near the front door, to find how his son learnt the

big data innovation

22 meaning and context of certain words. This extraordinary research and its discoveries would not have been possible without the capture and analysis of data; it truly shows the potential of the technology to improve children’s lives.

MIT Professor and Twitter Chief Media Scientist, Deb Roy didn’t want to miss this moment at all, and so using his family as test subjects, recorded over 90,000 hours of home video footage using fish-eye micro-cameras located in every room of his house

To this end, toy companies such as Mattel are starting to produce toys that can listen to and communicate with the child, utilizing the power of Deep Learning. Mattel’s ‘Hello Barbie’ listens to the child’s questions, responding using a bank of over 80,000 answers in under a second. There have been question marks raised about the privacy of the data recorded by the dolls; Mattel have tried to allay fears by saying that any data is securely stored at their headquarters, and only used to improve the products and service in the future. However, many are concerned that if personal details have been innocently mentioned by the child or its family in ‘earshot’ of the doll’s microphone, this sensitive information could be sitting on a database at Mattel HQ. In the midst of revelations surrounding mass surveillance in recent years, it is understandable that there is a certain degree of hesitancy with which people accept that this is the way forward for the toy industry.

One of the areas in which Big Data could have a hugely significant impact in the long term is in schools.

Analytics is already being used in the United States on a more widespread basis to compare the performance of different school districts in certain criteria, such as average test scores and internet use per week for certain age groups.

This information can then be used to improve the performance of the students and the staff. In some US schools, an EWS (Early Warning System) has been trialled. This system tracks the performance of individual students during their time at school and offers a score based

big data innovation

on their aptitude, which reflects the likelihood of the child succeeding at high school and college. The system means that students with lower scores can be monitored more closely in order to improve their score and increase the chance of educational success. However, the system’s critics, of which there are a considerable number, suggest that attempting to predict a child’s future successes through their schoolwork is inappropriate, and doesn’t allow the child to develop normally and naturally as a result of the constant monitoring.

There are several points that must be addressed before we allow the development of children to be influenced by Big Data worldwide.

Firstly, should children have a digital footprint on the internet before they are old enough to understand or agree to it? For example, should the many hundreds of photos of newborn babies be uploaded to social media channels like Facebook, when the child in question isn’t even aware of their photo being taken?

Furthermore, how should the issue of sensitivity be tackled when it comes to using personal medical data from children for the purpose of treating disease and illness? Is it right that parents should refuse to share their child’s medical data anonymously, which could potentially help save other children’s lives, if they are uploading images of their children to the internet?

Finally; using data to 'diagnose' chances of success or other character traits may in itself change a child, by adding pressure or providing excuses, especially when there is no one fixed definition of success. It would appear as though Big Data is set to be inextricably linked to almost every area of modern life in the next ten years.

When it comes to children, it may be best to consider where it might do good and where it might do harm before we let the markets define it.

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channels.theinnovationenterprise.com/authors contact ghill@theiegroup.com big data innovation

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The Key To Becoming A Data Driven Organization Shirley Lam Conference Organiser, Big Data, Singapore

With the constant drive for improvements, efficiency savings and speed of service, it is not a surprise that the use of data within companies has become one of the key themes running through successful organizations. In fact, the two most valuable companies in the world - Apple & Alphabet - can lay much of their success at the feet of effective use of data.

When companies look at these organizations, or Facebook, Uber and AirBnB, they see data machines, companies that can rely on the data they have. It is therefore no surprise that leaders across the board want to create a data driven organization, where decisions can be made through efficient use of metrics. However, achieving this is not as simple as starting an analytics program, it requires the entire company to focus on data.

To do this, you need to follow three key principles:

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25 Focus: Look at the right KPIs and know the difference between your data and your metrics.

Use outputs that can consistently be measured and which align with your core mission.

Often companies fall into the trap of collecting as much data as they can, then hoping that at some point in the future they will be able to make sense of it. This seldom, if ever, works as it creates a messy and unworkable system. The companies who have become truly data-driven are the ones who have identified early on which core metrics they need to look at, then built around them.

It is not to say that in the future a company will not expand into other areas, but trying to look at too much too soon means spreading yourself too thin and missing many of the fundamentals.

Speed: The rate of your insights must match the rate of your decision making.

The speed of decision making today is as important as making the right decision in the first place. A correct decision that's a few days too late will be the equivalent of making the wrong decision in a shorter amount of time. It is the concentration on the speed of decisions that has seen the rise in the use of in-memory systems like Apache Spark.

Some companies have realized that their legacy systems are simply not fast enough to give them the data they need at the time they need them.

When companies look at these organizations, or Facebook, Uber and AirBnB, they see data machines, companies that can rely on the data they have

Facebook have got around this through creating their own in-memory system; Scuba. Given that the site has over 1.5 billion active users, using traditional systems to analyze data across the site would take days, if not weeks, to analyze.

Therefore, the Scuba system was created to effectively utilize their data in a timely fashion.

Lior Abraham, Senior Software Engineer at Facebook at the time of Scuba's creation put the need for system clearly 'Traditionally, we’ve relied on pre-aggregated graphs or tools that query from samples stored in a MySQL database. While these approaches were often sufficient for basic queries and small datasets, as data grew in size, and as we needed to ask more sophisticated questions, they became way too rigid and slow.'

Empathy: Interact often with your customers/ members/beneficiaries and understand what success looks like for them.

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A correct decision that’s a few days too late will be the equivalent of making the wrong decision in a shorter amount of time

To become a data driven organization, the company needs to have an ultimate purpose.

Creating a system where you can see metrics quickly and clearly is not useful unless you know firstly what they represent and then what you are aiming to achieve with them.

For customer facing companies this often means a better user experience when people are using your product or site.

Knowing what your customers/ users want is not just about looking at the data and making educated guesses, but about finding out directly from them what they want and then using the data you hold as an indicator of how you can achieve it. Amazon are a clear example of this, through using data they have

big data innovation

managed to optimize the use of their sites through elements like recommendation engines, button placement and even the 'buy it now' button. This came from talking to customers and understanding what drives them, rather than what the company wants to drive them.

A company can never be truly data driven until it is genuinely being driven towards something, and the only way to know where it is being driven is through finding out where people want to go. Clearly, some of this will come down to innovative ideas, like the iPhone, where people don't know what they want until they have it, but these examples are as famous as they are rare.

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Big Data & Analytics Festival

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Big Data Innovation Summit Internet of Things Summit Predictive Analytics Summit HR & Workforce Analytics Innovation Summit

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