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4 Reasons Why We Need More Women In Big Data
OCT 2015 | #17
Big Data Top Trends 2016
Josie King looks at what we should Laura Denham takes a look at why it is expect to see change within big data in T H E Lfor E Athe D industry I N G VtoOincrease I C E I N the F I coming N A N C12Emonths. INNO VAT I O N | 24 beneficial female representation | 6
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San Francisco April 21 & 22 2016
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ISSUE 20
EDITOR’S LETTER Welcome to the 20th Edition of the Big Data Innovation Magazine
Welcome to issue 20 of the Big Data Innovation magazine. When we look at what big data can do, it is almost always through the context of how the technology helps us to perform analysis and come to conclusions. When you think about collecting data, then forming analyzable datasets, analyzing them and then finding correlations, they are all relatively simple things to do with effective software. However, what it cannot do is show why this correlation is showing what it does or even what this correlation means in a wider context. This is something that the FTC recently found in their study ‘Big Data: A Tool for Inclusion or Exclusion?.’ One of the most important findings was that ‘Companies should remember that while big data is very good
for detecting correlations, it does not explain which correlations are meaningful.’ In order to function properly, data needs to have human input and interpretation. To a database or computer, the number of people entering a shop is just a number, so the context in which the data is gathered is not understood. It could just as easily be the number of apples grown in an orchard or crimes in a neighbourhood. The correlations found between datasets are then just numerical patterns, rather than being contextualized. Without this kind of human interaction, data can be detrimental to a company, often creating bias and discrimination. For instance, in the same report the example is given of ‘one company determined that employees who live closer to their jobs stay at these jobs
longer than those who live farther away. However, another company decided to exclude this factor from its hiring algorithm because of concerns about racial discrimination, particularly since different neighbourhoods can have different racial compositions.’ So although big data has a significant part to play in businesses today, companies who rely on it too readily and without a human context can end up shooting themselves in the foot.
George Hill managing editor
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contents 6 | 4 REASONS WHY WE NEED MORE WOMEN IN BIG DATA
Laura Denham takes a look at why it is beneficial for the industry to increase female representation 9 | 5 BIG DATA TRENDS MARKETERS NEED TO KNOW IN 2016
With marketers becoming some of the key data consumers today, David Finkelstein takes us through what they need to know 12 | BIG DATA FOR THE GOOD OF SOCIETY
Big data is normally considered a corporate practice, but charities are increasingly using it to help with their work
18 | BIG DATA TOP TRENDS 2016
Josie King looks at what we should expect to see change within big data in the coming 12 months 22 | HOW MUCH IMPACT WILL BIG DATA HAVE ON HEALTHCARE
With all of the privacy issues surrounding the use of medical data, we look at how much impact data could have on healthcare 24 | IT PROFESSIONALS CAN BE THE WORST AT PROTECTING THEIR OWN DATA
Despite the threats from everywhere, recent research says that IT professionals could be their own worst enemy
14 | BIG DATA IS NEEDED FOR OUR NEW URBAN LANDSCAPE
As the urban population around the world increases we need to turn to data to help deal with the challenges this creates
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managing editor george hill
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contributors david finkelstein, xander schofield, josie king, elliot pannaman, gabrielle morse, laura denham, pearl cheng
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4 Reasons Why We Need More Women In Big Data
Laura Denham Data Evangelist
Big Data and Analytics have become a focus for several companies over the last few years, but one of the biggest challenges many face is in the recruitment of qualified employees to effectively implement new initiatives. Alongside this has come the increasing importance of a diversified workforce in all sectors of society, something particularly relevant due to ongoing inequality in many industries, especially tech and science. According to census data, 47.4% of the total labor force in the US is female and yet only 25% of STEM roles are filled by women. There are countless reasons for companies to make sure their data and analytics departments are made up of more women, we look at four below.
Practicalities There is currently a skills gap in the big data and analytics market, with McKinsey claiming that by 2018 we could see a shortfall of 140,000 to 190,000 applicants in the US alone. Quite simply, there are significantly big data innovation
more jobs than people to fill them and due to this, the industry and individual companies are suffering and will suffer even further in the future. From nothing more than a practical perspective, by employing more women and actively trying to get better female representation in relevant university courses, there will be an increased number of people to hire. Through creating more competition for jobs, the quality of
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There is currently a skills gap in the big data and analytics market, with McKinsey claiming that by 2018 we could see a shortfall of 140,000 to 190,000 applicants
the applicants will naturally increase and this in turn will result in new innovations and entrepreneurship amongst the most data-driven companies. Cutting out 50% of the population from a business area that is already struggling for enough applicants simply makes no sense when The U.S. Bureau of Labor Statistics predicts there will be a 24% increase in demand for professionals with data analytics skills over the next eight years.
Inherent Bias Although the differences between males and females are not as pronounced as many would have us believe only a few years ago, there are clear cognitive differences between the two genders. This has been shown consistently through the use of grey and white matter, as well as blood flow and even structural differences in the makeup of different areas of the brain, with women naturally having a larger hippocampus, or human memory center, therefore able to naturally take in more sensorial information. According to Psychology Today, ‘The female brain, in part thanks to far more natural blood flow throughout the brain at any given moment (more white matter processing), and because of a higher degree of blood flow in a concentration part of the brain called the cingulate gyrus, will often ruminate on and revisit emotional memories more than the male brain.’ This difference means that interpretations and biases between the two genders can be significant, especially when it comes to the analysis and input of data. This is not to say that one way of thinking is right, whilst the other is wrong, but more that through having two different perspectives there is likely to be a more accurate consensus created.
Early Development At present, data and analytics are in their formative years, having only really been effectively used in companies for the past 10 years. This means that the male/female balance that currently exists does not need to be something inherent in the roles, as it has become in the banking, financial and tech industries. For instance, of all Facebook employees, only 32% are female and of all the board members from the six largest banks in the world, only 20% are female. Through making sure that there are equal opportunities in data driven industries and departments at this early stage of career development, it will mean that the industry can develop and grow around a balanced and diverse workforce, rather than the typical male dominated environments that have caused significant problems for other more traditional industries.
Tech Sector Stereotypes Data based roles are normally grouped (rightly or wrongly) with the technology sector, and given the current controversy surrounding the number of women and minorities working within this sector, breaking away from this stereotype should be a priority. This needs to be done soon, as according to the Harvard Business Review, the percentage of women working in these areas has actually decreased since 1991 despite the area growing significantly. It is even more difficult to ascertain the reasoning for this as U.S. educational data shows no meaningful differences in math and science performance among more than seven million boys and girls in grades two through twelve, yet only 25% of STEM roles are filled by women.
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David Finkelstein Founder & CEO of BDEX
As marketing executives look towards 2016, shaping big-data focused strategies for customer acquisition is a key priority at many organizations. While 90% of organizations have a medium to high investment in big data, only 50% are ‘routinely’ applying big data to regularly engage customers. Some 44% report a ‘lack of consistency’ in omni-channel marketing campaigns. At companies with the technological basis for big data-driven marketing but a lack of consistency, targeting prospects and customers across multiple channels is likely to be an area of focus. Tech Journalist Craig Zawada predicts that in 2016, big data will shift from a marketing priority to a ‘main vehicle’ for driving growth in sales and revenue. As organizations increasingly adopt sophisticated personalization and targeting strategies based on first and thirdparty insights, organizations will likely have to choose between jumping on board these trends or risk falling behind. Join us as we review five rising trends that data-focused marketers should pay attention to in 2016.
1. Analytics Projects Will Stop Falling Short For many organizations, big data projects have been shaped by available data assets. Analysts at Tamr believe this trend will change significantly in 2016, as organizations ‘liberate’ themselves from ‘artificial constraints’ set by poor data asset availability or poorlyformulated business questions. Increased automation, better processes, and improved assets can allow organizations to stop participating in analytics projects that consistently ‘fall short’ of targets.
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2. Prescriptive Analytics Traditional business analysis thought dictates three stages to analytics adoption within an enterprise: • Descriptive Analytics: an attempt to answer ‘what happened’ • Predictive Analytics: using modeling to predict ‘what could happen’ • Prescriptive Analytics: the application of simulation to make business decisions Zawada predicts that in 2016, many organizations will reach final maturity stages and begin to apply prescriptive decision making to offer creation, targeting, and other segment-based marketing analysis. When prescriptive analytics is in full swing, an organization’s potential for marketing response can increase significantly.
3. Geo-Targeted, Programmatic Advertising Programmatic ad targeting has introduced a new era of automation for marketers competing in the Adtech space. Consumer adoption of mobile technologies has introduced the potential for locationbased targeting, which must be automated in real-time. AdTech writer Beth Principi writes that ‘combining location-based [big data] and programmatic’ will likely have a dramatic impact on outcomes for marketers in 2016. Gaining access
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to location-based streams of insight on consumers will be critical for organizations who hope to take advantage of this trend.
4. Static Dashboards Will Die CMO trust in static data dashboards is rapidly falling, according to recent research by Strata and Hadoop. As marketing executives realize the incredible potential of real-time big data, only 12% are willing to rely on static dashboard reporting for decision-making. This statistic represents a larger shift in the way organizations are thinking about data analysis and big data. The growing preference for dynamic reporting indicates that marketers are beginning to value real-time data as a tool for the most accurate insights. Subject matter expert Bob Gourley is in agreement, writing that organizations will begin using big data to ‘enhance agility and... market-dominating strategies’ in the year to come.
5. Mobile Takes Over Mobile is beginning to crush social media as a digital channel for marketers. Marketing writer Michael Della Penna reports that app downloads have begun to exceed new social media followers for major brands, which creates immense potential for targeted loyalty
experiences and personalization driven by big data. For companies with existing customer apps or new projects in development, big data strategies are likely to focus on: • Building existing customer loyalty • Delivering targeted offers for convenient purchase • Manage billing and electronic payments App-based integration of locationbased ‘beacon technology’ will be adopted by 85% of the leading retailers in the year to come. Organizations with the ability to integrate location and multi-channel insights will uncover new potential for highly-personalized marketing messages, which offer precisely what customers need in that moment. For organizations to remain competitive in the year to come, integrating high-quality, multi-channel data sources on consumer transactions, location, and web usage will be critical. As companies increasingly enhance their personalization efforts, organizations that drive results are likely to integrate 3rd and 1st party data sources from a Data Exchange Platform (DXP) for the most effective customer insights. Without access to the right kinds of big data, efforts to drive revenue through personalized mobile or programmatic, geotargeted advertising are likely to be meaningless.
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Big Data for the Good of Society It seems that all of the talk about big data in the past few months has been around how companies are misusing it, losing it or exploiting it for nefarious reasons. It tends to get mixed views within the press - use it too much and you are intruding, use it too little and you aren’t doing your job properly. Elliot Pannaman Head of Big Data Innovation Enterprise
However, there is one thing that even the most coldhearted journalist couldn’t criticise, using data for the good of society. One of the key ways in which this is manifested is through the use of data within charities. It is a practice that has increased significantly since the use of data systems has become more simple and less expensive. One of the most important ways that this shows itself is through the use of CRM systems such as Salesforce,
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which is used by Childnet, a London based charity who are dedicated to preventing online child abuse. The primary use of this kind of software is to not only sending emails, but also the collection, analysis and storage of data about their donors. It may seem like something that a corporation is likely to do, but the truth is that unless charities can earn money, they cannot operate. Data allows them to do this without begging for money or resorting to drastic measures, such as the
13 aggressive charity collectors you find on the streets. Data also has a key part to play in the work being done by charities. Through surprisingly simple data, it becomes possible to find the places where their work will have the most impact. For instance, if you look at the crime statistics of particular neighbourhoods, the type of crimes being committed can then dictate what may be beneficial there. Frequent violence may suggest a gang related charity could operate there effectively whilst a neighbourhood that has significant amounts of drug related arrests could benefit from a rehabilitation element. With the use of online mediums today, it is also possible for charitable organizations to use more complex data mining techniques, although given the expense and complexities surrounding this kind of work it is not found amongst most small-to-mid size charities. Going back again to Childnet, imagine if they had the opportunity to mine Twitter data to identify online harassment or exploitation of young people? It would allow for a far more comprehensive and far reaching programme.
Charities can also use their data in similar ways to private sector companies and increase efficiencies and productivities
charity could have money stolen by dishonest members of staff. Through a more data led process it would be much simpler to identify when this is happening and take action against those involved. In this case it has closed the charity completely, throwing those who relied on its services into uncertainty. Data has the opportunity to make a huge difference in communities across the world and as we have seen with some, it is already having a significant impact. As we move into a more data driven future, the skills required become more widespread and prices decrease, data may become the central cog to a successful charity. As it stands at the moment, those who have the opportunity to use it are certainly benefitting.
Charities can also use their data in similar ways to private sector companies and increase efficiencies and productivities. Given that funding for charities across the world has decreased since the financial crisis, the need to use the donated money to make the most change and making it go further is essential. Data can help to identify where there are elements that can be improved to increase the ROI for each element of the charity. Although the basis of the vast majority of charities is good, there are some who do not always operate properly, as we have seen from the recent Kids Company scandal where the money given to them may have gone missing or a
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Big Data Is Needed For Our New Urban Landscape Pearl Cheng Director, Smart Cities Innovation Summit
The concept of smart cities has been around for a while. The basis of it is simple - through using technology and data you can create a better, more sustainable urban environment.
According to the World Health Organisation, 54% of the global population live in urban areas, with the biggest growth shown in developing countries. The rate of growth is profound given that in 1960 only 34% of the global population lived in urban areas. These increases are unlikely to abate any time soon as they are predicted to be 1.84% per year between 2015 and 2020, 1.63% per year between 2020 and 2025, and 1.44% per year between 2025 and 2030. With this kind of growth in urban areas, the pressure on almost every aspect of urban infrastructure will be significantly increased; however, through the development of socalled ’smart cities', dealing with this pressure will be much easier. The success of this transformation will generally fall to innovative data initiatives - below we have outlined some of the key areas where it will have the biggest impact.
Disease Prevention One of the big issues with having millions of people in smaller areas, is that disease can spread considerably quicker than in a more dispersed population. Although increased urban populations may have greater resilience to common illnesses built through more exposure to bacteria and germs, there is a far greater risk of spreading new strains of diseases coupled with faster infection rates. It may be surprising to hear that during the Spanish Flu in 1918, 1 in 10 from urban populations died from the disease, whilst in some rural areas this ran to 9 in 10. This increased urban resistance was due to an earlier strain of the flu infecting more in the urban areas, making them more resistant to the later deadly strain. If doctors at the time had this kind of knowledge and the data to implement an effective prevention strategy, millions of lives could have been saved.
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1990
1990
5.5 mil
5.5 milcars cars
70 mil 70 mil 2010 2010 cars cars
this isthis an is an increase increase
of over
The increase of the number of cars on the roads in China, from 1990 to 2010.
of over 100% 100% in just 20 years in just
20 years
20.4 road deaths per 100,000 people
The amount of road deaths per 100,000 people in China.
Now nearly 100 years later, scientists are trying to harness big data to implement predictive strategies, allowing them to track the spread of diseases and potential cures or protections against them. One of the organizations doing this is Mount Sinai Hospital in New York, who have been using data to help track and halt the spread of Influenza in cities. By tracking how the flu moves through populations and how the virus mutates, they have helped to prevent it in the future. According to Sumit Chanda, Ph.D., CoSenior Author and Director (capitals in job title) of the Immunity and Pathogenesis Program at Sanford Burnham Prebys Medical Discovery Institute (SBP), 'Our research efforts are focused on finding unalterable host molecules—the ones within our bodies—that viruses hijack to spread and create full blown infections'. Through this, they are hoping to be able to prevent the annual spread of the flu throughout urban populations.
They are doing this through collecting data from thousands of patients, reported outbreaks (both current and historical) and also cross referencing this to mutations in the virus. This kind of work allows data scientists and doctors to predict how viruses will spread and the best ways to combat them. It will even be per 100,000 people possible to use predictive analytics to estimate how they could spread in urban areas, then take action to limit this.
20.420.4 road deaths
road deaths
Traffic Control China has seen a huge increase in the number of cars it has on its roads, with the country having only 5.5 million cars in 1990 compared to 70 million in 2010. This represents an increase of over 1000% in only 20 years. As infrastructure to cope with this increase is slower than the rate of growth, it has meant that Chinese roads are now some of the most dangerous in the world, with 20.4 road deaths per 100,000 people, compared to 11.4 in the US and just 3.7 in the UK. An unnamed city in Zhejiang, China worked with Intel to install 1,000 digital monitoring devices, 100 intelligent monitoring checkpoint systems, over 300 checkpoint electronic police, and more than 500 video monitoring systems, to deal with their 60% yearly increase in traffic. Through being able to monitor traffic and congestion more effectively, the city managed to improve traffic flow, increase prosecutions for trafficking offences and decrease road accidents. As similar initiatives spread through the US and Canada, collecting and analyzing a huge amount of data across smart cities to deal with the huge traffic booms, we are likely to be seeing even more innovation big data initiatives used in population hubs across the globe.
per 100,000 people
compared to the u.k and u.s The amount of road deaths in the U.K and the U.S.
11.4U.s
U.k
3.7
11.4U.s big data innovation
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3.
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Through surprisingly simple data, it becomes possible to find the places where their work will have the most impact
Waste Management Although it is not the most glamorous job, waste management is one of the most important elements to get right in any urban environment and data has a significant role to play in this. The Waste Disposal Authority in Manchester, UK, have a system that utilizes data in order to recycle as much of the waste created as they can. This reduces the land needed for landfill and also reduces the chance of environmental damage from garbage. By using weighing bridges as lorries come in and out of the processing plants from different areas, it is possible to find out how much waste is created in each location. This then allows them to create incentives for specific communities to recycle and reduce waste. Through using these kinds of metrics, it is also possible to reduce the environmental impacts of urbanization, as water, soil and air are tested for contaminates, allowing authorities to see changes over time and take steps to minimize future environmental problems before they have the chance to happen.
Housing Problems Without a doubt the biggest issue facing cities is going to be finding places for all these new people to live, especially in places where housing is already limited. Places like London have seen the property prices increase by around 250% in the last 20 years due to the decreasing amount of space for housing and the increased demand for it. Due to this, one of the main areas of housing that is being hit hardest is social housing. Here big data can have a profound effect, as the housing charity HACT has found, where it is currently collecting data from 400,000 houses to analyze for a variety of purposes. This could be anything from the
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improving repair of council owned properties through to how space is being utilized. The idea behind this is to use data to identify potential issues with design, construction or placement, which can then be rectified on those properties and avoided when constructing future buildings. This kind of information is not just useful in social housing though, as elements like use of space and degradation of materials over time, can have a profound impact on how wider housing projects are approached, allowing developers and cities to create accommodation that maximizes available space, whilst still allowing for a pleasant living experience.
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Top Trends in
Big Data
2016
Josie King President, Innovation Enterprise
As we move further into 2016, we have seen a considerable amount of change in big data and its perception. We believe that 2016 is going to throw even more up for the industry, so we are taking a look at what we think are going to be the top trends in the next 12 months.
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Quantum Computing To Grow The concept of quantum computing has been around for a long time, but has always been seen as something that we are going to see become a real possibility in some
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undefined future. However, 2016 may be when its use becomes more commonplace. After recent work by Australian researchers at the University of NSW it has become possible to code the machines in a more cohesive and understandable way. They have managed to entangle a pair of qubits for the first time, allowing for more complex coding to be created and therefore the use of quantum computers to potentially become more widespread.
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Through looking at the reactions of specific drugs in different patients, it will also give healthcare providers the best opportunities to provide the most effective treatments
2016 will not see the use of quantum computing becoming common, but its presence within data will become far more pronounced and some of the more experimental and forward thinking tech giants may begin to use it more frequently.
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AI & Machine Learning As the IoT moves steadily along the Gartner Hype Cycle, one of its most powerful foundations is going to become increasingly important and companies are likely to adopt machine learning and AI within their own systems.
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It will allow devices to automatically collect, store and analyze data, of which there will again be a huge increase in the next 12 months. Through the use of both AI and Machine learning, it becomes possible for these huge amounts of information to be processed, stored and mined without needing human interactions to do so. It creates the ultimate tool for modern data driven organizations and 2016 will see even more businesses realize this.
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Improved Security Scrutiny Data in 2015 has been in the media spotlight, but not for the ways that we would want. Unfortunately, the data hacks have become more common than many would have predicted, from the Ashley Madison hack to the TalkTalk hack, it has shown up that companies could do more to protect their data. 2016 will therefore see an increased scrutiny on how data is dealt with and protected. This will also come at a time when many countries around at the world are looking at implementing new data protection and data access laws, meaning that the waters are going to become increasingly muddied. Within this, companies will need to increase their security spending, improve database safety and prepare for seismic changes in the
way that hackers work. It is going to be a difficult year for data security, but it will build the foundation on which future stable and robust data security is created. Big Data To Become Small This is a two fold prediction. Firstly the use of masses of data as an indicator of success will turn to the quality of the data being collected. This will mean that the variety for each company is likely to decrease, but the specific data that will be collected will become far more efficient, useful and plentiful. As companies realize that most of what they collect isn’t being used and just taking up storage space, this will become more apparent and the use of this data will come under increased scrutiny. Secondly, the term big data is likely to become used more infrequently as a business function, instead this is likely to be broken down into the sum of its parts. Database management and data science technically fall under the same category at the moment, when the reality is that they are different. Companies are likely to realize this and use the term as a catch all rather than a function in itself.
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Analytics To Be Simplified & Outsourced We have seen the use of new data visualization and automation software breaking down the barriers between the data initiated and uninitiated. Through a continuation of this trend, we are going to see that conducting analysis on datasets becomes considerably simpler, we have already seen software that has a drag and drop analysis option available on tablets which is useable by almost anybody. This comes not only from the needs of the untrained, but because we are still in the midst of a skills gap in the data scientist market, meaning that companies need to look at big data innovation
20 how they can leverage their data without necessarily having the skills in house to do so. Therefore we have these pieces of software that can do relatively simple analysis for companies, but for the more complex analysis needed we are likely to see this being outsourced to companies who have the expertise. This is going to be a growth area in 2016 and we already have a number of companies leading the way in this regard.
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Data In The Hands Of The Masses Data is no longer just something being discussed in boardrooms and laboratories at the highest levels. Every day people get out of bed and look at the data collected on their sleep patterns, investigate what they are spending money on through apps or even just looking at the possession and running stats from their favourite sports teams. Data is now everywhere in our society, which means that the general population is becoming increasingly clued up on using it.
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It is not to say that the general population are going to suddenly become data scientists, but it means that the kind of data shared can become more complex as the understanding of it across a population increases. When discussing important matters, informed discussions can be had with data rather than conjecture. There will still be many who throw themselves at things with blind faith and gut instinct, but 2016 will see a growing segment of the population who can engage with matters through data in a way that they never could before, both through increased access and understanding of it.
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How Much Impact Will Big Data Have on Healthcare? Healthcare is an industry that can benefit significantly from the use of big data and analytics, although it is currently lagging behind in terms of uptake due to the restrictive policy-driven protection that surrounds medical data. However, as the ability to anonymize data has developed due to new technological innovations, the implementation of successful big data initiatives is likely to have an exponential effect on the industry. This data driven impact is a widely held belief too, with Health IT Analytics claiming that 95% of global healthcare leaders believe patient care is likely to change drastically.
Gabrielle Morse Data Curator
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This future may be closer than many people realize and almost every healthcare provider is utilizing data in one way or another at the moment. According to the Guardian, ‘Most healthcare organizations today are using two sets of data: retrospective data, basic event-based information collected from medical records, and real-time clinical data, the information captured and presented at the point of care (imaging, blood pressure, oxygen saturation, heart rate, etc).’ That being said, there are still several limitations to what can be done.
The reason behind these limitations come from poor practices in the past that have damaged the reputation of data use in healthcare. For instance, the UK’s NHS lost the data of 3,000 patients in 2013 and CNN reported that 90% of health care organizations in the US have exposed their patients’ data or had it stolen in 2012 or 2013. With these historical failures, people are wary of allowing medical organizations to access their personal information. If this taboo around data security can be broken, the benefits will be huge.
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Through looking at the reactions of specific drugs in different patients, it will also give healthcare providers the best opportunities to provide the most effective treatments
One of the biggest elements is going to be the ability to track and study diseases in ways not possible before. Through accessing the symptoms and specific elements of diseases in individuals, it is possible to track how they react to different strains of the disease, allowing for personalized treatment and diagnosis. Through looking at the reactions of specific drugs in different patients, it will also give healthcare providers the best opportunities to provide the most effective treatments. For instance, if a certain drug works better for 45 year old caucasian exsmokers than other groups, specific drugs can be created for this group, rather than using the same drug across all demographics.
THE NHS LOST
3,000
PATIENTS DATA
IN 2013
Real-time data analysis will also allow physicians to monitor their patients at all times, meaning telltale signs of bad health can be flagged and treated before they develop further. Alongside this are the benefits that come with monitoring healthy adults, as it can create the most accurate picture of effective functions, allowing for even small variances to be identified and analyzed. It is not only in the use of diagnosis and treatment that data will have an impact either. In a survey from Health IT Analytics of senior financial hospital executives, over half claimed that they have achieved significant ROI from adopting data in their payment systems. The performance of hospitals and doctors can therefore be better analyzed through analytics, leading to a better patient experience and better healthcare in the long run. Healthcare is in the midst of a data revolution, but it is being held back by recent mistakes mentioned above. Once healthcare providers have managed to create confidence in their ability to store data effectively, this will change and could open up a new era for healthcare.
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It Appears that I.T Professionals Can Be The Worst At One Very Important Thing: Protecting Their Own Data
Xander Schofield Big Data Commentator
Managing a corporate IT infrastructure involves protecting both the users and the organization’s data. This is a challenging task and in many cases, the IT professionals in charge of a company’s network are the greatest risk to its security. They pose significant risks precisely because they are techsavvy and have a lot of power. Below is a look at the problems posed by poor security habits and the ways in which they can be resolved. Because they are knowledgeable about IT, IT professionals tend to think that their knowledge is sufficient to handle all risks. The result is that they often ignore even the most obvious threats to the security of their IT infrastructure. For example, they might install their own antivirus software against the advice of their company’s software administration and information security experts.
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It is clear that much of the danger to a company’s data security comes from within
the problem of failing to update passwords Will Die According to a 2014 survey by Lieberman Software reported in Informationweek Dark Reading site, IT security professionals also often fail to update their organization’s service and process account passwords or only update them on an annual basis. Half of the survey’s respondents who failed to update passwords stated that they feared outages and downtime. According to Lieberman, the fact that they prioritize the prevention of downtime and outages over the prevention of security breaches shows that they lack awareness with regard to the destructive potential of a cyber attack. Another survey by Intermedia provided more evidence that IT professionals are among the worst offenders with regard to poor security habits. In their 2015 survey they found that many IT workers accessed their previous employers’ systems after leaving the company, shared logins among multiple users and were fine with installing applications without first consulting with their own IT department.
combating the greatest threat Based on the above surveys, it is clear that much of the danger to a company’s data security comes from within. This means that along with user education and data management, constant vigilance is necessary to secure IT infrastructure. IT professionals must therefore carefully analyze their network, monitoring everything from applications to devices on the network in order to identify activity that is unauthorized or insecure. They will use tools that allow them to monitor messaging, streaming media and a range of other traffic types. There are also security and network monitoring products that have regulatory compliance policies built into them that can be tweaked according an organization’s needs. Other measures include using firewalls that an IT security professional can tweak to filter messages for sensitive information. Monitoring a network has the following benefits: • It helps with spotting computer and network abuse. • It provides audit trails so that employees can be confronted with evidence of their insecure or otherwise suspicious activities. • It allows for suspicious traffic to be traced back to specific computers and users.
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In their 2015 survey they found that many IT workers accessed their previous employers’ systems after leaving the company
educating users While some users may resent having their habits called into question, most will eventually come around when it is explained to them why it is better for the company that they follow policy. An IT department can use the following advice to make the process of explaining the problems easier: The Problem Should be Approached Tactfully Users with large egos may strongly object and claim that the security risks are insignificant. They should be allowed to explain their actions as this may make them more willing to listen. The Nature of the Risk Should be Explained They must be made to understand why a practice like installing unapproved software poses problems such as incompatibility. They should also be educated on the issues that might arise when attempting to support software that is not approved by the company. The Organization’s Standard Operating Procedures Should be Used A copy should be kept on hand as problems are being explained to workers with bad habits. This is so that they can be shown the practices that have been approved and documented. No organization can afford to take internal threats lightly, especially when those threats are the result of poor habits from the very people in charge of the IT infrastructure. In order to remain competitive and profitable in the modern marketplace, a business will have to take network analysis and security seriously and invest in employee training as well as the right security products.
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Innovation Summit Speakers Include
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Melbourne January 28 & 29 2016
+ 61 2 8011 3007 dwatts@theiegroup.com www.theinnovationenterprise.com big data innovation
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