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As the Internet of Things (IoT) continues to steer operations in the 21st century, numerous challenges are coming to light.

While the IoT still has the potential to transform business for owners, employees and customers alike, those who already embrace this next-gen network still have some work to do.

Not only are they trying to make the most of IoT integration to benefit their own company, but they’re also treading new ground and serving as role models for those who have yet to take the plunge.

1. Meeting Customer Expectations

In the 1990s, the widespread availability of internet access forever changed the way consumers shop. It also switched the customer’s focus from standardized, mass-produced goods to customized products and services.

With the year 2020 on the horizon, customers have higher expectations than ever before. According to a recent report by Salesforce, 57 percent of consumers are more interested in doing business with an innovative or forward-thinking company — and 50 percent won’t hesitate to switch brands if their needs go unmet.

2. Easing Security Concerns

The IoT was initially touted as a hyper-secure network that was suitable for storing and transmitting confidential datasets. Although it’s true that the IoT is more secure than the average internet or LAN connection, it’s not exactly the bulletproof shell some users expected.

Some of the most significant security concerns involve both the IoT and the cloud. A recent analysis predicts a loss of up to $120 billion in economic fallout in the takedown of just one cloud datacenter.

Reports also state an annual economic cost of cybercrime at upward of $1 trillion — which is quite a leap for 2017′s record-setting figure of roughly $300 billion.

3. Keeping IoT Hardware Updated

Regardless of how a company uses the IoT or the cloud, data integrity is a common challenge. With so much data coming in from multiple sources, it’s tough to separate useful, actionable information from irrelevant chatter.

It’s critical to calibrate your IoT sensors on a regular basis, just as you would any other kind of electrical sensor. Next-gen sensors are embedded in many different devices, including panel meters, chart recorders, current clamps, power monitors and more, and it’s difficult to synchronize the dataflow between all this hardware without the help of a professional team.

4. Overcoming Connectivity Issues

In its current form, the IoT utilizes a centralized, server-client model to provide connectivity to the various servers, workstations and systems. This is quite efficient for now, since the IoT is still in its infancy, but what happens when hundreds of billions of devices are all using the network simultaneously?

According to updated reports from Gartner, more than 20 billion individual units will connect to the IoT by 2020. It’s just a matter of time before users start to experience significant bottlenecks in IoT connectivity, efficiency and overall performance.

5. Waiting for Governmental Regulation

While some businesses immediately embraced the IoT, others are hesitant. In many cases, these businesses are waiting for government officials to intervene with new standards and regulations.

However, since the IoT, the cloud and even the common Internet aren’t tied to one specific city, state or region, who is responsible for setting these regulations?

Complicating matters even further is the sheer amount of IoT-connected devices. Since these devices originate from many different sources, including international partners and vendors, how does a localized regulatory agency control the quality of incoming shipments?

Although most experts agree that IoT regulation is a necessity, they have yet to formulate any standards or guidelines for the public to follow.

Making the Most of the IoT in its Current State

Despite the challenges and bottlenecks of the IoT in its current state, it still has many benefits in today’s business world.

It’s useful enough that some are willing to throw caution to the wind and make the transition to the IoT — despite all the challenges it provides — to get a jumpstart on their competition before it becomes the next big thing.

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While there remains no cure for Parkinson’s Disease as of yet, Big Data is making major strides in grossly expanding what we know about the disease—and thus making a cure more likely in years to come.

Parkinson’s doesn’t just affect old people, either, as many people once suspected. More recent cases, such as those of Muhammed Ali and Michael J Fox, have helped prove the fallacy of that once commonly-held assumption.

Still, far less is known about the disease than is known. For instance, Parkinson’s is usually diagnosed through a series of 15-minute appointments. Given that Parkinson’s regularly varies in severity, however, those 15-minute appointments may not give doctors or a treatment team a very thorough picture of a patient’s disease.

Andy Grove, a former CEO of Intel, however, is helping researchers making strides—with the use of Big Data. In the last few years, Intel has teamed with the Michael J Fox Foundation to gather data from Parkinson’s patients, with the aim of using that data to better understand the disease.

The project uses a Cloudera-based platform on an Amazon server, gathering 9.7 terabytes of unique data every day, based on the following:

• 10,000 patients are participating in the study
• Each patient wears tech that monitors their steps, sleep schedules, and speed of movement, among other pieces of data; a full 300 measurements are taken every second
• This results in 1 gigabyte of data recorded for each patient each day

The data stored on the Amazon server, then, is automatically made part of a central database which has been made freely available to researchers and data scientists.

This vast data set, in turn, allows researchers the ability to sort through it with dedicated algorithms, searching for patterns, correlations, and other relationships that help them better understand Parkinson’s disease. This is the sort of work that would clearly have not been possible even 5 or 10 years ago, but the advancements of wearable tech have made it far less scary for Parkinson’s patients.

Most amazing, though, is the sheer quantity of data, and what it allows researchers to sift through. The development of ever-increasing computing power allows researchers more and more capacity to sift through enormous quantities of data, better allowing them to see patterns and relationships as advanced algorithms can work through that data—and that, in turn, helps create meaningful and actionable reports.

While this has been occurring for years in sports and athletics, only recently are we beginning to understand how much Big Data can do for medicine and medical research. Parkinson’s disease research is only one of those frontiers, and as more of that data is analyzed, we may well find ourselves well on our way to a cure.

Click here to read more about Parkinson’s disease. Published with permission.

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For a very long time, women working in the fields of science, technology, engineering and math were unwelcome and underappreciated. Take for example the story of Katherine Johnson and her colleagues, who made remarkable contributions to the early years of NASA’s space program. The world had not even heard of her name until two years ago, when the movie, Hidden Figures, hit the screens.

Sadly, it is still a man’s world in the STEM fields, and women struggle every day to find a strong foothold in it. The disparity between the number of men and women with successful careers in STEM is unfortunately large. But is shrinking. Initiatives like Girls Who Code and Technovation are changing that equation. As well as talented individual women who have managed to make their mark in the world of data and technology.

These women are role models for the little girls who will follow in their footsteps. Because it important for dreamers to have stories of people who inspire them. The knowledge that someone has gone through the same struggles and has managed to conquer them is comforting.

So here is a list of twenty amazing women who have excelled in the world of AI, machine learning, data science and big data. And they are but a handful of a large community of women who are nothing short of inspirational.

1. Silvia Chiappa is currently a senior research scientist at Google DeepMind. Armed with a PhD in machine learning, her research interests lie in Bayesian reasoning, graphical models, approximate inference, time-series models, deep learning and reinforcement learning. Having co-authored several papers on a range of topics, she also served as one of the editor for the book, Bayesian Time Series Models.

2. Jana Eggers is the CEO of a neuroscience inspired AI company, Nara Logics. With more than 25 years of experience in the world of technology and leadership, she has previously been a part of American Airlines, Lycos, Inuit’s corporate Innovation Lab, Los Alamos National Laboratory among several others. She is a speaker, writer, marathoner and mentor on AI and start-ups.

3. Nikita Johnson is the founder of an events organizing company called RE•WORK. Being driven by a team which is majorly made up of women, the company specializes in organizing events that brings together breakthrough technology, cutting-edge science and entrepreneurship. With a background in international development and urbanization, she went on to start the company based on her mission of marrying emerging technology with smart entrepreneurship to tackle global challenges and shape a better future.

4. Fei-Fei Li is a renowned academic in the field of computer vision, a branch of AI which focuses on teaching computers to recognize objects in images. She is the director of Stanford University’s Artificial Intelligence Lab, as well as an associate professor in the university’s Computer Science Department. Currently, Dr. Li is also the Chief Scientist of Artificial Intelligence and Machine Learning at Google Cloud.

5. Jia Li is another well-known researcher in the field of machine learning and AI and is the Head of R&D at of Google Cloud AI as well as the President of Google AI China Centre. Previously, she was the Head of Research at Snap Inc., the parent company of Snapchat.

6. Manuela M. Veloso is a Herbert A. Simon University professor and the Head of Machine Learning Department in the School of Computer Science at Carnegie Mellon University. And is regarded as an international expert on AI, robotics and machine learning. She was the co-founder and former president of the RoboCup Federation, as well as the president of Association for the Advancement of Artificial Intelligence until 2014.

7. Cynthia Breazeal is the founder and Chief Scientist of Jibo, Inc., a company which specializes in building social robots, and a pioneer in Social Robotics and Human Robot Interaction as well. Her research focuses on developing the principles, techniques, and technologies for personal robots that are socially intelligent and can interact and communicate with people on human-centric terms. She also spends some of her time as an Associate Professor of Media Arts and Science at MIT, where she founded and directs the Personal Robots Group at the Media Lab.

8. Daphne Koller is considered a leading scientist in the field of artificial intelligence. She initiated and piloted the online education platform, Coursera, which offers online course from top universities and organizations to the general public. She was also the former Chief Computing Officer of Calico Labs, which she left earlier this year.

9. Rana el Kaliouby is regarded as the pioneer in emotional AI or emotional intelligence (EQ). A co-founder of the start-up Affectiva, she invented the emotion recognition technology, which the company uses in their mission to humanize technology with artificial emotional intelligence. Before founding Affectiva, she was a research scientist at MIT Media Lab, where she spearheaded applications for facial coding to benefit mental health, autism and other research areas.

10. Carol Reiley describes herself as an entrepreneur, roboticist and an executive. She is the co-founder and President of the AI self-driving vehicle start-up, drive.ai. She is an academic research graduate from John Hopkins University, and has also earlier founded Tinkerbelle Labs, consisting of low-cost DIY healthcare hacks. She has also authored a children’s book, Making a Splash, about growth mind-set.

11. Karen Matthys is the Executive Director of External Partners at Stanford’s Institute for Computational and Mathematical Engineering, where she develops relationships with companies and national laboratories interested in computational mathematics, data science and visualization, machine learning and high-performance computing. She is also Principal at Indigo Partners, a marketing and business strategy consulting firm, since 2001.

12. Jill Dyché has been thinking, writing, and speaking about business-IT alignment for over two decades. She is the vice president of SAS Best Practices, where she leads client strategies and market analysis in the areas of data governance, business intelligence, master data management, CRM and big data. She has also authored several well-received books, over a range of topics.

13. Megan Price is the Executive Director of the Human Rights Data Analysis Group, an organisation which leverages statistical analysis to surface evidence for use in testimony to push for action and change. Armed with a PhD in biostatistics, she designs strategies and methods for statistical analysis of human rights data for projects in a variety of locations including Guatemala, Columbia and Syria.

14. Neha Narkhede is the co-founder and Head of Engineering at Confluent. Previously an engineer at LinkedIn, she is one of the initial engineers who created the open-source software platform called Apache Kafka.

15. Carlie Idoine is the Research Director for Business Analytics and Data Science for Gartner for IT leaders. With more than 25 years of experience in both business analytics and data science, she provides a unique blend of business and industry knowledge and is leading successful efforts to bring together new technology and effective business solutions.

16. Claudia Imhoff is an internationally recognized expert on analytical CRM, business intelligence and the architecture to support these initiatives. She is the founder and President of Intelligent Solutions, Inc., a consultancy on CRM and business intelligence technologies and strategies. She is also the founder of Boulder BI Brain Trust, a consortium of independent consultants and analysts.

17. Judith Hurwitz is the President and CEO of Hurwitz & Associates, LLC., a research and consulting firm which focuses on upcoming technology big data, cloud computing, service management, software development, computing management, and security. She is considered a pioneer in anticipating technology innovation and adoption, and has authored eight books, including several in the “For Dummies” series.

18. Jen Stirrup is a well-known Business Intelligence and Data Visualization expert, author, data strategist and community advocate. She recently became a Microsoft Regional Director, and is also a SQL Server Most Valuable Professional (MVP), Director-At-Large (elect) for PASS, holding the Business Analytics Portfolio, and not to mention the founder of Data Relish Ltd.

19. Jen Underwood is the founder of Impact Analytix, LLC., an organization of integrated product research, consulting and technical marketing. She is recognized as an analytics industry expert, with over 20 years of experience in developing data warehouses, reporting, visualization and advanced analytics solutions.

20. Joanna Schloss is an expert in the Dell Center of excellence. She specializes in data and information management, drives product marketing tactics and strategy as director of product marketing at Datameer. With experience in both startup and G500 environments, Joanna has successfully launched numerous products, from business-focused analytic applications to data warehousing tools such as Business Objects Data Services.

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Big data is a strong currency in today’s climate in Canada, with companies and brands desperate to utilize it to streamline their own operations and save on costs, while delivering the optimum service. So it’s an important aspect of nearly every single sector – including the online casino industry. With any process of digitalization, data plays a part, and moving casino offerings to online platforms, and ramping up the advances in technology in line with the latest developments means that data has to play a part. But how important is big data when it comes to the online casino?

Well, gambling has always been a numbers game. And those with the most information on the accurate numbers usually ending up being the winners. So, in a world where information is king, big data helps casino establishments and websites to track and analyze customer data. Thereby, it can benefit the online casino industry by tailoring messages and offers to certain players and benefitting those who are interested in specific areas of casino e.g. slots, poker, or roulette.

Casinos can now also understand demographics of their consumers better, thereby catering to their preferences. For example, most younger players, who play slots and roulette, belong to a higher thrill seeking demographic. They are drawn to games that usually themed, with luck having a large hand in it and high stakes. That means huge rewards on winning.

By offering a bespoke service, the customer is more likely to be retained, and a greater profile can be drawn up to further personalize the service in a feedback loop. Moreover, greater odds can be analyzed for games and online gaming as a whole, based on what has gone before and how the industry is faring.

Data analysis can also help build gaming profiles based on tournaments and high-stakes games, such as with poker, and can offer potential players a greater evaluation of how well they might do. One game which has greatly benefitted by placing more odds in their players’ favour is poker. Online poker has caught on like a wild fire, both as a past-time and at a competitive level. Websites like SharkScope, provide its players with information such as statistics of other players, and latest trends. This helps them strategize effectively and improve on their own games.

Ultimately, big data can be used to monitor what people do and the journey they make on an online casino site through real time analytics to create a better offering for players and to create a sustainable advantage over competitors.

Another way in which big data helped level the playing field for bookers and players was by establishing more realistic odds. Using predictive analysis has made it easier for bettors to predict the outcome of games in tournaments, not only in a physical sport, but also in online contests. Players and bookers can determine betting amounts based on the information gathered and analysed from history, hence providing them with real-time odds.

Big data also helps with the games which online casino developers are creating. It has been well noted by developers that gamers tend to be drawn to certain games in certain patterns over gaming career. Big data and predictive analysis can identify and utilize these patterns to draw the attention of specific customers to specific game models. By analyzing data of how long games are played for, how long players are playing for, they can assess what aspects might keep a player playing for longer.

So, for example, when it comes to online slots, developers can see whether those with greater supplementary video and audio content, or those that invoke brands, or even just themes such as Slots Heaven’s offering of Ancient Greece or a Man of Steel film tie-in work better. And it can be seen that the big data analysis may augment or compound reviews, such as OCC’s review here, one of the biggest Review sites in Canada. By taking all kinds of data into account, a greater picture can be developed to create games that players will definitely want to play.

While big data may be used to a greater degree in other sectors and industries, it should not be neglected when it comes to online casinos. Businesses throughout Canada are harnessing the power of big data and seeing profitable results. The data can be used to personalize experiences on the site, create more impactful games, or to assess the use of online casino bonuses that might make certain players come back to the site to play again, or diversify the games they play.

Big data is definitely going to come more into its own as each sector realizes the power it wields and how beneficial it can be.

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Passwords are not just random texts that you need to gain access to your online accounts. These can completely change your life for the worse if they had gotten into the wrong hands.

An average person spends about 11 hours online each day. They chat with their friends, email their colleagues, play games, watch movies, or just catch up with their Facebook friends every day. And each time they open a website, they log in to their accounts, making their presence known to other people online.

If you think your password—which is probably the name of your pet, your birth date or your children’s names—is safe from prying eyes, think again. Most people don’t use a secure password, which makes them vulnerable to hacking. And when hackers are able to infiltrate one of your accounts, they may infiltrate all of them, including your online bank accounts and personal records.

And they can do all that by manually guessing your password or using automated programs to guess it. There are other ways of discovering a user’s password, but all these can be prevented, or at least slowed down considerably, by using a strong password. A strong password is your protection online.

Originally appeared here. Published with permission.

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It’s been a couple of years since the world was introduced to an exciting new career prospect. But the hype around data science still hasn’t died down. With it being predicted to remain the ‘hottest’ job for the next couple years, it doesn’t look like the buzz around data science would go away anytime soon.

And why not? Data science and its related job profiles have increasingly been the turning point of many enterprises. Quite a few companies have been hopping onto the data science band wagon in the hopes of better revenues and fresher business solutions.

However, as promising as it is, there are several challenges and difficulties which a data scientist has face in their career.

More than what meets the eye

Despite being one of the highest paying jobs in the software industry, everything is not all hunky dory in the world of data science. Studies show that nearly 13.2 percent of data scientists are looking for new jobs. As reported in an article published by the Financial Times last year, the field of data science has the second highest number of people who are unsatisfied with their jobs.

But why are data scientists so unhappy with their jobs?

At first, one tends to blame the candidates themselves. Due to the increased intertest, the demand for well qualified data scientists in industries is high. And so, individuals looking for jobs within the data science spectrum sure do have their fair pick of the crop. Which would lead them to be fickle and picky with the opportunities they explore.

However, the blame does not fall entirely on the candidate’s shoulders. A large portion of it falls on the companies who are looking to incorporate data science in their organizations.

Is the corporate world ready for data scientists?

With the interest around data science increasing, it is not surprising that every company wants to be a part of it. Data science is the cool new toy of the corporate world. And every kid on the block wants one.

However, being a relatively new field, not many companies know what to expect while setting up a data science department. Blindly following suit because everyone else is doing it, companies hire data science candidates without fully comprehending the necessity or the purpose behind it.

As Q McCallum mentions in his blog post, there needs to be a certain amount of preparation before a company should start hiring data scientists. Which includes compiling and preparing the data that must be analyzed. One of the major reasons why data scientists tend to a leave a company is the sheer amount of data that they are expected to sort through before they can begin the work their job profile calls for.

They end up dealing with poor quality of data, which include incomplete values, missing samples and poor representation of the samples they do have. This leaves them feeling discontented because their full potential as a data scientist is not realized.

On top of that, companies directly hire junior level data scientists with little to no experience in the field, since they do not expect much in terms of salary. But, without a senior to guide them, these rookies are left to navigate the large amount of dirty data on their own. This usually leads them to feel lost and frustrated. Eventually, they leave the company for more satisfying job opportunities.

The dilemma of a data scientist

Another major reason that drives data scientist to quit or change jobs is corporate politics.

Okay, admittedly, any politics in an organization can make anyone’s job a lot more difficult than necessary. However, since data science is supposed to have a direct impact on the improvement of revenue of the company, data scientists are often caught in the cross-fires of the upper management. So, it becomes extremely important for them to be on the right side of the right people.

Which means taking on a lot of additional tasks that have no relation to their job description. They become the go to person for anything related to data and numbers. And are expected to have the answers to everything at the right time.

For instance, data scientists are expected to translate the data into relevant points of action. Because, in all honesty, upper management is not interested in the numbers, but are interested how these numbers can be used to generate better revenue for the company.

And in enterprises which never had data scientists before, there would be certain amount skepticism from parts of the management. So, the data scientist must answer questions of individuals who do not buy in to their analysis and forecasts. Not to mention they are sent on a wild goose chase trying to sort and compile all the raw data in the first place. Which leads to resistance in data collection from the skeptics.

All of this put together can put a significant amount of stress on a data scientist.

In conclusion

In retrospect, the ultimate reason why individuals lose interest in their data science jobs is because the job never really lives up to their expectations.

When junior level data scientists first enter the field, they have a glorified image in their heads. They believe that they would be to solving complex problems using cool algorithms, and overall having a significant influence on business. And considering all the hype which surrounds the job description, it is not surprising that things tend to get a tad exaggerated.

However, after having mentioned some the challenges which data scientists face, it in no way means that aspiring data science candidates should be discouraged from pursuing a career in it. Borrowing the words of Jonny Brooks-Bartlett, a data scientist himself, the job can be fun, stimulating and rewarding.

If you think about it, every job available has their own set of challenges to overcome. What’s important is to find a place where you can fit in and enjoy what you do.

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