How is your information protected on social media? Is it protected? These are the questions we’re asking ourselves more and more, especially in the light of high-profile privacy scandals that demonstrate how vulnerable our data really is.
In fact, recent research from the Pew Research Center shows that the majority of Americans don’t trust social media sites; according to their “Americans and Cybersecurity” study, 51 percent of respondents said they’re not confident in the ability of social media websites to protect their data.
Despite waning trust and publicized breaches, we’re still logging in — and giving our information away. To better understand just what data we’re sending to social media providers, and determine how they keep our personal data safe, Varonis looked at the security blogs of three popular social sites: Facebook, LinkedIn, and Twitter. Check out the full infographic on social media security below.
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In the wake of the Federal Communications Commission’s announcement to repeal the 2015 net neutrality laws in the United States, there has been an air of general confusion and disappointment among the American public. Ajit Pai’s stubborn determination to completely undo the previous laws regulating internet providers, has sparked a secondary round of debate. One which was silent ever since 2015, when the laws first came into existence.
But despite the constant buzz around net neutrality, not many people have a clear understanding of it. And there are many varied views on the topic flying around. Considering the extent of divide between the advocates of net neutrality and its opposing force, it’s not surprising that the average individual would be confused. So, whether you decide to support net neutrality or not, here are a few facts to bear in mind before you jump into the argument.
1. Net neutrality is a principle, not a law. Much like the concept of freedom of speech, it is a fundamental right of internet users. Net neutrality is basically the principle of open and fair internet.
2.Ensures that Internet Service Providers (ISPs) treat online data equally. Without any discrimination between user, content, website, platform, application, type of attached equipment or method of communication. Consumers can choose the digital content they prefer to see, without the broadband providers limiting the options available to them or discriminating between certain content providers.
3.ISPs have divided opinions on net neutrality. Large ISPs, especially those in the U.S. strongly oppose the concept of treating all data on the internet equally. Companies like Verizon, Comcast and AT&T argue that strong internet regulation could negatively effect business for small and new enterprises. As well as wipe-out new competition. While bigger organisations like Google, Netflix and Amazon would be able to survive despite the regulations, they believe that net neutrality could curb innovation. Especially for smaller enterprises. However, this is definitely not the opinion of all the broadband providers. A group of smaller and local ISPs have joined together and have filed a lawsuit against the American government and FCC in a bid to retain the previous net neutrality laws. They believe that FCC’s repeal would benefit only the mega-established broadband providers. And adversely affect consumers, content providers and smaller ISPs.
4. Enterprises and websites are for net neutrality. Sharing a similar opinion as the small-time ISPs, tech, media and e-commerce establishments and websites believe that net neutrality is an absolute necessity. Of course, if broadband companies begin charging content providers more in exchange for better services, then already established giants would easily be able to pay the price by simply charging their customers more. However, smaller websites and companies, would not be able to do so. While giants like Google, Amazon, Facebook and Twitter have verbally displayed their support for an open and fair internet, they have remained somewhat aloof in their approach to the issue this time around. However, websites such as Tumblr, Reddit, Etsy and thousands more have decided to take a more active stance against FCC’s repeal. They have decided to be part of a campaign scheduled to take place on May 9th, ahead of the Senate vote.
5. Different countries have different approaches to net neutrality and data protection. Countries like Brazil and Portugal (and the United States before the controversial repeal) banned throttling and blocking of data/websites, but not zero-rating. In Japan, the government follows a fairly ‘hands-off’ approach to net neutrality, as the industry itself obeys voluntary self-regulatory measures. And in Australia there are no net neutrality laws in place. Instead, they have pretty strong consumer protection laws, which focus heavily on transparency on the ISPs’ half. Similarly, in India, the newly placed internet regulations focus on complete transparency.
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Companies all over the globe are embracing the big data revolution at a staggering pace. The latest data indicates that 53% of companies had adopted big data platforms by the end of 2017. That figure represents a 36% increase over a span of just two years. That giant leap has produced something of a talent gap in the modern workforce, with the need for data science skills far outpacing the supply of trained workers.
A lack of candidates with skills in data science was already apparent in 2015 before the adoption rate had even swelled to where it stands today. To meet the demand, companies have increasingly turned to hiring from within and skills training initiatives as stopgap measures. As it turns out, those methods may be the most effective way for businesses to acquire the exact skills they require, and existing employees hold the advantage of industry familiarity and intimate knowledge of their specific business. Here are three ways that companies can train up existing employees to meet their big data skills demand.
Big Data Boot Camps
For businesses that already have staff with programming knowledge, there are a variety of big data boot camps available to teach them vital data science skills. A boot camp refers to a short, focused intensive training program that is designed to enhance an employee’s skills in data science by building on their existing knowledge in programming or computer science related fields. Due to the current level of demand for data science skills, there are already a variety of boot camp programs available. Some offer broad, generalized training, such as those offered by industry leader Metis. Others are specifically tailored to individual industries such as healthcare, like the Insight Health Data Fellows Program.
Employer-Sponsored Degrees
Another common avenue of training is the creation of an employer-sponsored degree program for qualified employees. This method is particularly useful for companies that have existing employees that already hold bachelor’s degrees in computer science, mathematics, or statistics. In those cases, companies may choose to sponsor part-time education programs for employees to earn a master’s degree in data science. Such degrees are the most common level of educational attainment for today’s data science professionals, representing 64% of the data science workforce in 2017. There is a wide variety of master of data science programs available online, such as the one offered by James Cook University.
Application-Specific Certifications
There are some situations where a business needs to train employees for one specific big data platform or application. This is common for businesses that opt to outsource the majority of their data operations but lack the internal talent to make full use of the customer-facing components provided to them. Certification programs are available for a variety of applications, ranging from data visualization tools to programming languages like python. Online learning platforms like Coursera and Udemy provide customized instruction for just about any data science topic imaginable and allow existing employees to gain new skills at their own pace.
Staying Ahead of the Curve
By employing a mixture of these methods to allow existing staff to meet growing business demands for data science skills, companies can ensure a steady, reliable flow of qualified big data professionals within their ranks. They are also an excellent way to increase employee retention. In fact, 76% of the current generation of employees view professional development opportunities as an important part of company culture.
In a competitive field like data sciences, that’s a retention benefit that no business can afford to ignore. As big data solutions continue to permeate industries all over the world, creating effective ways to meet the demand for data science skills is fast becoming the business challenge of the 21st century, and for companies looking to meet that challenge, it should be comforting to know that there are plenty of accessible ways for them to do so.
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When California-based marketing software company, Marketo, conducted a survey in US, UK, France, Germany, and Australia in 2015, the results placed beyond any doubt the need for a change in marketing strategy. Random generic messages simply didn’t work for more than 63 percent out of 2200 people that took time to say what they find annoying about existing marketing approach. The survey showed that clients prefer to receive ads according to their overall interaction with multiple channels, not based on activities on a single platform such as Facebook, Google or any other isolated channel. It is clear that with so many products and services that offer high level of personalization, the customers are placing high levels of standard in personalizing content no matter where it comes from. This call was widely heard and statistics show that 92 percent of marketers admit they apply some form of personalization, with almost three thirds deploying their messages via email.
The backbone of hyper personalized marketing, a marketing strategy that relies on gathering data all the time from every place, consists of six main components, each adding its own value to the whole enterprise.
Data component
In order to hit the target persona with each message sent, marketers should actively work on gathering as much data as possible from all platforms. Data should consist of user interaction with the brand in order to craft the best possible solution for each persona individually. Additionally, data management is required in order to sort the data and remove outdated or irrelevant data. Lead scoring is in direct correlation with data, according to Eloqua, in order for lead scoring to be effective the data needs to be clean. Ultimately, data gathering seems to run smooth with the Millennial consumer generation, according to Salesforce. According to their research, around 63 percent of Millennials would gladly give their data in return for more personalized advertisement content.
Messages
With user data all in one place and arranged it is easy to generate hyper personalized messages for individual receivers. Each message containing specific offer or content that appeals to recipient’s needs. This component reduces the risk of sending materials that are not valuable or interesting to the potential client, thus improving the chances for sale. Per example, if a company is promoting a product that costs $450, the information from the data base allows insight into the list of leads that often buy that type of products at a $400-$500 range.
Personalized offers
According to previously mentioned Marketo survey it is estimated that 75 percent of consumers are more likely to take on offer that is personalized in that way so it depicts prospect’s most recent engagement with the brand. A good example how this aspect of hyper personalization works is famous coffee shop, Starbucks. What this company did is implement A.I. to generate push messages and send recommendations and offers to recipients using live data. The system is able to create 400 thousand unique offers, each based on users’ past purchases, activities, and preferences.
Multichannel approach
A study conducted by Advertising Research Foundation revealed that brands would have higher ROI (Return Of Investments) if they decide to channel their advertisement through multiple platforms. The study shows that a 19 percent grow in ROI can be expected if a brand conducts marketing on two platforms instead of one. Furthermore, the number grows higher, reaching up to 35 percent for brands that decide to channel their content through five different platforms. Continuous personalization through multiple channels can increase total consumer spending up to extraordinary 500 percent, according to statistics.
Perfect timing
Timing is everything in almost any business or endeavor. Contextual data can help figure out “hows” and “whys” of visitor’s interaction with a certain brand. However, through introduction of predictive algorithms it is possible to determine when is the best time to send a certain offer to a potential client. Moreover, hyper personalization influences the rate of “first call resolutions” as real-time collected data cuts down the time to process the client’s issue. This benefits not only clients but the company as well, because more clients are available to process this way.
Testing
It is difficult to understand which part of the content is the most compelling. However, usability and multivariate testing allow easier way to determine the effectiveness of each part of the component. Not only that, the tests could also compare each part of content individually and in various combinations to determine which combination shows the best results, which is more potent than simple A/B testing. Systematical live testing with actual visitors allows fully effective personalization at scale. Finally, the testing should be performed as often as possible in order to refine the content. All the time, content should go through adjustment process using the test results as pointers.
Final words
Harsh competition and the need to cut expenses gave birth to hyper personalized marketing strategy. Through time, companies embraced the benefits of such an innovative and proactive marketing approach that over 92 percent of companies use hyper personalization as their weapon of choice in the marketing jungle. The benefits are vast and quickly visible; the return of investments is significantly higher, the customer retention is improved, time is saved, etc. All these benefits consequently lead towards higher revenue, which is the core reason for any marketing campaign. Nearly half of interviewed marketers plan to increase their personalization budget for the next year, which is a clear signal how effective is this type of marketing strategy.
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The days of depending on banks and a limited number of high street forex brokers to carry out cross-border fund transfers are long gone. Now that this field is home to several FinTech players, sending money from one country to another is no longer as time consuming, complicated, or expensive as it was until a couple of decades ago. However, can using big data give FinTech alternatives a further edge?
It is possible that money transfer companies might not see the benefits of using big data to compile information of different kinds at this stage, given that aggregation of big data comes at a cost. This may be particularly true of companies that already have their fare share of repeat customers. However, a clear benefit of looking at big data in the right way is that companies can use the information they get to build better personal relationships with their customers.
Analyzing big data continually can also help overseas money transfer companies spot glitches and out-of-normal occurrences. Consider this example. U.S.-based Xoom relies on some of the top players in the big data world to analyze all the data related to its transactions. In 2011, the system detected an anomaly that might have missed the human eye. A criminal group was carrying out an exceptionally high number of New jersey-based Discover Card transactions to defraud the company, and they may well have passed off as being legitimate without big data analysis.
The Benefits are Far Reaching
Carrying out an international money transfer requires the exchange of information in different forms. Companies typically have access to the sender’s and recipient’s complete names, the countries and the currencies involved, the payment and transfer methods, as well as the transfer amount. Service providers, in all likelihood, also know the reasons behind most transfers.
Overseas money transfer companies can rely on the analysis of big data to formulate strategies by identifying underlying patterns. Businesses, for example, stand to benefit by learning why their customers favor one service over another, their frequency of transfers, how much money they send, and whether they transfer money to one or more recipients.
By aggregating big data, money transfer companies can also get insight into aspects such as timestamps, locations, and devices. They can, for instance, use the information to determine if customers prefer using their websites or apps.
M-Pesa is a mobile phone-based small-value money transfer company that is headquartered in Kenya. While originally only a money transfer company, it has now branched in into salary payments, purchase of goods and services, as well as micro-financing. Around 85% of Kenya’s households now use the services of this company in some form. By analyzing the big data it has access to, M-Pesa can get valuable insight into aspects such as disposable incomes and remittances.
Discerning the Useful From the Not
Big data brings with it information that is voluminous, to say the least. As a result, being able to sift through what’s important and what’s not is important. Ideally, money transfer companies should focus on specific points and aim to build personal connections with their customers. FinTech companies such as Azimo, TransferWise, OFX, and WorldRemit will benefit if they can manage to use their big data to identity and act on prevailing trends. For customers, having the ability to voice their needs will make them feel empowered.
Conclusion
The monetary benefits of analyzing big data might not be plainly visible to money transfer companies at the onset, but the potential the process holds gives businesses the ability to build long standing relationships with existing customers. Where there’s ongoing patronage, money follows.
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Singapore is quickly becoming Asia’s go-to hub for all things digital. The past couple years have seen a steep rise in the number of big data and AI start-ups from the city-state. Studies indicate that the data analytics industry contributes to nearly 1 billion Singaporean dollars to it’s economy each year. This is all thanks to Singapore’s Smart Nation vision, to be an economically competitive global city.
With this thriving new industry being facilitated by the government, it is not surprising that the start-up scene in Singapore is booming as well. And why not? Opportunities and talent are abundant. So, here is a list of big data analytics companies from Singapore who are thriving in this growing industry.
1. Aureus Analytics: First founded in 2013, Aureus Analytics is a Customer Intelligence and Experience company. The company helps insurers to provide superior customer experience leading to greater customer retention, loyalty and lifetime value. Their propriety products and platforms, Crux and Pulse, support the rapid enablement of big data analytics at the point of decisions. They are equipped with powerful analytical models, which are flexible enough to allow business users to plug and play their custom algorithms. Initially based in Singapore, Aureus Analytics have offices in Mumbai and New York as well.
2. Crayon Data: Crayon Data is a big data and AI start-up, with an ambitious vision to ‘simplify the world’s choices’. Crayon’s flagship product Maya™ is the world’s only choice engine, delivering digital personal experiences centred around taste. Powered by Crayon’s patented TasteGraph™, ChoiceAI and Lifestyle Marketplace, Maya makes it possible for any enterprise to compete in the ‘lifestyle economy’, by personalizing each customer’s experience. The platform enables enterprises to link their behaviour data to Crayon’s Tastegraph™ to create massive Personalised Taste FingerPrints for millions of their customers. Started in 2012, they have offices in both Singapore and India.
3. InfoTrie: With their headquarters in Singapore, InfoTrie is a News Analytics, Financial Engineering and Big Data company. Since being founded in 2012, they have since expanded to India and Europe. By leveraging big data technologies, they are changing the way unstructured data is consumed in the field of Finance. FinSentS, their flagship solution is a cutting-edge Sentiment Analysis and News Analytics engine. Along with their other APIs, they help financial institutions, analysts, traders and investors evaluate past performances and assess current financial positions of assets, topics and companies.
4. Lynx Analytics: Lynx Analytics is a product company founded in 2010 by a group of professors and students from INSEAD. This Singapore based start-up aims to solve complex business problems with big data graphs. Their propriety platform, Lynx Enterprise, enables scalable processing of large datasets, by visualizing and analysing the Web for invisible relationships in business and public data. Primarily serving the telecommunications and financial services industries, they operate in the United States, European Union, Hong Kong, Indonesia, Philippines and Malaysia.
5. Nugit: Based out of Singapore, Nugit is offers a Data Story-telling platform, which supplies online marketers and agencies with insights to help achieve ROI improvements and cost savings. It does so by making important stories hidden in data accessible in real-time. The platform uses artificial intelligence to fill gaps between dashboards and stories and creates smart campaign reports in an email-friendly format. Started in 2013, the company uses its platforms to put a human perspective to the changing world of the digital era.
6. Plunify: Founded in 2009 with the purpose of optimizing chip design performance, Plunify developed a cloud platform that enables semiconductor chip designers to shorten product time-to-market and reduce development costs. Their flagship software product, InTime, provides FPGA timing closure and optimization solutions using unique Machine Learning technologies. Originally from Singapore, the software company has operations in Malaysia, China and Japan as well.
7. Sift Analytics Group: Headquartered in Singapore Sift Analytics Group is one of the initial providers of enterprise software solutions. Having started in 1999, the organization is a veteran in the field of Big Data analytics. They help enterprises obtain clear, immediate and actionable insights into current performances. As well as the ability to predict future outcomes for effective planning. They provide solutions across a range of areas, including Predictive Analytics, Business Intelligence, Enterprise Planning and Budgeting, Enterprise Marketing Management, and Enterprise Content Management.
8. Sparkline: A software company founded in 2013, Sparkline aims to helps enterprises develop a practical, strategic and saleable approach to Digital Analytics. Through a range of customized consulting, education and in-house technology solutions, they enable companies to focus on a data-centric framework. Their Propriety Framework engages businesses across the data spectrum and interprets large digital data sets to provide insights into the behaviours of their customers. With their headquarters in Singapore, Sparkline specializes in Digital Data interpretation and optimization to achieve superior ROI.
9. Taiger: Taiger is a Singapore based company which develops artificial intelligence semantic software for finance, insurance and government sectors. They specialize in knowledge worker automation solutions, cutting edge AI technology and large scale digital transformation. The company offers several platform solutions and tools such as iConverse, iSearch and iMatch, which substantially and quantifiably increase operational efficiencies while reducing risk. Having been founded in 2015, they now have operations spread across the globe, including Madrid, San Francisco and Hong Kong.
10. Tookitaki: With a vision to emerge as the leader in machine learning powered regulatory compliance, Tookitaki is a machine learning forecasting platform for the banking industry. They are building an intelligent decision support system (DSS), which helps enterprises and business owners go beyond the barriers of existing statistical packages. Their flagship products Alerts Management Suites (AMS) and Reconciliation Management Suite (RMS) focus on anti-money laundering and reconciliation, respectively. The company was founded in 2014 and have offices in Singapore and India.
11. Vi Dimensions: Vi Dimensions was founded in 2015, with a simple idea in mind. Applying video analytics efficiently, with the help of data analysis and machine learning. They aim to build smart surveillance on a larger scale, by tapping into the vast network of cameras throughout a city. The Singapore start-up has developed an abnormality detection (AD) technology, which can identify abnormalities in countless variations of public behaviour, right from unusual crowd build-up to public brawls and unattended toddlers. Using their ground-breaking algorithms and Machine Learning techniques, their propriety software can sift through large amounts if video feed streaming in real-time and derive actionable in-sights accordingly.
12. ViSenze: Born out of a lab in the National University of Singapore, the founders of ViSenze delivers intelligent image recognition solutions that shorten the path to action as consumers search and discover on the visual web. Built on deep learning and computer vision, they power visual commerce with artificial intelligence at scale for retailers, brands and publishers. Using breakthrough technology to turn any image and video into engagement opportunity, by simplifying search experiences and uplifting conversion rates. Having started in 2012, the company now has offices in the U.S., the U.K., India and China, besides Singapore.
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