Big Data Made Simple - One source. Many perspectives.

The shift towards artificial intelligence (AI) and machine learning is all around. A surprising 80% of enterprises already invest in some form of AI today. Business communication tools are no exception. Both well-known and fresh chat apps like Facebook Messenger or Chanty start to actively come up with AI-powered features. Along with this trend, chatbots became the hot topic of last few years.

In a nutshell, chatbots are any bots that live in chat platforms. Unlike humans, these conversational agents are available for work 24/7. This feature turns chatbots into ideal tools for delivering information services. No wonder people started to actively harness them in different industry fields (like banking or publishing) and on e-commerce websites.

The invasion of chatbots in the messaging app industry has also unlocked a new gate for the way people collaborate at work. Timely team messengers became not only a place to exchange information, but also powerful assistants. How exactly can any team take advantage of using a team chat app with chatbots? Below are the main points to consider.

1. Easy task and project management

Ever-evolving capabilities of chatbots help team messengers ease up the entire project management process. Employees no longer need to leave their team chat app to make reports, as well as track time and expense. For example, Busybot lets users create and assign tasks right in Slack. Talla, in turn, keeps everybody focused on the critical tasks with alerts and reminders.

2. Automated routine processes

Frankly speaking, we all hate to do the same work all the time. Nevertheless, office workers spend about 552 hours a year doing work they did before. The possibility to cut down daily tasks off our shoulders sounds tempting, isn’t it? Chatbots are here to fight the working routine. With their help, employees can concentrate only on significant tasks, saving time and improving work efficiency.

3. Single information center

Employees’ productivity majorly depends on the one aspect: speed. Modern working process suffers as we lose time juggling between different online tools about 300 times per day. Chatbots integrated with a team messenger and other apps fix this problem. They pull information from all third-party tools and gather insights in your team chat application.

4. Smarter customer service

According to Gartner, chatbots will power 85% of all customer service interactions by the year 2020. And, why not? Chatbots like Twyla or Clare.AI improve any existing helpdesk or live chat support, resolving customers’ issues at a blink of an eye.

5. Saving costs

CNBC states: “Chatbots currently account for business cost savings of $20 million globally”. Of course, these virtual agents work round the clock and don’t ask for sick leaves, vacations and days off. With their ability to multitask, chatbots interact with several people at once and slowly replace real employees. Besides, a huge amount of platforms for building chatbots will let you create an assistant for any business needs.

To sum up, we have currently touched five main benefits of integrating chatbots into team communication tools. Virtual agents help messengers serve as a single information center and become awesome co-pilots in managing projects or dealing with repetitive tasks. Moreover, their 24/7 accessibility contributes to instant customer support and saving money by answering more questions in less time without the help of humans involved.

Once you choose the right chatbots for your team messenger, the company will not only improve internal communication, but also start working more efficiently.

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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.

SOURCE

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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 9^th, 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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