The advent of advanced algorithms, Big Data, and the exponential increase in computing power has propelled AI‘s transition from theory to real-world apps.

However, AI has also unveiled a darker side, attracting cyber attackers to weaponize the technology and create havoc in ways unimaginable!

Deloitte states that 34.5% of organizations experienced targeted attacks on their accounting and financial data in 12 months. This shines a light on the importance of maintaining a risk register for tracking potential threats.

Another research further emphasizes this – a staggering 80% of cybersecurity decision-makers acknowledge the need for advanced cybersecurity defenses to combat offensive AI. Let us dive deep into the double-edged nature of the technology.

Top 4 AI-enabled phishing and cybersecurity threats to know

Cyber threats are on the rise, both in terms of complexity and volume. Here are four examples that are creating a buzz in today's security landscape for all the wrong reasons:

1. Deepfakes

This manipulative technique creates realistic-looking and highly convincing video, audio, and image content that impersonates individuals and organizations using AI algorithms.

Deepfakes can push fake news or negative propaganda to confuse or skew public opinion and imitate the victim's voice or appearance to gain unauthorized access to secure systems.

Using this technology, cyber attackers can instruct employees to perform actions that compromise the organization's security, such as sharing confidential data or transferring funds.

Remember when in 2019, the CEO of a UK-based energy firm got scammed into wiring 220,000 to a scammer's bank account because he thought he was speaking to his boss on the phone, who had the recognizable “subtle German accent?”

The voice, in fact, belonged to a fraudster who used AI voice technology to spoof the German chief executive. Deepfakes are known to make phishing attempts much more personable and believable!

2. Data poisoning

While data poisoning is typically associated with Machine Learning (ML), it can also be applied in the context of phishing.

It is a type of attack where misleading or incorrect information is intentionally inserted into a dataset to maneuver the dataset and minimize the accuracy of a model or system.

For example, most people know how prominent social media companies like Meta and Snap handle data. Yet, they willingly share personal info and photos on the platforms.

A data poisoning attack can be launched on these platforms by slowly corrupting data integrity within a system. Once the data gets tainted, it leads to several negative consequences, such as:

• Inaccurate predictions or assumptions
• Disruptions in day-to-day operations
• Manipulation of public opinion
• Biased decision-making

Ultimately, data poisoning is considered a catalyst for financial fraud, reputation damage, and identity threat.

3. Social engineering

It typically involves some form of psychological manipulation, fooling otherwise unsuspecting individuals into handing over confidential or sensitive information that may be used for fraudulent purposes.

Phishing is the most common type of social engineering attack. By leveraging ML algorithms, cyber attackers analyze volumes of data and craft convincing messages that bypass conventional cyber security measures.

These messages may appear to come from trusted sources, such as reputable organizations and banks. For example, you might have come across an SMS or email like:

• Congrats! You have a $500 Walmart gift card. Go to “http://bit.ly/45678” to claim it now.
• Your account has been temporarily locked. Please log in at “http://goo.gl/45678” to secure your account asap!
• Netflix is sending you a refund of $56.78. Please reply with your bank account and routing number to receive your money.

Cyber attackers want to evoke emotions like curiosity, urgency, or fear in such scenarios. They hope you would act impulsively without considering the risks, potentially leading to unauthorized access to critical data.

4. Malware-driven generative AI

The powerful capabilities of ChatGPT are now being used against enterprise systems, with the AI chatbot generating URLs, references, functions, and code libraries that do not exist.

Through this, cyber attackers can request a package to solve a specific coding problem only to receive multiple recommendations from the tool that may not even be published in legitimate repositories.

Replacing such non-existent packages with malicious ones could deceive future ChatGPT users into using faulty recommendations and downloading malware onto their systems.

How to protect your organization against AI phishing scams

As the sophistication levels of cyber attacks continue to evolve, it is essential to adopt several security measures to keep hackers at bay, including:

1. Implement the Multi-Factor Authentication (MFA) protocol

As the name suggests, MFA is a multi-step account login process that requires additional info input than just a password. For instance, users might be asked to enter the code sent on their mobile, scan a fingerprint, or answer a secret question along with the password.

MFA adds an extra layer of security and reduces the chances of unauthorized access if credentials get compromised in a phishing attack.

2. Deploy advanced threat detection systems

These systems use ML algorithms to analyze patterns, identify anomalies, and proactively notify users about potentially dangerous behaviors such as deepfakes or adversarial activities, thereby giving organizations a leg up over cybercriminals and other threat actors.

Many Security Operational Centers use Security Information and Event Management (SIEM) technology in tandem with AI and ML capabilities to enhance threat detection and notification.

The arrangement allows the IT teams to focus more on taking strategic actions than firefighting; it improves efficiency and cuts down the threat response time.

3. Establish Zero Trust architectures

Unlike traditional network security protocols focusing on keeping cyber attacks outside the network, Zero Trust has a different agenda. Instead, it follows strict ID verification guidelines for every user and device attempting to access organizational data.

It ensures that whenever a network gets compromised, it challenges all users and devices to prove that they are not the ones behind it. Zero Trust also limits access from inside a network.

For instance, if a cyber attacker has gained entry into a user's account, they cannot move within the network's apps. In a nutshell, embracing Zero Trust architectures and integrating them with a risk management register helps create a more secure environment.

4. Regularly update security software

This measure is commonly overlooked, and it is essential for maintaining a strong defense against AI-driven phishing and cyber security threats. Software updates include patches that address known anomalies and vulnerabilities, ensuring your systems are safe and secure.

5. Educate and train your employees

Training programs come in handy to raise awareness about the tactics employed by cyber attackers. You must, therefore, have the budget for teaching your employees different ways to identify various phishing attempts and best practices for responding to them.

Over to you

The role of AI in phishing indeed represents a frightening challenge in this day and age. Addressing such cybersecurity threats requires a multi-faceted approach, including user education, advanced detection systems, awareness programs, and responsible data usage practices.

Employing a systematic risk register project management approach can help you enhance your chances of safeguarding sensitive data and brand reputation. In addition, you should work closely with security vendors, industry groups, and government agencies to stay abreast of the latest threats and their remediation.

The post Digital Deception: Combating The New Wave Of AI-Enabled Phishing And Cyber Threats appeared first on Datafloq.

That's when the power of advanced technology and tools to conduct seamless online classes became more evident. Several industry leaders took the initiative to introduce artificial intelligence, cloud, and various other advanced technology-based tools to help educators. The investment in these e-learning tools witnessed remarkable growth and continues to flourish with no end in sight.

In fact, reports state that the e-learning market size, which was $200 billion in 2019, might cross $400 billion by 2026. What's more? Research affirms that learners and educators have optimistic opinions about e-learning.

This post is about the top benefits of using advanced technology and tools for e-learning. You'll also learn about state-of-the-art tools to enhance the online teaching-learning experience. So, let's get started!

Top Benefits of Advanced Technology and Tools for e-Learning

Here are the top five benefits of using advanced technology and tools for online teaching-learning.

1. Provides a Personalized Classroom Experience

Personalized teaching sessions involve understanding students' unique needs and learning patterns to impart in-depth knowledge via lectures. Its goal is to reduce friction and boost students' interest in a specific topic or subject. However, creating a personalized experience is challenging. The reason? Each learner has their preferences when it comes to learning. Besides, collecting and analyzing real-time data is labor-intensive.

Traditional e-learning platforms often come with basic collaboration functionalities. A lack of intelligent features can fail to deliver personalized experiences, negatively impacting the outcomes. Research affirms that the right technology and tools are vital for positive learning results.

What's the solution?

With artificial intelligence technology-powered e-learning tools, you can achieve your goals! For instance, these tools use natural language processing (NLP), sentiment analysis, and machine learning to discover in-depth insights into students' profiles. The educators can observe granular details like areas of expertise and learning patterns of each learner.

AI-based e-learning platforms leverage advanced analytics to help educators analyze crucial insights and track learners' progress. This approach can help tweak teaching plans, thus ensuring seamless e-learning.

2. Offers Access to the Library of Digital Resources

Advanced technology-based tools can lay a strong foundation for thoughtful and skill-based learning. They can help educators encourage the students to develop a “learner” attitude and skills like problem-solving, reasoning, critical thinking, and more. Here's how:

Educators can create and share a library of digital assets offering in-depth insights (beyond a particular topic or subject). These valuable resources can imbibe curiosity to learn more. In fact, research states that digital educational resources can revolutionize the teaching-learning process. Traditionally, educators used tools like Google Drive, iCloud, and more to manage and share resources. But segmenting and finding helpful resources from hundreds of digital assets was labor-intensive.

To tackle this issue, advanced cloud technology-based digital asset management (DAM) software can empower educators to overcome these content management challenges. For instance, DAM tools can help organize, save, access, and distribute digital content assets to learners, thus streamlining the e-learning process.

With a few clicks, educators can find the required content to be shared, including images, videos, documents, and more. There's no need to browse the plentitude of files, thus saving time and effort. The result? The students get tailored content to enhance their learning. Besides, you can leverage AR and VR-driven e-learning tools (augmented and virtual reality). They can create immersive experiences to explain complex concepts.

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So, provide the best resources to support your students all along.

3. Assists with Monitoring Student Attendance

Research reveals that students' attendance directly impacts their overall performance. No wonder all educators track students' attendance without fail. Most e-learning tools empower educators to accurately monitor each student's attendance with a few clicks in real time. However, AI-based attendance tracking tools can take the process a notch further.

For instance, with AI-powered attendance trackers, you can monitor and generate detailed reports on:

• The exact number of days when students miss out on classes
• Classes that learners skip and attend – daily/weekly/monthly/yearly
• Attendance patterns (how often they are absent)

In the case of high absenteeism, you can take corrective measures without hassles. Advanced AI tools offer predictive and prescriptive analytics to help educators take actionable steps. This strategic approach can encourage learners to attend classes, thus boosting their overall performance.

4. Helps Assign and Manage Projects

Besides conducting online teaching-learning sessions, educators have a few more crucial responsibilities, such as creating, assigning, and managing projects.

Here's the traditional way of doing it:

• Educators research and collect the latest information to prepare for an upcoming lecture.
• Based on the topic, they create a questionnaire in advance.
• After the lecture, they dictate the assignment to the students.

The entire process consumes valuable time, effort and is a big headache! Leveraging advanced cloud technology-based platforms inbuilt with project management capabilities can ease your journey. These platform offers features to help educators create, upload, assign, and manage projects with a few clicks. This saves their precious time and effort. For instance, with cloud-based project management tools, you can –

• Create project deadlines
• Track students' project progress
• Share project updates
• Provide instant feedback
• Build collaborative spaces (for project discussions)

This way, you can optimize your teaching process and boost classroom productivity for the best outcomes.

Summing Up

For educators embracing digital, deploying advanced technology-driven e-learning tools is crucial to foster a positive learning experience. The advanced technology-equipped tools shared in this post can be your reliable partner in the online teaching-learning journey. They can help educators engage and establish strong relationships with students. So, leverage them to streamline your online teaching workflow and enhance productivity.

The post How Advanced Technology and Tools Are Transforming e-Learning appeared first on Datafloq.

Questionnaires and in-person consumer interviews were formerly the mainstays for product managers in collecting end-user feedback. These tactics helped product managers learn about their customers' experiences with their products. But now, they use a product management platform that integrates with product analytics tools to scoop key data about customer interactions.

Product managers leverage data to understand customer trends and preferences, optimize product performance and identify new growth opportunities. Another factor is data can be used to optimize product designs and features to improve customer experience. Data analytics can help measure product launch campaigns' success and track customer feedback trends.

Product managers use data analysis to get answers to questions like –

• What happened to the product after its release?
• What is the current state of the product?
• Can this product be better?
• Do users like the product? If not, what are their expectations?

Such descriptive answers require collecting data from multiple sources (tools, customer feedback, customer support team, etc.) and then summarizing it into useful metrics that provide insight into the current performance of the product.

Overall, 89% of marketers already leverage data analytics to make strategic decisions, which shows why data analytics is important for product management.

For more, let's dive in!

Track key metrics

Product managers may not involve themselves in collecting and poring every possible information they find. Doing so may overwhelm them and other team members to make sense of the data. Therefore, they can pick up the ethics of analytics and start identifying the necessary information that feeds the product enhancement actions.

Focus on key performance indicators that help save time and effort by restricting the search to the most relevant results.

Here are some of the most important indicators for a product manager to consider-

Engagement: Knowing how much the customers use your product can help plan a roadmap to enhance their experience. Collect key user information like how they found your product, what made them sign up, which features they often used (and seldom), and what kept them coming back. Such data can help fine-tune products to provide a large audience with the best possible user experience.

Customer churn: Keep tabs on customer churn to discover why customers stop using the product. It compels product managers to reconsider product features, customer service concerns, or pricing. This way, product managers will better understand where to put their efforts to reduce customer attrition.

Cost of new customers: This powerful metric feeds the marketing and sales team to streamline their efforts. It helps product managers calculate the cost of acquiring a new customer. The rise and fall observed in this metric help adjust marketing and product pricing strategies.

Customer lifetime value (CLV): This metric can help move beyond simply gauging the money customers spend buying the product. Knowing this helps you know the behavior of your valuable customers, based on which you can motivate them to continue with such behavior. Product managers must fine-tune aspects like onboarding experience, improving average order value, and long-lasting building relationships that drive loyalty.

Make informed decisions

First, data analytics plays a pivotal role in product management that helps managers make informed decisions and provide the direction to meet customer needs effectively. It begins with gaining insights into customer preferences and behaviour, which further propels identifying new product trends and opportunities.

And this way, product managers can measure existing product success, and if needed, they can refine the same.

The decision-making of product managers through data analysis includes (but is not limited to) the following –

Track product sales and performance: Data analytics help track the sales and performance of a particular product. This helps product managers decide where to allocate resources and how to promote products best to maximize profitability.

Identify trends in customer buying habits: With data analytics, product managers can identify trends in customer buying habits and use this information to develop new strategies for marketing campaigns or create more targeted promotions for specific customers or segments.

Customer satisfaction: Data analytics also provides valuable insight into customer satisfaction, enabling product managers to identify and analyze potential issues before they start hindering customer experience.

Understand the competitive landscape: Data analytics helps product managers better understand the competitive landscape by providing an in-depth look at competitors' offerings, pricing strategies, and promotional activities to stay ahead of the competition while keeping costs low effectively.

Open inroads for A/B testing

A/B testing involves comparing two product versions (A and B) to determine which is more effective. This test involves identifying the most successful features in improving user engagement, satisfaction, and loyalty (some of which were discussed earlier).

For this, product managers must accurately understand how users interact with their products. And this is where data analytics comes in for A/B testing – leveraging metrics like user behaviour, usage patterns, and preferences.

Quickly identify changes that impact user engagement and other key metrics by implementing A/B tests for effective product management. Here, product managers can make small changes to a single product feature or element and measure its impact on user behaviour.

This approach simplifies identifying features that need improvement or product elements that no longer add value. Making such adjustments based on data-driven insights helps product managers ensure that they continuously improve their products by making the right decisions time and again.

Optimize pricing strategies

Pricing plays an integral role in determining the success or failure of a SaaS product. Therefore, product managers must implement an effective pricing strategy by leveraging data analytics tools that offer audience segmentation, cohort analysis, retention analysis, or other predictive modelling techniques.

Product managers can analyze customer segments or other factors like seasonality or competitors' pricing to optimize pricing strategies. This requires them to set prices that maximize profits while still being competitive in the marketplace.

Firstly, product managers collect necessary customer data (past purchases, successful upselling, etc.) and use advanced analytics and product management tools to analyze it. These tools allow businesses to uncover patterns in customer behavior and market trends that help make informed pricing decisions.

For example, product managers can use predictive analytics to forecast future demand for certain products or services based on past data. This helps adjust prices accordingly to capture the maximum profit while remaining competitive with competitors.

Wrapping up

A lot would depend on the data quality when using analytics for product management. It helps drive smarter decisions when developing new offerings, refining an existing product, or targeting a new market. Using data for making such crucial decisions can help optimize deliverables and ensure maximum profitability.

Product managers can leverage the power of data-driven insights to help teams gain visibility into user behavior patterns. This allows tailoring products to cater to customer needs and prove successful in target market segments. Also, product managers work to capitalize on new opportunities quickly before they become saturated with similar offerings, and this keeps the product relevant amidst the growing competition.

The post Importance of Data Analytics for Product Managers appeared first on Datafloq.

For instance, in 2015, Coca-Cola strengthened its customer retention strategy with the help of big data analysis through a loyalty program. Netflix is also known to use it for its personalized recommendations and targeted advertising efforts by studying the behavioral data it collects.

Today, we would be focusing on the how to' part of using Big Data. But before we move on to that, let's take a quick look at the why' as well.

Why Is Big Data Analytics Important?

Reaching an understanding of why we insist on analyzing a vast data mine is of crucial significance. Unless we realize why an act is being done, it cannot be carried out optimally and data analysis is not a place where you'd want to be any less than thorough.

Following are some of the reasons that make data analytics as important as it is.

• Realistic Goal Setting: Ambitious yet attainable goals can be set according to the market conditions and the company's own standing, backed by the data.

• Effective Marketing: The efforts put into marketing and promotional activities can be more target-oriented and personalized, thus being more effective than generic hit-and-miss.

• New Revenue Opportunities: Unexplored potential markets can be discovered and aimed for, thus uncovering new business growth opportunities.

How To Leverage Big Data Analytics?

For many companies, success with big data analytics is often limited. This is due to the lack of know-how about how to use data optimally for better business decisions and results.

But while every organization has its own ways of going about it, here are three key ways to start gaining some amazing results with big data analytics for your business.

Marketing and Targeted Advertising

Although analytics has now become the heart and soul of marketing activities, its entry into the industry can be traced back to the period where companies began realizing what ideas enticed the consumer towards their products and services.

Ever since then, data analysis has been used for several purposes, from A/B testing to experimenting with new marketing channels. Gaining a deeper understanding of the markets as well as the potential and existing consumers also helped focus the advertising efforts in a more targeted manner.

For instance, big data analytics can help provide insights on what particular customers are most interested in, and that information can be used to target them with more personalized email campaigns.

Analyzing customer behavior and buying patterns for enhanced personalization is now a favorite of marketers around the world. In eCommerce, search intent analysis also enables marketers to devise stronger eCommerce SEO strategies.

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HR and People Analytics

Admittedly, information on consumer behavior is of significant importance as we discussed above. However, data about the efficiency, wellness, and welfare of employees working in your organization is also vital to creating a productive and happy work environment.

When used in the human resources department, data analysis can help in a wide range of tasks sorting qualified resumes, keeping a track of the impact of training provided, and collecting performance data of the workers alongside their pain points to name a few. By figuring out and resolving your employees' problem areas, you not only help your staff out of a bottleneck but also enable them to focus on crucial tasks by freeing up their time.

Qlik and Visier are two tools that can assist you with this process.

Supply Chain

Out of the three main entities in any business transaction, we have covered the use of data analytics for two. Now comes the third one, your vendors and suppliers.

Supply chain management has always been a complex activity and even though the traditional ways have come a long way, they can still be improved significantly using big data analytics.

For example, with the predictive analytical models, a supplier can monitor their inventory along with determining the chances and timeline of potential delays in delivery and preemptive replenishment, if any. The supply routes can also be optimized which can prevent them from suffering major losses.

Modern supply chain systems based on big data allow for more complex B2B supplier networks. PepsiCo is one of the major market players that relies on data analytics for an efficient supply chain management process.

Closing Thoughts

As a business owner, what ultimately matters is the fact that your enterprise is on track for growth, and an assurance that it will continue to do so in the future.

Big data analytics is an important enabler for that, so it's time to start leveraging it for your business.

The post 3 Ways to Successfully Leverage Big Data Analytics for Your Business appeared first on Datafloq.

The analytics and forecasting applications alone have enabled companies to have a giant reputation by having market and customer data at their fingertips.

As the world recovers from the after-effects of the pandemic, AI is likely to assume an even bigger role in our lives, and certain key trends are expected to take off in a big way in 2021. Here is how AI applications can benefit your business this year:

1. Emphasis on advanced and ethical data processing

The amount of data currently stored on devices, and the cloud worldwide is immense, and it will only increase as more devices come online every day.

To take advantage of this data, businesses need to incorporate machine learning algorithms to mine this data and draw insights from it.

One obvious application of data is hyper-personalization, where tailored marketing messages can be sent to each customer based on what they have browsed for before, where they are based, what income level they belong to, and so on.

Another aspect of data processing in 2021 will be data collection that navigates the privacy and legal connotations of using a customer's identity and online activity for marketing purposes.

2. Rise in augmented and virtual reality applications

Thus far, augmented and virtual reality (AR/VR) have been regarded as something of a novelty, more for recreational applications (like gaming headsets or apps such as Pokemon Go) than anything else.

In 2021, however, more companies will be incorporating AR and/or VR into their marketing strategies. For instance, IKEA's Place application allows customers to preview what a piece of furniture will look like in their own homes.

Several fashion brands, such as Nykaa and MAC Cosmetics, also create this kind of immersive experience, allowing customers to try lipstick shades virtually and determine the best color for themselves.

This helps customers make better choices and makes the shopping experience more fun, both of which contribute to more sales. With COVID-19 and social distancing norms in place, more and more people will be looking for virtual shopping options, and AR/VR technologies make this possible.

3. Website optimization for voice-based searches

Over the last decade or so, the use of AI-powered voice assistants like Apple‘s Siri, Microsoft‘s Cortana, Amazon‘s Alexa, and the Google Assistant has gone up.

People often prefer to say what they would like done ‘from smartphone activities to managing home appliances ‘rather than type it out.

Major advances in machine learning that allow search engines to pick up natural language speech patterns have contributed to this popularity in voice search.

According to Statista, 8.4 billion voice-based assistants will be in use by 2024. Imagine how easy it would be for people living with physical disabilities to use voice-based devices and search for specific keywords and easily navigate websites.

Thus, one way for websites to adapt to the voice search trend is by ranking for keywords and phrases that people are likely to use while talking. Other strategies include optimizing local content and providing clear, immediate assistance in response to the search queries (rather than merely redirecting them to your website).

A significant component of this is conversational search, such as which restaurant near me is open now' rather than restaurants open now.' For content ideas on what natural language phrases people use in search queries, try this social listening tool called Answer The Public.

Once the site is optimized, test its accessibility quotient using an ADA compliance tester to ensure everyone can access and consume the web content.

4. Increased use of chatbots for customer support

Chatbots have taken off massively over the last decade. They have developed into sophisticated bots that can answer a wide variety of customer queries with ease in conversation language that makes them hard to distinguish from a human executive.

On the business side, chatbots significantly bring down operational costs (as companies no longer need to have large support teams) and bring invaluable data about their target audiences.

On the other hand, customers are also willing and welcome to use chatbots, as they can get instant query resolution 24×7 without having to wait in line for a support executive to be free. Ecommerce transactions via chatbot are projected to go up to $112 billion by 2023.

Businesses new to chatbots can easily set one up with various tools available online, many of which do not even require coding skills.

5. Real-time cyber threat detection and mitigation

The hyperconnectivity the world has today allows for countless new opportunities – however, it also makes data more vulnerable to cyber-attacks. AI's powerful predictive algorithms can play a significant role in ramping up a business' cybersecurity.

AI can detect cyber-weaknesses much faster and detect the pre-conditions that could lead to a weakness in the near future, thereby allowing for preventive measures to be implemented before any cyber-attacks.

AI will enable companies to respond much faster to any security breaches and thus minimize the damage. Finally, AI and the Internet of Things [IoT] can allow for easy monitoring of cyber-activity in real-time so that any deviations or malicious activities can be spotted and contained instantly.

6. Introduction of AI-powered chips in various industries

Applications and products that did not traditionally function on AI can do so now, thanks to AI-powered chips that will enable facial recognition, object detection, computer vision, and Natural Language Processing (NLP).

This has significant applications in industries such as healthcare, gaming, manufacturing, and finance. In gaming, AI-powered chips like Qualcomm‘s new Snapdragon 732G can create an unprecedented immersion level in the game.

In manufacturing, such chips can allow for automated product checks and even product assemblies to happen in a fraction of the time it took before (as BMW does for its Germany factory). By 2025, the market for AI-powered chips is projected to hit over $91 billion.

Wrapping it up

The applications of AI in business are manifold. Regardless of which industry you operate in, AI can boost your profitability, bring you more customers, and help you design better products and experiences.

Therefore, it is essential to stay in constant touch with which way the wind is blowing so that you can be the first to spot new opportunities for AI in your business. The sooner you embrace these new trends and invest in them, the sooner you can reap the rewards.

The post How You Can Start Using AI to Improve Your Business in 2021 appeared first on Datafloq.

In other words, AI as a technology has never been more accessible, with the vast majority of industries leveraging it to further their market dominance and innovation. Even small-scale businesses and startups are using AI in some or other way to further improve their products and customer experience.

Customer experience, simply put, constitutes every single experience positive and negative between your business and its customers. It directly impacts your odds of converting people into customers and keeping them loyal to your brand.

Now, your website is a crucial customer experience touchpoint, and AI can greatly contribute to enhancing your customer's on-site experience, as it:

Paves the way for seamless site navigation with semantic search

Intuitive navigation is a must-have for any modern business website and a search bar goes a long way in ensuring that. Many visitors have a specific intent in mind and would use your site's search bar to quickly navigate to that particular piece of content or product.

Obviously, a successful search experience will gratify those visitors and lead to a better customer experience, forming a positive impression on the minds of those visitors.

But traditional search aka lexical search does not go beyond literal matches of the query terms (or at most, its variants) and does not understand the meaning of the query. And visitors, being human, often make typos or may not know the exact words for what they want, which means they won't get relevant results.

With AI-powered semantic search, your search engine can determine the searcher's intent and contextual meaning behind the query, improving the relevancy of the results delivered and thus, improving the customer experience.

Enables quick and convenient self-service for your visitors

Your modern customer hates waiting and demands near-instant answers to their questions, be it a general product related query or post-purchase support related concern.

While having live chat on your website helps, your customer-facing team can't be online 24/7. That's where AI enabled chatbot comes in, when you have an AI-powered customer-service agent that simulates pleasant human conversations and provides immediate, personalized responses to all FAQs – 24/7.

As you are aware, chatbots are pretty mainstream these days, with 35% of consumers wanting to see more businesses incorporating chatbots on their websites.

A chatbot can also help guide visitors to different parts of your website, and instead of having visitors fill out a form and await a response from you over email, they can get real-time answers to commonly asked questions.

So, just as a knowledge management the centralized repository of information with FAQs, how-to articles, and product guides meant for instant self-help allows your audience to learn anything they want about your company and its products/services on their own terms, an AI chatbot enables quick self-service for your visitors, which is key to a great customer experience.

Helps provide next-level on-site personalization

Everyone loves personal attention from businesses and the modern customer (both B2b and B2C) actually expects businesses to personalize their offerings.

An Infosys study found that 31% of consumers wish their shopping experience was more personalized than it currently is. With machine learning, AI enables predictive personalization with which you can automatically deliver dynamic experiences (content and products) on your site that are most likely to appeal to each individual visitor.

Take Amazon, for example. Its AI-powered product recommendation engine utilizes data from past purchases, browsing history, and items that are related and regularly bought together to create a personalized list of products that customers are far more likely to buy.

It's so effective that it is supposedly responsible for 35% of the company's revenue.

On-site personalization applies to content as well. With AI, you can display different content to different visitors, tailored to their personal preferences and behaviors. This will enhance their experience on your website and improve engagement.

Closing thoughts

75% of organizations using AI have enhanced their customer satisfaction by more than 10%, according to Capgemini.

So, enable instant self-service from knowledge management software to provide tailored on-site recommendations, AI can clearly boost your website's customer experience and conversion rate to a whole new level.

The post Top 3 Ways AI Can Enhance the Customer Experience on Your Website appeared first on Datafloq.

Studies have shown that AI can boost business productivity by 40%. And it is anticipated that the global AI market will reach almost $60 billion by 2025.

But how do you start leveraging the power of AI? The right place to start is to dig into your business problem.

Do you face difficulty in understanding your customer‘s needs? Are you getting complaints about poor customer service? Is the workload too much to handle? Are your advertising campaigns going in vain?

All these issues can be solved by leveraging the power of AI in your business. Let's discuss some crucial points that will help you out while implementing AI.

1. Understand your customers better and improve their experience

Customer experience is crucial if you want your business to succeed. AI can help you to collect customer data, which you can use in understanding your customers better.

For instance, by using the data, you can personalize their shopping experience.

You can make product recommendations based on previous purchases by using their purchase history.

Here's how Sephora sends personalized emails to their customers for better customer satisfaction.

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North Face is another company that uses IBM Watson's cognitive computing technology. They ask their customers where they'll wear the clothing and what they will be doing. Making use of that information, they make personalized recommendations.

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And if you choose Shopify Plus as your platform to sell your products, you can turn complicated tasks into a streamlined experience that your customers will enjoy.

You will find easy to use templates, you can tag and segment customers based on their buying behavior, automate inventory management for low-stock products, track and reward customers based on lifetime spend and more.

2. Lighten employee workload

Automation can help you to work smarter and realize business objectives faster and more efficiently. With the help of AI, you can set up an intelligent digital assistant to help workers do data-oriented tasks.

You can prioritize emails based on urgency, balance workflows, and attach relevant content to emails. It will eliminate the issue of information overload.

Moreover, AI can help you to examine metadata tags and relationships automatically. Thus, you don't need to manually categorize the content.

And if there are adjustments to be made in your employees' practices or if you want to engage workers better and decrease high turnover, AI can constantly monitor employee morale by analyzing internal data, such as pulse scores, and employee reviews.

3. Improve marketing and advertising

Artificial intelligence can significantly enhance your advertising efforts. It can help you to control the purchase and sale of advertising in real-time, including ad exchange, third-party network, and advertising products on social platforms.

There are tools that can automatically improve advertising spend and targeting. You can process the amount you spend on ads and targeting data, get results, and learn what actions will increase profit.

For example, the Red Balloon, an e-commerce company, used AI to identify ways to improve spend and targeting that got them a 3,000% return on ad spend.

4. Provide employee training

Digital adoption platform companies that are powered by AI can make employee training easier and more productive. For instance, Whatfix is a digital adoption platform that offers LMS-integrated Microlearning.

It makes the content that you offer to your trainees more engaging and valuable by deeply personalizing it for them. It is designed for interactive learning, and the content can adjust to a user's needs.

You can support preferred learning styles with auto-generated articles, videos, PDFs, slideshows, and more, thus reducing the time spent on classroom training.

5. Enhance customer service with bots

Bots are being increasingly used by companies to provide customer service, and it is bringing positive results.

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AI-powered bots and live chat tools can handle numerous tasks, whether it is troubleshooting or interacting with potential customers in real-time, eliminating the need for human employees.

It can provide 24/7 customer service, which becomes difficult and tiresome when handled by humans.

Usually, customer support teams spend plenty of time researching answers. And customers hate to wait. With the help of AI, you can store answers and identify commonly asked questions. Thus, it provides you with an option of frequent answers that can be inserted automatically based on the content in the ticket.

Not only is it helping the customers, but the workflow also gets reduced. You will be saving a lot of time and money spent on maintaining human customer service teams.

It also helps you boost sales like nothing else!

Of course, the ultimate end of every business is to boost sales. By automating your sales processes and equipping your salespeople to follow up on leads, you can significantly improve sales.

It is said that the adoption of AI by sales teams is anticipated to increase by 139% in the coming three years. And as per Salesforce, automating activities such as email campaigns and sales follow-ups have resulted in a 14.5% increase in sales productivity.

So if you are not automating your sales processes, you are missing out on a lot. Plus, the type of customer experience you give will impact your sales.

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And US companies are losing over $62 billion yearly because of bad customer service.

By using AI, you can provide personalized service, offer 24/7 support, conduct follow-ups, and thus improve customer service.

Wrapping-Up

AI can be rightly said as the future of business. Companies that don't implement it are lagging behind. In this modern era where digitalization is evolving at a rapid scale and revolutionizing business, you can give a competitive edge only by adopting the latest technology powered by AI.

Use AI to understand your customers and provide them with a superior experience, cut down your workload, enhance marketing and advertising efforts, provide employee training, use bots for offering 24/7 support to your customers, and boost sales.

What is not there to love about what AI offers? Sure, you will need to put in some investment, but the ROI you get and the efficiency and accuracy it will bring to work is worth the money.

The post How to Start Leveraging the Power of AI in Your Small Business appeared first on Datafloq.

The threat is real, and the danger is imminent.

According to the famed Warren Buffet, Cyber-crime is the number one problem for mankind. And the quote is seeming prophetic, after 1.76 billion records were compromised worldwide, in January 2019 alone.

This clearly indicates that if you don't ensure cybersecurity, it is going to cost you a great deal.

In fact, simply Ransomware compromise is slated to cost companies all over the world around $11.5 billion by 2019. That's a very deep hole to be burnt into one's pocket.

What is data-centric security?

Nearly all of the traditional security technologies currently in existence focus on the physical location of data, like a PC or a server. But there is a flaw. As soon as the data is moved somewhere else, you need another solution. And till that time, the data is left simply unprotected.

Data-centric security, however, is a different ball game altogether. It focuses on the exact data to be protected, and the files containing sensitive information.

The security is aimed at the data itself, not where the location. Hence, it is a much safer bet for highly sensitive data, and that is why it is becoming the more preferred type of cyber-security for businesses.

How to ensure data-centric cyber-security?

Every business organization is different. They have different needs, different goals, and different methods of working. Every business, however, needs to come to a consensus on the unique solution that will address their business needs and security concerns.

Here are four ways for every organization to ensure data centricity in their cyber-security:

1. Central Management

In the words of the famous architect Peter Eisenman, The more centralized the power, the fewer compromises need to be made in architecture.'

Quite like architecture, businesses are discovering the overwhelming advantages of having central management controlling all business data. For one, it ensures the data obeys the company security policy and protocol.

This helps in:

• Giving the business complete and unchallenged control over any sensitive data or file from the time it is generated
• Revoking or granting access to sensitive data almost immediately
• Every activity is closely monitored
• Adequate allowance for proper auditing and accounting

Once the central management takes control of the data, the data cannot be accessed or tampered with from any decentralized or unauthorized source. Which, in turn, ensures data-centric security.

2. Plugging the gaps

Traditional data protection methods like device-centric protection or network-centric protections leave a lot to be desired. The data security is not fool-proof and has certain fractures, that can be exploited to significant effect by seasoned cybercriminals.

A primary reason for the incapabilities of traditional methods is that once you need to transfer data between two devices or networks, you must first decrypt it. In layman terms, it means you must strip the data of its security protocols, and keep it exposed, even if for a few seconds, in order to transfer it.

And this momentary lapse of security is what the cyber criminals seek, and take advantage of.

To Ensure data-centric security, you must perform:

• 100% Data encryption, outside the company network
• 100% Data encryption, in company server and platform

Both of these are missing in conventional methods. By plugging these gaps in the traditional methods, you can put in place a data-centric security protocol in place.

3. Encryption management and automation

Company data is always accessed and used by employees. That is why, once you encrypt your data, you have to look for ways your employees can use them without compromising security.

The employees must be able to use the files they need and access the requisite databases to ensure the smooth functioning of the company, without sabotaging security. This is where automation is stepping in.

Automation helps in:

• Eliminating employee error
• Reducing the number of steps for an employee
• Ensuring compliance with protocols
• Guarding against data breach

Encrypting data and automating steps in your data protection protocol is a move in the right direction for data-centric security.

4. Flexibility

Albert Einstein once quoted, The measure of intelligence is the ability to change.'

Nearly a hundred years later, data-centric security protocols must also be adaptable to change, if they are to succeed.

Within a business organisation, there may be millions of data types and thousands of data policies. Every data type may require very different treatment and operations.

So, a data-centric security protocol must be amenable to change and must be able to accommodate the changing nature of data and its uses within the organization.

Organizations should be able to do permutations and combinations with their data and data types at will, as per the business requirements, without compromising security.

Thus, a foremost requirement of ensuring data-centric security is creating a flexible and adaptable cyber-security plan.

5. Employee training

Properly training employees is the final step towards ensuring your data-centric cybersecurity protocols actually work real-time. Your employees must understand and appreciate the reasons why those protocols are there in the first place.

A poorly trained employee who does not understand the long-term goal of a data-centric approach may find it circuitous and laborious. They may try to sidestep and bypass the security measures, leading to the compromise of sensitive data, or unauthorized grant of data access.

Proper training is required so that the employees know exactly what they should and should not do with respect to sensitive data. If possible, help them to acquire cybersecurity certifications to bolster.

The Future Ahead

As the world is moving online, cybercrime is showing no signs of letting up any time soon. In a recent study by the University of Maryland, hackers worldwide are now attacking computers at the rate of 1 attack every 39 seconds.

Cybercrime is evolving and it is getting substantially more expensive to tackle its after-effects. The average cost of dealing with the effects of cybercrime has risen to $13 million per company in 2019, from $11.6 million in 2018.

It is time to make the unanimous shift to data-centric security from the existing traditional methods. Cybercriminals and online hackers are getting smarter – it's time we followed suit.

The post 5 Ways to Ensure Data-Centric Cybersecurity appeared first on Datafloq.

No wonder, the global technology spend on IoT is expected to reach USD 1.2 trillion by 2022. The growth of this segment will be driven by firms deploying IIoT solutions and giant tech organizations who are developing these innovative solutions.

To help you stay ahead of the curve, we have enlisted a few trends that will dominate the industrial IoT sphere.

1. Cobots Are Gaining Popularity

Digitization is having a major impact in the industrial robotics segment as connected cobots or collaborative robots, making their place in the smart manufacturing ecosystem. This trend is improving the efficiency of operations and the reliability of the production cycle.

IIoT is making robots mobile and collaborative, offering technologies, such as self-driving vehicles (mobile collaborative robots), machine vision (part identification), and additive manufacturing that can boost production efficiency and business growth with an excellent ROI. No wonder, the global cobots market size had crosses USD 649 in 2018 and is expected to expand at a CAGR of 44.5 percent between 2019 and 2025.

2. Digital Twins Are on the Rise

A growing number of firms are deploying IoT solutions to develop a digital replica of their business assets. Thus, instead of sending data to each physical receiver separately, all the information is sent to the digital twin, enabling business units to access the data with ease.

Digital twins are growing in popularity as they decrease the complexity of the IoT ecosystem while boosting its efficiency. Gartner shares that 24 percent of enterprises are already using digital twins and an additional 42 percent plan to ride on this wave in the coming three years.

Smart businesses are already using digital twin software to incorporate process data, enabling them to reach accurate insights and address operational inefficiencies.

3. Augmented Reality Is Disrupting the Manufacturing Domain

AR is benefiting the manufacturing domain in more ways than one. The technology has disrupted the manufacturing areas like product design and development, maintenance and field service, quality assurance, logistics, and hands-on training of new employees.

For instance, in the assembling operations, AR is replacing the traditional paper instruction manual with IoT-enabled systems that have voice-controlled instructions along with a video from the previous assembly operation.  

AR is also allowing manufacturing technicians to have access to instant intelligence and problem insights related to maintenance, thereby improving their efficiency and reducing equipment downtime.

4. IoT-Enabled Predictive Maintenance Is Becoming a Part of the Overall Maintenance Workflow

With the advent of Industry 4.0, several enterprises are investing in IoT-enabled predictive maintenance of their assets to fix automated systems before they get disabled. In today's competitive business environment, it is extremely important for firms to keep machines running seamlessly. Connected sensors and machine learning are helping companies anticipate component failures in advance, thereby reducing equipment downtime and time to locking up machines for preventative maintenance checks.

As a result, many organizations are running predictive analytics and machine learning to monitor systems and gather data, allowing them to estimate when components are likely to fail.

5. 5G Will Drive Real-Time IIoT Applications

5G deployments are digitizing the industrial domain and changing the way enterprises manage their business operations. Industries, namely transportation, manufacturing, healthcare, energy and utilities, agriculture, retail, media, and financial services will benefit from the low latency and high data transfer speed of 5G mobile networks.

For instance, in the manufacturing domain, 5G will power factory automation, ensuring that the processes happen within the time frame, thereby reducing the risk of downtime. Further, 5G will help manufacturers in real-time production inspection and assembly line maintenance.

6. Firms Are Shifting from Centralized Cloud to Edge Computing

Until now, the centralized cloud was a popular choice among firms for controlling connected devices and data. However, with IoT devices and sensors expected to generate an ocean of data,  more and more enterprises want IoT to monitor and report data and events remotely.

Though most firms are using centralized cloud-based solutions to collect data, they are facing issues, such as high network load, poor response time, and security risks. Edge computing is helping businesses collect, analyze, and store data close to its source, thereby reducing the costs and security risks and improving system efficiency. That explains the growing demand for edge computing!

A research report from Business Insider Intelligence forecasts that by 2020, there will be over  5,635 million smart sensors and other IoT devices globally, generating over 507.5 zettabytes of data. The need to collect and process this data at local collection points is what's triggering the shift from centralized cloud to edge computing.

7. Firms Will Continue to Invest in Cybersecurity

Cybersecurity threats continue to evolve each day. Connected systems pose a serious threat to data and cause massive system disruption and loss to the firm. A 2018 Data Breach study by IBM revealed that the cost of an average data breach to companies globally is USD 3.86 million.

As a result, an increasing number of firms are investing in innovative services like virtual private network or VPN to access the internet safely. Such innovative security solutions are becoming increasingly popular with enterprises across domains.

8. IoT Analytics Is Gaining Significance

While sectors such as manufacturing, aerospace, and energy and utilities are deploying IoT-powered sensors and wireless technologies, the true value of industrial IoT lies in analytics. The connected systems generate a large amount of data that needs to be effectively employed to optimize operations. Thus, the demand for  IoT analytics will rise in the coming years. As a result, firms will have to depend on AI and ML technologies to find and effective ways to manage the data overload.

Companies like SAS, SAP, and Teradata are already offering advanced analytics software to help enterprises evaluate real-time data streaming from connected systems on the shop floor.

Going Forward

IIoT is all set to fuel the fourth industrial revolution. Firms across various industries are adopting innovative IoT devices and technologies to accelerate business growth. These IIoT deployments will help enterprises improve operational efficiency, reduce downtime, and get a serious competitive advantage in their respective domains.

The IIoT trends shared in this post will set the stage for innovative enterprise platforms and tech advancements. Organizations wanting to remain competitive should be not only aware of these trends but also take adequate measures to embrace them.

The post 8 Industrial IoT Trends of 2019 That Cannot Be Ignored appeared first on Datafloq.

From delivering highly engaging lectures that are better understood by students, to performing more intuitive aptitude assessments to propel students into the right courses for higher education, AI and big data are helping change the very course of formal education and bringing it closer to the goal it was originally intended for to inform young minds and enable them to make the best use of that information.

So let's take a look at all the ways AI and big data solve the biggest problems students and institutions face.

1. Improved Effectiveness of Learning through Personalized Program

One of the major players in a conventional classroom setting is student diversity. Some students are naturally good at grasping mathematical concepts while others struggle with it. Just one monotonous teaching curriculum isn't enough. Effective data analysis of individual test scores over the sessions can help teachers understand the underlying strengths and inherent challenges of each student.

Educational institutions are constantly generating critical data such as student surveys, credits analysis, data from participation in various school activities and a host of other resources. Tests that are taken online are a goldmine of data, presenting valuable insights into how long students take to answer a question, which ones they like to attempt first and many others.

Careful analysis of all this data can help educators develop personalized teaching plans that play up the strengths and mitigate the weaknesses of each student, sizably improving the effectiveness of teaching. Further, AI can help develop such personalized teaching programs adjusted to individual student needs. Already, companies like Carnegie Learning' and Content Technologies' are working on creating intelligent instruction design and creating a firm ground for such technologies.

2. Reduce Dropouts

More than 48% of first-time college students in America dropped out of college in 2016. This means that a staggering 2 million students starting college each year will drop out before earning a diploma. School and college dropouts are a huge problem in America and the rest of the world. The problem is multi-faceted and needs immediate solutions. Fortunately, AI and machine learning solutions have a substantial ability to tackle many of the reasons that lead to dropouts.

One of the big reasons so many students drop out of courses they worked so hard to get into is that they were just not prepared for the academic pressures they face in college. Another reason is courses unfit for their aptitude, something we will discuss in the next point.

Big data analysis done to determine the level of preparedness of a student before enrolling in a college and investing their time, money and effort into it can profoundly improve student outcomes. It can even help students prepare for the upcoming curriculum.

3. Performing Better Skills Assessment

Often, students find that a course they thought was their ultimate goal isn't really a good fit for them. Preliminary ambitions are driven by an outside view of their chosen field often become a burden when young students are faced with the reality of the subject. Often, good grades in math and physics make students and teachers think of engineering as a good option. However, when faced with the nuances of an engineering degree, students don't quite find it to be what they expected.

Once again, data analysis and AI can help these students perform a better assessment of their interests and aptitude. Data analysis of their grades, performance and participation throughout their school life and not just the final entrance exams can better indicate their inclination and determine courses more suitable for them. This in turn immensely helps students pick the right course.

4. Better Grading

Relying on monotone standardized tests is no longer the right way to assess a student's capabilities. That is something educators, parents and authorities are beginning to truly understand now. As a result, developing a better, more efficient system is imperative, one that can objectively measure a student's performance. A student's participation in class, analytical thinking, curious questioning, consistency in class attendance and many other aspects can be made legitimate performance criteria to measure their performance and grade them accordingly.

5. Improve Student Performance

In the same vein as above, all the data collected to these ends can also help find out which areas a student is struggling with, in subjects that they otherwise show an aptitude for. For instance, if a student is showing great critical thinking and analytical skills in class, but still scoring poorly in tests, maybe they need help with writing their papers or expressing their thoughts on writing. Working on those particular aspects then help improve the overall performance of the student and prevent them from falling into the discouragement trap of low grades on paper.

Wrapping Up

Education is one of the most sensitive matters of society that is struggling with enough problems of its own. Yet, imparting quality education and giving every student a level playing field is of the utmost importance if we are to obliterate the biggest of our challenges. The above points are a good indicator of how we can begin leveraging big data and AI to improve the lives of students and deliver better education to our future generations.

The post 5 Ways AI and Big Data Are Revolutionizing Education appeared first on Datafloq.