The world’s most successful companies set their focus on customer satisfaction. The reason being that customers leave organizations where they are not satisfied with the service. New products with unique and improved features will continue to pop up in the market. Still, the customer would rather continue doing business with companies that serviced them well over time. This is why companies have to pay apt attention to customer loyalty and advocacy. Data Science in Improving Customer Satisfaction The advent of new technologies and the utilization of data science methods on huge amounts of data makes it easier for companies to place laser focus on the factors that cement customer loyalty for their products. Companies across the world now invest time and money in data science, analytics, and statistical testing. Data scientists help businesses navigate their way through the vast ocean of data available to them in a bid to make the right, timely business decisions. How B2C & B2B Companies Use Data Differently Data analytics is a source of valuable insights that can inform how both B2c and B2B companies make decisions about products, marketing, and sales. Though they each have a unique set of challenges, B2c and B2B businesses both collect, visualize, and analyze their most valuable asset – customer data. Both B2B and B2C companies use data analytics to unlock new pathways to increase customers, more profits, and better decision-making. But they access these pathways in totally different ways. So let’s go over the differences between how B2B and B2C companies use data. Sales Data B2C businesses often have shorter sales cycles, with a large part of their revenue coming from advertisements. This implies that the customers need to be engaged for longer and the sales cycle optimized. Leveraging data on the customer’s experience in making a purchase can help point decision-makers in the right direction. B2B companies, on the other hand, have much longer sales cycles. Here, the goal is to minimize the amount of time the customer spends making a purchase. Using data science, the company can improve efficiency and shorten the sales cycle. Data scientists can analyze sales data for insight into improvements in customer experience. Customer Data Since B2C companies typically have more customers than their B2B counterparts, there is usually no shortage of data to analyze. This allows data scientists to analyze several different customer data points related to their experience with the business. Data scientists can use customer data to segment customers accurately and outline better user personas to guide product and marketing initiatives.

There has been a gradual but wide-scale shift in the business world, compounded by the movement restrictions and other impacts of the COVID-19 pandemic, to a remote work model. These changes have led to the need for effective next-gen network security for businesses. As the nature of work changes, there must be a corresponding reevaluation and subsequent transformation of how organizations approach network security. Ultimate Guide to Effective Next-Gen Network Security for Organizations Moving work to the cloud via work-from-home policies eliminates the physical boundaries of cybersecurity. Moreover, this extension of the traditional bounds of network security establishes a strong basis for the greater adoption of edge security practices. Apparently, end-to-end security now seems more like edge-to-cloud security. This transformation is not just about technologies and tools, although those are critical in adapting networks and cloud environments to the new model. Rather, this transformation is, foremost, a change of outlook. Data and Network Security With the increase in the number of technological tools at work, including and especially IoT devices, more data is being collected. And the more data is collected, the more effort must be exerted in protecting the information from intruders. This supports the earlier submission that the new normal in network security is not just around transformation but more an extension. An extension of security capabilities to accommodate the revolution of attack approaches. Basically, when it comes to network security, like all other organizational processes, business leaders must think in scale. After all, cyber attackers are not backing down; instead, they devise newer and newer means of network intrusion and system destabilization. Change and Adaptation It is understandable why some leaders may first prefer to dip their toes into the water; the world has not witnessed this scale of a comprehensive upset in a long time. Yet, the greater mistake, and one that supports that form of approach, unfortunately, is that many people believe that the time and the current scale of challenges we now face would eventually pass.

Artificial intelligence is gaining more and more attention. Intelligent self-learning programs disrupt many industries, including eCommerce, manufacturing and production lines, transportation, agriculture, logistics and supply chain, and more. Moreover, such programs automate redundant processes and don’t require a high level of creativity, increasing its overall effectiveness. “It is difficult to think of an industry that AI will not transform. This includes healthcare, education, transportation, retail, communications, and agriculture. There are surprisingly clear paths for AI to make a big difference in all of these industries.” –  Andrew Ng, Founder, and CEO of Landing AI Innovative Application of AI in Recruitment These disruptive forces have started hitting the HR industry as well. And it’s not just a trend; the innovations brought by AI are going to stay. Moreover, it’s anything but a temporary phenomenon. The most recent development in HR technology is AI in recruitment. The changes brought by AI in recruitment will be significant since many recruitment aspects have redundant, time-consuming tasks that can be easily automated. Apart from that, AI can bring innovative solutions to the new-age emerging problems faced in HR and recruitment, like managing a multi-generational workforce, rising mental health issues, promotion, or inclusive culture. The entire HR industry will be going under major changes while AI makes their jobs easier, faster, and better. This article will explore the role of AI in recruitment, its possible use cases, top tools available in the market to automate recruitment processes, potential challenges attached with the adoption of AI, and its overall impact. The Role of AI in Recruitment — nine helps for recruitment Intelligent Screening According to 52% of talent acquisition leaders, the most challenging part of recruitment is screening and short-listing candidates from a large talent pool. When integrated with applicant tracking software (ATS), an AI screening software can make hiring recommendations by utilizing data like candidates’ performance, merits, experience, etc. The AI screening software can learn from existing candidates’ experience and skillsets and make recommendations accordingly.

Manual operations in manufacturing often lead to increased costs and decreased growth. Manufacturers have to resolve 4 critical challenges: operations optimization, cost savings, production quality improvement, and demand forecasting. Digitizing one or two processes can only work to an extent and only a complete digital solution could come in handy. Especially, critical challenges like demand forecasting require a robust prediction system based on operation data analysis and without this manufacturers can never plan for the future. Predictive Analytics in Manufacturing – Why it Matters and How it Works So, what would be the best possible way to address these challenges? An interesting yet best way to overcome this challenge is by automating the process with predictive maintenance solutions. Let’s get started with the applications of predictive maintenance in manufacturing across improving operations and production quality at reduced cost and forecasting demand for the future in detail in the sections below. What is predictive maintenance? “Predictive maintenance (PdM) is maintenance that monitors the performance and condition of equipment during normal operation to reduce the likelihood of failures. Also known as condition-based maintenance, predictive maintenance has been utilized in the industrial world since the 1990s. The goal of predictive maintenance is the ability to first predict when equipment failure could occur (based on certain factors), followed by preventing the failure through regularly scheduled and corrective maintenance.” (Source: Reliable Plant) Manufacturing Predictive Analytics Market Outlook 2018 to 2026 “The manufacturing predictive analytics market size was valued at $535.0 million in 2018 and is projected to reach $2.5 billion by 2026, growing at a CAGR of 21.7% from 2019 to 2026. The advent of Industry 4.0 boosts substantive recent innovations in manufacturing.” (Source: Allied Market Research) How the entire predictive maintenance system works A predictive maintenance system comprises the Internet of Things (to collect data from any surface); Cloud (to process the data); Mobile applications (to push notifications based on data); AI/ML (to analyze and predict insights using data); web application (to share entire operations data under one roof).

The realization of the necessity of an interconnected system that leverages the Internet to make things easier revolutionizes the way we live. It led to the creation of IoT, and although the term is 16 years old – the concept dates back to the 70s. Previously known as “Embedded Internet,” the term IoT was coined in 1999 by Kevin Ashton. A Comprehensive Overview of IoT, Big Data, Cloud Computing Today, IoT is the primary source of big data collection. The analysis and processing of this gathered data have given numerous modern analytic solutions. IoT is also the main reason for innovation in the modern world, with more robust information. It has given rise to new business opportunities. Today, IoT technology has given a new meaning to the world “Smart.” By forming a relationship with other technologies like cloud computing and contributing to them in terms of information, the IoT technology helps new businesses and old ones in terms of growth. Cloud computing advances processes and data analytics For example, cloud computing has been advancing processes and data analytics in an economical and scalable manner. Such empowering capabilities are being provided to brands by the generation of Big Data through IoT. Moreover, IoT is also responsible for generating analytics solutions that, without Cloud, were costly and complicated due to a complex infrastructure or architecture and storage and processing requirements. Since the IoT was a data-driven technology, it contributed significantly to Big Data, influencing various velocity, accuracy, and reliability domains. In a nutshell, the IoT is the future of businesses and lifestyles. With the integration of Big Data and Cloud, more innovative digital solutions that consist of better, more advanced analytics and data-oriented decision-making characteristics are becoming increasingly possible. Since IoT is a vast and continuously evolving field, it is getting difficult to understand the technology and its benefits. So, in this article, we’re going to explore a comprehensive overview of What is IoT? We will explain the relationship holistically between IoT and Big Data and IoT and Cloud. We will also cover some of the advantages of using IoT integrated Big Data and Cloud computing.

The digital world has been entirely transformed with the help of technological breakthroughs, and IoT (Internet of Things) is to be credited among AI (Artificial Intelligence), ML (Machine Learning), Data Science, and more. Internet of Things has been the futuristic concept of connecting and controlling our devices and items remotely. This future idea alone has brought drastic change within many industries that have seen improved processes, increased productivity, and many other benefits. IoT for the Disabled – Breaking Barriers and Changing Lives However, one of the most significant contributions that IoT has made in assisting users with disabilities. How IoT for the disabled? We’ll get to that thought in this article. For now, let’s shed light on the concept of IoT for our readers that would like to understand the technology first. What is IoT? The Internet of Things can be explained as a network of physical objects with sensors, software, and various other technologies embedded within them to connect and exchange data with other devices, systems, or mobile apps over the internet. What could be those devices? They could range from regular household items, medical equipment, or industrial tools. Today, we have nearly 7 billion connected devices, with experts expecting the numbers to spike up to 10billion by 2020 and 22billion by 2025. How Is IoT Important? IoT is the upcoming technology of the 21st century that has allowed human connection with everyday objects like kitchen appliances, cars, house lights, and much more, enabling data sharing without any human intervention. Basically, an ordinary item can transmit data and automate tasks without any manual control, giving new waves of opportunities to contactless during the COVID times and otherwise. Not to mention, with low-cost computing, big data analytics, and mobile technologies, the Internet of Things is relatively cheap and easily accessible to the masses. Of course, IoT comes with its own challenges and trends that should be kept a close eye on, not to mention the many security challenges that IoT faces. Speaking about trends of IoT, ‘AIoT’ is also a reality that includes AI playing a big role in changing three industries in particular.