Category: What’s New in Technology

Customer loyalty is critical to any successful business strategy in today’s digital age. With emerging technologies such as the internet of things (IoT), companies are now leveraging a new approach called the internet of behavior (IoB) to gain deeper insights into their customers’ behavior and preferences.

What is IoB?

The internet of behavior exists because of the internet of things. IoT is the interconnection of physical digital objects that gather and exchange information over the internet. On the other hand, IoB makes sense of the collected data from various sources, including wearable devices, digital household devices, human online activity and social media.

The acronym internet of behavior (IoB) was coined by Gartner, a tech research firm, as identified among the top 10 trends in their strategic technology report for 2021. However, the concept of using data to influence customer behavior was developed in 2012 by Göte Nyman, a psychology professor at the University of Helsinki, long before the internet of things took hold.

Gartner defines IoB as an extension of the internet of things, focusing on capturing, processing and analyzing the “digital dust” of people’s daily lives.

Simply put, IoB interconnects IoT, consumer psychology and data analytics. The data is analyzed in terms of behavioral psychology to capture patterns that marketing and sales teams can use to influence customer behavior.

How IoB can Influence Customer Loyalty

Aside from products and services, customer experience has become a significant factor in business success. By understanding customer behavior, businesses can leverage IoB data to influence customer loyalty in various ways.

Personalization

Personalization has the power to transform customer experience. This is reflected in a survey that revealed 76 percent of Americans are more likely to complete a purchase because of a personalized experience.

To take advantage of IoB, companies study insights extracted from collected data and use it to decipher customer behavior; that is, their practices, preferences, habits, needs, wants and more. The company can then leverage this data to offer personalized product recommendations, such as insurance premiums, saving plans, travel destinations, etc.

For example, an insurance company can have users install apps on their phones that collect data on distance traveled, car speed, etc., and optimize their car’s premium based on driving behavior.

Timely Improvement of Products and Customer Services

IoB also makes studying how customers interact with specific services or products easy. This saves companies from time-consuming surveys that are used to determine consumer preferences. The collected data is analyzed to identify pain points and issues of concern. The company can then address the issues before they become significant problems, such as by improving on products and services. This is an excellent way to build trust and confidence in a brand, leading to customer retention.

Behavioral Retargeting

Since companies can access customer preferences, recent activities, likes, dislikes, and location data, they can send real-time notifications to customers about discounts and new offers in stores nearby. They also can track loyal customers and offer them rewards. This kind of retargeting will make customers feel like a business values them and caters to their interests.

Develop a Tailored Marketing Strategy

Insights from IoB data can help tailor marketing strategies to individual customers. For instance, a retail store can offer products or services based on the mood, age or gender of a customer; thereby providing a satisfying experience that will lead to a stronger emotional connection with the brand.

Key Challenges that must be Addressed for the Success of IoB

Despite the opportunities IoB offers, companies must be aware of some key challenges to fully realize its benefits.

• Privacy Concerns – Although personalization will make consumer lives easier, there is a concern about privacy. Companies must implement strong cybersecurity policies and measures to ensure that customer information is used only for that which a customer has given consent.
• Convincing Users to Share Personal Data – People might not be comfortable sharing their personal data.
• Laws and Regulations – Strict regulations around collecting and using personal data, such as the General Data Protection Regulation (GDPR), require companies to comply in order to avoid fines and legal issues.
• Cybersecurity – As reliance on technology rises, so do cyberattacks. Cybercriminals may access sensitive data on consumer behavior, making consumers susceptible to online scamming and identity theft, among other threats.

Conclusion

Leveraging IoB can provide businesses with a competitive edge and drive revenue growth. Companies seeking continuous success should consider placing IoB at the center of business innovation to create personalized customer experiences. At the same time, they must also examine any challenges that might reduce the effectiveness of IoB.

Natural language processing (NLP) is a technology that allows computers to understand and process human language. Processing of natural language is necessary when you want an intelligent device to follow your instructions. NPL is an artificial intelligence (AI) component with many real-life applications.

As technology advances, business leaders have to figure out how to tap into the new trends to remain relevant, stay ahead of competition, and meet consumer expectations and needs.

How NLP Works in Brief

NLP involves making computers perform tasks with the natural language humans use. The input and output can be spoken or written text. NLP combines computational linguistics – rule-based modeling of human language – with statistical, machine learning, and deep learning models.

NLP aims to build machines that understand and react to text or voice data and then respond with text or speech in a similar manner as humans do. Examples of NLP in real life include voice-operated GPS systems, personal assistant apps, speech-to-text dictation software, and customer service chatbots.

As businesses seek better ways to improve efficiency, NLP is one technology promising huge rewards for enterprises dealing with vast quantities of unstructured text. In accounting, unstructured data include transaction descriptions, invoices, written communication, etc.

The use of NLP is growing significantly in enterprise solutions designed to help streamline business operations. Large companies such as Deloitte, Ernst & Young (EY), and PricewaterhouseCoopers (PwC) have implemented various NLP solutions. A good example is Deloitte, which incorporated NLP into its Audit Command Language to improve contract compliance.

How NLP Can Improve the Efficiency of Accounting Processes

Areas in which NLP helps improve efficiency include:

1. Forensic Investigations
   When CPAs want to perform forensic investigations, they have to deal with significant amounts of data from documents such as bank statements, transaction data tables, and data found in emails or deposition transcripts. Analyzing all the data as they try to look for specific patterns or gain insights is challenging. However, the application of NLP can be helpful in the investigative analysis process. NLP using algorithms can identify patterns automatically and reduce the time it would have taken to analyze the documents.
2. Accounting and Auditing
   Auditing is challenging due to the process of reviewing financial statements and ensuring they match regulations and legal standards. Auditors must have excellent analytical and decision-making skills to spot inaccuracies in financial statements. However, NLP helps to optimize the auditing process.
3. Financial Analysis and Automated Generation of Financial Reports
   NLP can automatically extract financial data from balance sheets, income statements, and cash flow statements. This can cut down on time and error-prone work. At the same time, it can obtain insights from massive financial data sets and financial reports. This enables accountants to make data-driven decisions and quickly identify trends and patterns in the data, hence, making it easy to provide guidance to clients on investments and household finances.
4. Automated Data Entry
   NLP can be used to extract data automatically from unstructured text documents, including bills and receipts. It also can be used to automate the entry of data from tax documents and input it into accounting systems. This can cut down on time and error-prone work.
5. Improve Centralized Data Management Solutions
   Incorporating NLP in accounting and procurement helps improve the ability of a centralized data management system to collect and integrate data from different sources. This enables standardization and collaboration. Additionally, the data provided has higher-quality insights. As a result, there is better financial planning and improved risk assessment and management.
6. Customer Interaction
   NLP can be used to enhance the effectiveness of customer interaction. This is done by automating the procedure for responding to client inquiries, such as concerning invoices, payments, and account balances.

Conclusion

Natural language processing is proving to be a powerful technology that can help improve the efficiency and effectiveness of accounting processes. As it continues to evolve, it will likely become an increasingly important tool for accountants and other financial professionals. Most importantly, these advanced technologies take care of manually reviewing unstructured data. This helps businesses scale and – at the same time – reduce costs.

Technology advancement has brought about great digital transformation. Unfortunately, this has come with a global tech talent shortage. IT executives highlight the shortage as a huge barrier to the adoption of emerging technologies, as reported by this Gartner study.

It is estimated that the demand for tech talent will keep increasing, and this could result in an estimated 85 million global talent shortage by the year 2030. Therefore, companies need to rethink their approach to hiring and retention.

Reasons Behind the Tech Talent Shortage

It is worth trying to first understand what is causing the tech talent shortage. A few of the reasons that have led to the shortage include:

• Advances in technology – technology is advancing at high speed, requiring workers with skills to match the new technology. Unfortunately, the tech education system can’t keep up with the speed, hence a shortage of people with the required skills.
• The great resignation – this became a buzzword with work from home that came with the Covid pandemic; unfortunately, even after the pandemic, people are still leaving jobs. A survey by TalentLMS and Workable found 72 percent of employees working in tech are considering quitting their jobs or exploring other opportunities.
• High demand for tech talent – There has been an increase in the demand for tech workers in recent years as more businesses and industries turn to technology for daily operations. New technology creates new roles, such as data professionals, data security specialists, and software engineers, among others that are highly competitive.
• Challenges in training and development – some companies might not have the resources and time to invest in employee development.

Business Challenges of IT Talent Shortage

Businesses are feeling the effect of the tech talent shortage, especially when it comes to digital transformation. Emerging technologies such as robotic process automation (RPA), artificial intelligence, blockchain, and augmented reality that promise to keep a business ahead of its competition require skilled workers.

Hiring new talent or reskilling employees also comes at a cost, and companies struggle to fill positions. On the other hand, failing to have skilled employees results in unrealized annual revenues.

As a result, businesses of all sizes find themselves failing to develop projects on time and hence fail to meet deadlines. In other cases, the existing employees end up overburdened with too much work, and this may lead to them quitting. Eventually, a business experiences slow innovation and slow growth.

How to Handle the Tech Talent Shortage

A few strategies to help address this issue include:

• Investing in employee development and training
  Providing ongoing training and development opportunities for current employees can help them acquire new skills and knowledge. This will not only make them more valuable to your organization, but also less likely to leave.

• Attract top talent through a strong employer brand
  Building a strong employer brand can help in attracting top talent to your organization. This can involve highlighting your company’s culture, values and mission, as well as offering competitive compensation and benefits packages. A good reputation will also help attract new talent.

• Partnering with educational institutions
  A company may also partner with local colleges and universities to gain access to a pool of talented students who are looking for internships or entry-level positions. Additionally, setting up mentorship or internship programs helps build a pipeline of talent for your organization.

• Increase recruitment efforts
  Sometimes it might be difficult to find the right talent, which makes it necessary to increase recruitment efforts. This could involve working with recruitment agencies, posting job openings on job boards and social media platforms, and attending job fairs and industry events.

• Consider hiring remote workers
  Even with all efforts in place, it may still be difficult to find the right talent in a business location. Today, technology has enabled people to work remotely. This offers access to a larger pool of candidates and also can help attract top talent from other parts of the country or even the world. It is also possible to work with freelancers or contractors to fill specific skills gaps on a project-by-project basis.

• Enhance the recruitment process
  An inefficient recruitment process will cost the company good talent. Therefore, any poor communication or delayed communication will affect talent acquisition. A company might need to streamline its recruitment process.

Final Thoughts

The global tech talent shortage is already negatively affecting businesses. Since the shortage is expected to rise, business leaders need to decide on the best way forward so they are not left behind in digital transformation. A good decision should fit business goals whether choosing to hire internal talent, remote workers, or outsource technology needs.

Data has become a primary asset for businesses today. Consequently, the survival of a business in our data-driven environment is highly dependent on the ability to have total control over data storage, extraction, and manipulation.

As businesses continue being bombarded with vast volumes of data, datafication has become a big trend that provides a solution to turn data into quantifiable, usable, and actionable information. 

What is Datafication?  

The term datafication was coined by Kenneth Cukier and Victor Mayer-Schöenberger in 2013 when they explained it as the transformation of social actions into quantifiable data.

Today, much data is collected at the point of contact with any technology device. Aside from data such as text, images, and numbers, there are logins, passwords, device activity logs, clicks, interaction times, and more. Datafication helps translate all of these human activities into data, which is then repackaged in a form that offers value.

In business, datafication means converting every activity of a business model into actionable data. This has been enabled by a rise in technologies such as artificial intelligence, machine learning, big data analytics, and predictive analytics.  

It’s worth noting that datafication is not the same as digitization. While datafication is about taking all aspects of life and turning them into a data format, digitization involves converting analog content, such as images and text, to a digital format.

Examples of Datafication in Real Life

There are various ways datafication has been applied in real life, including:

1. Social media platforms – a lot of data is found on social platforms through profile updates, preferences, reactions, comments and posts. Such information is used for customer profiling.
2. Ad personalization – tech giants such as Facebook, Google, Apple and Amazon are already using collected data in their storage to personalize their ads and target potential customers.
3. In customer relationship management – data collected through language and tone in emails, social media and phone calls are used to understand customer needs and wants as well as buying behavior and personalities.   
4. Human resources – HR uses data obtained from social media or mobile apps to discover characteristics and personalities when looking for potential employees. They also use the data to assess employee productivity. This means that it may no longer be necessary to take personality tests, as the collected data can be analyzed to check if a person matches the company culture and role for which he applies.
5. Insurance and banking – understanding the risk profile of a customer applying for insurance or a loan, as the data is used to assess the client’s trustworthiness.  

Datafication for Competitive Advantage

With the above use cases, it is evident that businesses can leverage datafication to help improve operations, thereby increasing productivity and revenue.

For instance, collecting real-time customer feedback can help improve products and services. Additionally, it becomes easy to determine and predict sales by analyzing data from social platforms such as Facebook, Instagram and Twitter.

The information collected from social media, emails and other digital platforms is then used to create personalized campaigns, effectively targeting the most interested audience.

How Businesses Can Implement Datafication

Any trending technology that presents benefits to a business comes at a cost. Luckily, cloud computing eases datafication for businesses as they don’t have to worry about acquiring necessary hardware and software. With readily available software as a service (SaaS) or platform as a service (PaaS) technologies, businesses need only to define the goal they want to achieve with the data collected.

The main concern of a business remains the proper implementation of datafication. To begin with, it is best to ensure that the right technology – such as mobile devices, voice assistants, wearables, IoT – is used.

Next is to use appropriate platforms. Using the right platform will help effectively extract data that a business needs. Such platforms should also analyze massive amounts of data and produce reports that enhance decision-making.  

Another critical factor is to have a centralized repository where all authorized people in the organization can access the data.

Finally, it’s crucial to have skilled professionals in data infrastructure, data management and data analytics to evaluate and manage the data. This could either be an in-house team or outsourced.

Conclusion

Businesses that wish to remain relevant must consider datafication as part of their digital strategies. However, as datafication enters digital transformation, its successful implementation will require attention to data protection through adhering to legal requirements, technical measures such as access control, and best business practices.

Early technology adopters are more likely to gain better business results, including higher revenue growth and market position. With businesses facing complex problems every day, it is no doubt that they are always watching out for the next big tech that offers a better solution.

Although still in its infancy stages, quantum computing is a technology whose commercial use will disrupt the business environment.

What is Quantum Computing?

Quantum computing is a technology that focuses on manipulating and controlling different laws of physics. This non-classical technology uses quantum mechanical concepts like superposition and quantum entanglement.

The idea of quantum computing is not new and has come a long way. The first algorithm of large integer factorization for quantum computing was introduced in 1994. This algorithm intended to reduce the time it would take classical computers to find the prime factors of large numbers. It’s worth noting that the majority of the current infrastructure for encryption and information security is built on prime factorization.

Since the first algorithm was developed, more technological advances have been reported, and the field is continuously receiving funding. According to the McKinsey & Company Quantum Technology Monitor, funding from private and public sectors for this new technology is skyrocketing worldwide.

How it Works

Unlike classical computing whose information is encoded by bits, in quantum computing a qubit is the basic unit of quantum information. Qubit allows all combinations of information to exist simultaneously so that quantum computers can solve problems exponentially faster and with less energy consumption than classical computers.

In 2019, Google, in partnership with NASA, achieved quantum supremacy by demonstrating that quantum computers can compute in seconds what would take advanced supercomputers thousands of years.

Advanced development in this technology has also seen the introduction of quantum-computing cloud infrastructure through Quantum as a Service (QaaS). QaaS provides access to quantum computing platforms over the internet to customers. Major technology companies, such as Amazon, Alibaba, IBM, Google and Microsoft, have already launched commercial cloud services for quantum computing.

With the continued increase in the quantum computing ecosystem and emerging business use cases, business leaders must stay aware and prepare to adopt the new technology.

Business Use Cases for Quantum Computing

1. Quick Data Analytics

Today more than ever, businesses are faced with big data and a large quantity of information requiring analysis and storage. Since classical computers are built to solve one task at a time, it takes longer to solve these complex problems.

However, quantum technology has the potential to turn complex computations into simple calculations that are solved in less time.

2. Optimize Investment Strategies

Optimization is all about finding the most ideal solution in a situation. When many options are available, it takes a classical computer a long time to find a solution. Therefore, classical computers use shortcuts, and the final solution is partly optimal. But, with quantum computing, there will be better optimization.

3. Better Forecast and Prediction

Businesses rely on forecasts and predictions generated after analyzing complex and large data sets. Quantum computing is built to process huge amounts of data quickly and more accurately. As a result, better forecasts and predictions will enable better decision-making.

4. Solve Problems With Financial Services

There are various computationally intensive jobs in finance that could be facilitated by quantum computing, such as credit-risk management, financial crime reduction and trading strategy optimization. These tasks will greatly benefit from quantum algorithms that increase the speed of financial calculations.

5. Improve Data Security

Quantum computers are built to break encryptions that ordinary computers cannot. This might become a problem if hackers were to acquire encrypted data and store it until large-scale quantum computers are operational. To handle this problem, postquantum cryptography, a type of cyber security that can be used by conventional computers, is currently being developed. Therefore, a switch to quantum-resistant cryptography will prevent the possibility of data being exposed. At the same time, it will ensure better protection of digital assets.

Final Thoughts

Quantum computers will not replace classical computers; however, the two will form a hybrid solution whereby each task will be assigned to the most suitable machine – either quantum or classical.

Achieving the aforementioned benefits will require businesses to have teams of experts who are knowledgeable about the implications of quantum computing and who can recognize the company’s potential future needs, opportunities and vulnerabilities.

With signs of commercial quantum computing becoming a reality, it’s not too early for business leaders to consider how it will encourage digital investment, reshape industries and ignite innovation. Therefore, having a thorough understanding of quantum applications is essential for positioning a business to gain a competitive edge.