Data is becoming more important to modern organizations than ever before. However, many companies are still struggling to use it effectively. One poll found that 74% of companies feel they are still struggling to use data effectively.
One of the problems is that they don’t manage their data well.
How Companies Can Manage their Data Better
The process of managing data can be quite daunting and complicated. Data management is a set of processes and policies that organizations use to collect, store and share data. It involves understanding how the organization uses data and how the data is stored, and then working out what to do with it.
The main objective of data management is to collect, collate, analyze, store, manage, update, and use data for decision-making. Data Management is considered to be a core function of any organization. Data management software helps in reducing the cost of maintaining the data by helping in the management and maintenance of the data stored in the database.
Data management systems provide a systematic approach to information storage and retrieval and help in streamlining the process of data collection, analysis, reporting, and dissemination. It also helps in providing visibility to data and thus enables the users to make informed decisions. Data management software helps in the creation of reports and presentations by automating the process of data collection, data extraction, data cleansing, and data analysis. It also helps in the analysis of the data using sophisticated statistical tools and then producing reports and presentations for the decision-makers.
Data management software is useful in collecting, organizing, analyzing, managing, disseminating, and distributing information. The main objective of TDM is to facilitate the process of data collection, analysis, and decision-making. It helps in providing visibility to the data stored in the database and thus enables the user to make informed decisions.
There are various types of data management systems available. They vary in terms of their complexity and application. These include, but are not limited to, database management systems, data mining software, decision support systems, knowledge management systems, data warehousing, and enterprise data warehouses. Some data management strategies are in-house and others are outsourced.
Data management involves a few main components:
Databases: are used to store and retrieve data. They are a part of the data management system. A database consists of data structures or data models which are used to store and organize information. Data models help in storing and retrieving the data efficiently.
Data mining: is the process of discovering patterns in the data by applying different techniques such as data classification, clustering, regression, association, time series prediction, etc. These techniques are applied to identify hidden relationships and patterns in the data. Data mining software is used to discover the patterns in the data.
Decision Support Systems (DSS): help in making decisions by analyzing and synthesizing information. It is basically a process of developing and maintaining information systems, which are designed to assist the decision-makers by analyzing and synthesizing information. These systems are used to solve problems, make recommendations, suggest alternatives and help the decision-makers in making informed decisions. Decision support systems can be implemented at the tactical level, the operational level, or the strategic level.
Knowledge management: is the process of collecting, organizing, sharing, and managing knowledge. Knowledge management software is the tool used to manage and share knowledge.
Enterprise Data Warehouses: is used to consolidate the data from multiple sources into a central repository. This is done so that it becomes easier to find and access information in one place. Enterprise Data Warehouse is a tool that helps in analyzing the data and provides visibility to the information stored in the database.
Data collection: This is the gathering of information from a variety of sources. For example, we might collect data about our customers (such as their contact details), their likes and dislikes, their purchasing patterns, their preferences, and feedback. In the case of schools, we would need to gather information about their enrolment rates, their curriculum requirements, and how their students perform.
Data storage: We store this data in a computer, which is usually managed by a data manager. They need to ensure that the information is secure and available to those who need it. For example, we might store the data about the customer in a database.
Data analysis: Once we have collected the data, we analyze it using statistical methods, such as modeling, to see how it might be used to support decisions. In education, data analysis could involve analyzing student records to see if students are achieving.
To support data management, you need the right skills and knowledge. It requires a different mindset from the ones needed to simply manage records. In education, data management is about working with data and using it to support decision-making. This involves:
• Understanding what data means to your organization
• Having the skills to understand how the data is structured, what it says, and what it tells you
• Being able to collect and analyze the data
• Knowing how to access and use the data
• Having the right policies, procedures, and tools to support data management
It is important that you know what you are doing. The more you understand about the data you are managing, the better you will be at supporting your organization. To help you, you can look at the Data Management Checklist. This provides questions to ask yourself about the data you are managing. It may also help you to identify what additional training you might need to develop your skills.
You will also need to find out what data is needed, who needs it, where it is, and how the data will be used. This requires you to think about the context of the data, and your organization’s business strategy, as well as the data itself. It will help to understand your organization’s structure and the context in which data will be used.
Data management is a set of skills that you can learn and develop. These include:
Learning the basics of dataWorking out the purpose and meaning of the data you collectUnderstanding the difference between data and informationUnderstanding the purpose of data managementUnderstanding what your organization needs to achieve its goalsRecognizing when you have a problemKnowing what to doBeing aware of relevant legislationBeing able to work with the teamDeveloping your communication skillsKnowing how to work with ITGetting help when you need itHaving a clear set of policies, procedures, and practices
Data-savvy companies are constantly exploring new ways to utilize big data to solve various challenges they encounter. A growing number of companies are using data analytics technology to improve customer engagement.
Werner H Kunz of the University of Massachusetts Boston and some of his colleagues addressed this in their paper Customer Engagement in a Big Data World. They discovered that big data is helping more companies improve relationships with customers.
How Does Big Data Facilitate Customer Engagement?
Proper consumer engagement strategies are vital factors that might determine how profitable your business becomes. Learning to improve customer engagement has several benefits that may also help prolong your business’ lifespan. The good news is that data analytics technology can drastically improve your customer engagement strategy.
How much can data analytics help companies with their customer service strategy? According to an analysis from SAS.com, companies using customer service analytics had 31% better results with their customer engagement.
However, it is important to come up with the right strategies when using data analytics to improve customer engagement. When used with the right analytics tools, these strategies help create better customer experience, drive retention rates, and stimulates greater lead generation.
Several brands find it easy to beat their competition with a practical, effective, data-driven customer engagement strategy. You need to be consistent, engage with your base, and offer more than a bare-bones product.
If you don’t know how to do these, don’t despair!
The information in this article can help improve customer engagements to your ecommerce business or other ecommerce interests. Following these practices will make attracting more clients while sustaining the best customer experience with ease!
How Can You Use Data Analytics for Better Customer Engagement?
Customer engagement refers to interactions with consumers through different channels to build better relationships. The process of engaging customers focuses on creating value that transcends business relationships in consumers. Proper customer engagement will elicit an emotional attachment between consumers and your business. Businesses with strengthened customer relationships tend to receive more return patronage over time.
Data analytics is becoming a more important element of modern customer engagement. Here are some steps to integrate big data into your customer relationship building strategy.
1. Use analytics to understand your customers
Listening to customers’ desires and expectations might seem impractical for some, but it’s a vital strategy to boost engagement. Whenever customers establish contact, understand why they are communication to improve trust in your brand.
Data analytics is going to be an important part of this process. You can aggregate data on customer feedback and mine it when necessary to implement a better customer service strategy.
Consumers might take alternative channels to compliment or lay complaints about your services. Reaching out to your customers is a sure way to let them know you acknowledge their input. The approach also helps attract potential customers to your business.
However, their feedback will be of minimal benefit if it isn’t digitized and stored. The right data storage and analytics tools will make this easier.
You can better understand your customers by:
Itemizing all their complaints and providing solutions to each onePracticing prompt engagement, maybe within 24 hours of customer posting a complaintAdopting data-driven response tools (auto-generated emails, chats, etc.) to provide immediate answers to questions
Data-driven companies that take these steps will have better success.
2. Use analytics to optimize in-product ads
Digital marketing has tons of benefits, but don’t rule out offline marketing tools to drive customer engagements just yet. Data analytics can help optimize all of them!
In-product ads will be a relevant tool to improve engagements every single time. These ads are targeted and sometimes personalized messages placed on an ordered product. Data analytics has made personalization and targeting much more seamless.
An in-product message can help customers’ walkthrough product use, understand benefits, and much more. Other vital uses of in-product ads include:
Sending discounts and other personalized information in shipped products,Ads for new offers and benefits of purchasing your product, andNotifications of due invoices, current payment plans, and other available options
In-product ads focus on improving the overall customer experience consumers get while using your product. Data analytics makes this a lot easier to implement.
3. Use data analytics to assess performance and maintain a strong presence across online and offline channels
Customers prefer different communication channels, and you can’t miss out on any if you plan to improve customer engagement. You can use data analytics tools to better understand their response to different messages across various channels.
Apart from being active on social media, customer engagement receives useful boosts through other online or offline channels. Customers could communicate with your brand through emails, mobile apps, partner retailers and websites. Data analytics tools help you assess your ROI across all of these platforms.
With data-driven omni-channel engagement, you can bring all customer communications under one umbrella to guarantee better responses.
Proper marketing automation features from services like Sellvia could prove useful to provide multi-platform consumer feedback.
4. Leverage polling and store data on responses
Several platforms have polling features for individuals and businesses to leverage for data gathering. Polling ideas on new products and services could be the vital element to drive your customer engagement campaign.
Take advantage of polling to understand if your new product or service solves pressing problems customers face. You can take advantage of simple polls with options or go for detailed options with feedback forms.
Social media platforms like Twitter and Instagram are great locations to take full advantage of polling for customer engagement. You can also include surveys on your website landing pages to get vital feedback from your customers.
Once you have accumulated these responses, you will be able to store data on them and improve your strategies.
5. Use giveaways
Everyone loves a nice reward for patronage every now and then. Customer loyalty programs could be an ideal way to improve engagement with existing consumers and even reach out to potential consumers.
The most common rewards programs used by online brands include:
Early access discountsSpecial invitesSurprise discounts for loyal subscribersPoints accumulation per purchase (points can be converted into discounts for subsequent purchases), etc.
6. Use analytics to maximize social media ROI
Improving customer engagement without social media is just not possible today. With social media users numbering in the billions, neglecting the space will dent your customer engagement campaign.
You need to engage your customers where they are and support multiple social media platforms if possible. The most common social media platforms to drive customer engagement include:
Facebook,Twitter,Instagram,LinkedIn, etc.
Social media channels like Facebook and Twitter can help solve several customer engagement challenges. You could host contests, giveaways, provide customer service, and interact with your current and new consumers on these platforms.
However, you can’t just run an ad hoc social media strategy and expect results. You need to collect data on social media engagement and use it to optimize your strategy.
7. Adopt personalized services
Personalization isn’t possible in all markets, but where it’s possible, you can leverage it for customer engagement with ease. Curate products to meet your customers’ needs, respond to their complaints/suggestions in line with preferences to build engagement. Big data and AI have made this a lot easier.
You can guarantee improved customer engagements with the aid of available user data and information provided by consumers. Using big data and personalization drives web sales and boosts engagement.
8. Use chat automation made available with AI
Live chats help businesses maintain automated responses, allowing brands provide immediate solutions to customers’ questions/complaints. Using chat automation boosts communication with customers and adds a positive drive to your brand’s engagement campaign.
Big Data is the Backbone of Modern Customer Engagement Strategies
You can improve customer engagement, build better interactions, and keep your business profitable with ease. Businesses just need the right approach and platforms to guarantee a better customer experience for clients.
Sellvia provides the support your business needs to increase engagements that drive productivity. You can optimize every interaction with customers across all channels and get better lead generations with ease.
Did you know that global companies are projected to spend nearly $1.6 trillion on AI by 2030? The demand for AI services is growing due to the many powerful benefits it offers.
Various applications, from web-based smart assistants to self-driving cars and house-cleaning robots, run with the help of artificial intelligence (AI). With the growth of business data, it is no longer surprising that AI has penetrated data analytics and business insight tools.
Business insight and data analytics landscape
Most traditional business insight tools are no longer capable of processing and visualizing the growing volumes of complex data coming from various devices, channels, and business systems. Artificial intelligence and allied technologies make business insight tools and data analytics software more efficient. Many software developers use AI to create programs and apps for problem solving and decision-making. In addition, several enterprises are using AI-enabled programs to get business analytics insights from volumes of complex data coming from various sources. AI is undoubtedly a gamechanger for business intelligence.
AI and machine learning
Before you can have AI-driven apps, you need to train a machine learning model to do the work. This means feeding the machine with vast amounts of data, from structured to unstructured data, which will help the device learn how to think, process information, and act like humans.
As unstructured data comes from different sources and is stored in various locations. It needs a data management platform that can sort the data, analyze the data’s bits of information, and make it more accessible.
Benefits of AI-driven business analytics
A retail store with many outlets spread all over the country, for example, would use AI/ML-enhanced technologies to process product and customer data each outlet generates daily. They will be using business analytics software to process the data the outlets produce to help the company make strategic decisions based on business insights. AI makes it easier and faster for BI tools to analyze large-scale data. The time spent on analysis can affect daily business decisions and strategic actions.
As you can see from the above example, businesses today are data-centric. Most organizations base their decisions on what data tells them. AI-powered analytics and business intelligence tools can help identify why some strategies do not work, allowing them to change tactics and make new decisions according to the results. Here are some benefits you gain from AI-driven business tools.
It makes data preparation faster. Preparing data for analysis is time-consuming if you do it manually. Using AI-driven analytics can automate the process by collecting, extracting, and loading the appropriate data for analysis.Improves accuracy. Using AI-driven business analytics apps allows organizations to learn customer behaviors and patterns more accurately. In addition, they can understand the correlations with other statistics, helping them make changes in their product offerings, pricing, and marketing thrusts. Takes advantage of predictive analytics. Every organization should know that they could improve customer experience and business outcomes by understanding consumer behavior and industry trends. They can use predictive analytics to closely study their current situation and forecast future results.
You will have an industry-specific advantage with AI-driven business analytics tools. Of course, challenges with data analysis will always be there. Still, with the help of AI, you can quickly generate forecasts in real-time, identify changes, reduce churn rate, and minimize security loopholes.
AI and Business Analytics Support Each Other in Important Ways
There are a lot of important benefits of business analytics technology. These applications are becoming more effective, due to advances in AI technology. Smart companies will continue to leverage AI to make the most of their business analytics insights.
There are many reasons big data has become a double-edged sword for businesses. One of the biggest examples is with employee monitoring.
Many companies are using data analytics to monitor their employee productivity and other behavior. It can be even more beneficial than using big data for recruiting. This is an area where big data can help immensely. However, it also raises some concerns. Employees are more concerned than ever about their data privacy. Regulators also require employers to be cautious about how data is stored and used.
Employers Must Find Ethical Ways to Use Data Analytics to Monitor their Workers
Since 2020, companies have been struggling to find alternative methods to supervise their workers, given that 97 percent of employees would prefer to work remotely. That explains why the demand for employee monitoring software has been on the rise steadily since the pandemic. Data analytics technology has made this a lot easier.
According to a recent survey by Instant Offices, 78 percent of companies use employee monitoring software to track work performance and online activity. This heightened surveillance, while it makes sense due to the lack of physical supervision, still raises ethical concerns.
Knowing the do’s and don’ts of using data analytics for employee monitoring will help you create a fair, transparent, and non-invasive employee monitoring strategy.
The Biggest Concerns Employees Have About Workplace Surveillance in a World Governed by Big data
Employers find employee monitoring beneficial since it provides the logical solution to overcoming the challenges of remote work while ensuring sustained productivity. Big data has made this easier than ever. With the right data analytics tools, employers can:
oversee employees’ work from anywheretrack project progressassess performanceprotect themselves against lawsuits and insider threatsprevent inappropriate workplace behaviorprotect their companies against security breaches
However, employees have different perceptions of employee monitoring with big data. Your intention for collecting employee data should be to create a company culture that enables workers to perform their best. It’s not to spy on them.
But that may not be how your remote workers are seeing it. Some of the biggest concerns of employee monitoring are:
Trust Issues
The first question that employees will raise when you introduce workplace surveillance is whether you trust them. They will most likely think that you’re implementing the tools to spy on them.
This can lead to feelings of anger and dissatisfaction, which will turn to disengagement.
Privacy Concerns
Monitoring your employees’ activities raises privacy concerns. People use computers for personal use and would prefer to keep their personal stuff private. They won’t like the idea that everything they do is being tracked.
Breach of Personal Data
Workplace monitoring involves collecting data about your employee’s computer usage. Once collected, they have limited control over its usage or security. They won’t know who will have access to their data or how the data will be used.
This raises concerns among employees if their data may be misused or used to punish them.
Employee Monitoring Do’s and Don’ts
With the points above in mind, you should consider privacy, trust, and ethical issues when creating your employee monitoring strategies. Otherwise, your remote workers’ morale and productivity will suffer.
So, what’s considered ethical and unethical in workplace surveillance?
The Do’s of Employee Monitoring
Be Transparent
The number one rule within the monitoring space is transparency. Employees should be aware that you’re monitoring them, so they don’t mistake it for spying. Make it absolutely clear that you don’t intend to surveil their personal activities, you just need to collect data to help boost workplace productivity.
But don’t stop there. The key is to make sure your employees are as comfortable as possible with employee monitoring. For example, you can send them feedback with data showing how much time they are spending on personal social media or other non-work-related stuff.
A survey by the American Management Association revealed that 84 percent of employers notify their employees that they review computer activity. Greater transparency will have the following benefits for your organization:
Increased buy-in for employee monitoring software. A DTEX and Harris poll shows that 77 percent of employees won’t have a problem being monitored as long as their employers are transparent about it.It builds trust. Being transparent about workplace monitoring reduces the negative impact associated with the practice. While it won’t clear all doubt in employees’ minds, they will be more understanding if you’re open about it.Encourages self-management. Allowing your workers to access and use the collected data can help improve autonomy and empower them to self-manage their workflow.
Create an Employee Monitoring Software Policy
An employee monitoring policy will help you define your intentions more clearly. Some of the things you should address in the policy include:
Reasons for monitoring your employeesWhat you’ll be monitoring: internet use, social media, emails, calls, employees’ work screens.Outline the type of monitoring you’ll implement: location (GPS) tracking, keylogging, audio, time tracking, screen and video capture, etc.How many hours will you monitor? Will it be only during work hours?
Clarifying all these lets them know what to expect and helps dispel trust issues.
Communicate Before, During, and After Implementing an Employee Monitoring Software
In the spirit of being transparent, you should make sure your remote workers understand why you want to monitor them. Of course, remote workers should expect some form of surveillance due to the lack of physical supervision.
That said, communication is the key to the successful implementation of your monitoring strategies. Before introducing the tool, make sure they understand the reasons for monitoring, which include:
Using time tracking and monitoring apps provides accurate time logs that will help facilitate accurate payments. This reduces both time and money waste for both the employer and employee.The tools bring issues with overworking to light, enabling you to distribute work equally and prevent burnout.The apps will expose cases of workplace malpractices or abuse, allowing them to be resolved internally.
Clearly lay out how monitoring will be in the best interests of everyone concerned. Once implemented, follow up to get feedback, insights, and queries about the process. What can be improved? Is the tool aligned with company policies?
Making sure that everyone is satisfied with the program will promote trust and buy-in.
Use Data for its Intended Purposes Only
Keep in mind, ethical employee monitoring isn’t just about how you collect employee data – it’s about how you intend to use it. The data should strictly be for the specific purpose of improving productivity.
Sharing your intentions openly with your employees about how their data will be used to help them goes a long way in winning them over. According to an Accenture survey, 89 percent of employees are open to data collection on them and their work if it will improve their performance and wellbeing.
Protect Employee Data
Your workers may not be comfortable about who will have access to their data. They may also be concerned about the possibility of blackmail and data leaks.
You must assure them that you’ve taken measures to safeguard the data from misuse. But, how can you keep the employee monitoring data safe? Here are some tips:
Limit access to employee data only to key stakeholders in your company. The stakeholders must understand the privacy expectations of the employees and be well versed with the company’s policies regarding employee data.Treat all recorded data as sensitive. Apply the necessary administrative and technical security measures to protect employee monitoring data. Apart from limiting access to authorized personnel only, make it mandatory to acquire authorization to access the databases. You should also remove from the database any data that’s no longer relevant.
Assure Employees You’re Observing State Laws
Be sure to keep in mind the labor and privacy laws that protect employee privacy when drafting your employee monitoring policy. Each state and country have different laws about what employers can and cannot monitor.
Remember, what you’re monitoring affects how you can do so legally. For example, the Electronic Communications Privacy Act (ECPA) protects against intercepting in-transit digital electronic communications. However, it provides exceptions for businesses.
Likewise, the General Data Protection Regulation (GDPR) act in the European Union requires employers to get the employees’ consent before monitoring them.
Make sure your employee monitoring strategies align with the existing monitoring laws of the countries your employees belong to. More importantly, let them know that you’re sticking to these laws. This will help minimize ethical issues in the workplace.
Just to be safe, consult with your lawyer about these laws and regulations before implementing the system.
The Don’ts of Employee Monitoring
Don’t Spy on Workers
Don’t track your employees just for the sake of it. The employee data you collect should be used to streamline and improve your work processes over time. The tools are meant to provide proof of work, which can help prevent inaccurate payroll processing, among other things.
When you use the data for the sole reason of spying, then it becomes unethical for employee monitoring. You’ll risk losing employees or even possible legal actions against your unethical practices.
Don’t Collect Data Outside of Work Hours
The remote work environment has raised the issue of after-hours monitoring. Often, employees will use the company devices for personal matters after their shifts have ended. If you record their activity during these hours, chances are you’ll capture sensitive personal details that could have serious implications.
The best approach is to forbid the use of company-owned devices outside work hours for personal use. Alternatively, remind employees to turn their trackers off while on break or after their shifts.
Your personnel will have peace of mind knowing that what they do during their free time stays private.
Don’t Secretly Monitor Your Workers
Depending on the laws governing workplace monitoring where you’re located, you may not be obligated to disclose that you’re monitoring your workers. In other states, employee consent is required. We recommend letting them know either way.
If you don’t, and they find out they are being monitored without their consent (even if the law allows it), it can lead to stress, anger, and anxiety. All of which will reflect negatively on their performance and productivity.
When you keep them in the loop, they will consider you an honest and open employer. This will help cultivate a culture of trust and honesty. Moreover, your workers will feel valued for seeking their permission to track them.
Don’t Collect Personal Data
Most employee monitoring tools come with the screenshot feature, while others allow you to record the screen and keystrokes on your staff’s computers. For the most part, the screenshot function serves as proof of work to prevent micromanaging.
However, the screenshots will also capture confidential data like bank accounts, passwords, and personal social media activities. These are personal data that you aren’t supposed to collect.
To avoid this, invest in software that respects employee privacy. For example, a free clock in and out app that intentionally blurs screenshots ensures that employers still track what the employee is doing without seeing the contents.
Additionally, the software should give employees control over activity monitoring. That is, they should be able to delete screenshots they don’t want to be seen or stop activity tracking altogether. Employees will appreciate the autonomy to dictate when they want to be monitored.
Don’t Monitor More than You Should
Invasive monitoring can be demoralizing and illegal. Doing it excessively infringes on the proportionality principle of certain data privacy frameworks, including GDPR. For example, monitoring website and app usage should provide enough data to enforce internet use policy.
It becomes invasive when you use keyloggers. Try to find less invasive ways to collect employee data to meet your business goals.
Don’t Monitor Selectively
Employee monitoring shouldn’t apply only to remote workers. No matter where your employees work, they should all benefit from the productivity and performance insights gathered from activity monitoring.
Tracking one group and not another is a recipe for conflicts, unhealthy workplace relationships, and a lack of cohesiveness. It may also lead to issues with employee morale and workplace fairness.
Final Word on Ethical Employee Monitoring with Data Analytics
Regardless of the type of employee monitoring you use, it’s best to lean on the side of transparency and balance. Focus on creating employee monitoring strategies that are less invasive, and most importantly, respectful of employee privacy.
When you’re open about employee data usage, data collection won’t be a source of concern for your remote workforce. You can then gather insightful data that you can use to maximize workflow, task delegation, and employee productivity.
As businesses strive to remain competitive in today’s economy, the question of how to best compensate employees becomes more important. Many companies are turning to artificial intelligence (AI) to help them create an ideal employee compensation package.
AI can analyze data from a variety of sources to help businesses determine which benefits and salary levels will be most appealing to potential employees. In this blog post, we will discuss how AI is being used to create optimal employee compensation packages and the benefits this technology can offer your business.
The use of AI to create employee compensation packages is a relatively new practice, but one that is growing in popularity. By using data from a variety of sources, businesses can get a more holistic view of the market and what potential employees are looking for in a compensation package. This information can then be used to create customized packages that are designed to attract and retain the best talent.
AI stands for artificial intelligence. It is the result of applying cognitive science techniques to artificially create something that performs tasks that only humans can perform.
Today, AI is used for tasks such as identifying objects in pictures or videos, translating languages and providing customer service. AI is also being used to develop self-driving cars and to create personalized recommendations (such as what music you might like).
There are different types of AI, including natural language processing and machine learning. Natural language processing is where a system understands human language and responds just like humans. Machine learning on the other hand is where the computer system can learn from its own data.
AI has been around for centuries, but it is only recently that it has started to become more mainstream. This is because of advances in computing power and data storage. It is also because of the increasing amount of data that is available.
Despite its long history, it only recently started to become more mainstream, and it has also entered into the recruitment and employee payment sector.
Employee compensation is the total amount of money that a company pays to its employees in return for their work. It includes salaries, wages, bonuses, and benefits. Benefits can include health insurance, retirement plans, and paid time off.
In order to determine how much to compensate employees, companies must first assess what the job is worth to the company and then what similar jobs pay in the marketplace. The goal is to set compensation levels that are fair and competitive so that the company can attract and retain the best talent.
There are many factors to consider when designing a compensation plan, but some of the most important include:
The cost of living in the area where the company is locatedThe cost of labor in the industryThe company’s budgetThe skills and experience of the employeesThe market value of the jobs
The most important factor in setting employee compensation is to make sure that it is fair and competitive. By doing so, companies can attract and retain the best talent. Other factors to consider include the cost of living and labor in the area, as well as the company’s budget. Employees’ skills and experience should also be taken into account when designing a compensation plan.
Finally, businesses must also look at the market value of similar jobs when determining how much to pay their employees.
Once the company has determined how much to pay employees, it must then decide how to structure the compensation.
Introducing AI and Its Ability to Create Ideal Employee Compensation Packages
The potential for AI in the world of employee compensation is vast. With its ability to analyze large data sets and identify patterns, AI can help employers create more customized and fair compensation packages for their employees.
AI can also help employers save time and money when it comes to creating these packages. By automating the process of analyzing data and identifying patterns, AI can help employers create compensation packages much faster than they could without it.
Overall, AI has the potential to revolutionize the way employers think about employee compensation. By making the process fairer, easier, and faster, AI can help employers create better working environments for their employees. And that’s something everyone can benefit from.
The Factors That Go into an AI-created Package: Cost Per Hire, Skills, Experience, and Location
When it comes to creating an AI-created package, there are a few key factors to consider. First, you’ll need to make sure that the AI has the potential to include all the necessary packages required for calculating compensation for employees.
Then, you’ll need to take into account how it will be implemented. And lastly, you’ll need to make sure that the AI is compatible with your company’s culture. By taking all of these factors into consideration, you can be sure that you’re creating a package that will help your company succeed.
An example of the packages that need to be taken into consideration is the cost per hire. The cost-per-hire metric plays a great role in knowing the cost the company can incur in recruiting the employees or filling an open position. It is important because it can help you determine whether your recruiting process is efficient and cost-effective and later makes your compensation judgment easier.
To calculate your cost per hire, simply divide the total cost of your recruiting efforts by the number of employees you hired. For example, if it cost you $5000 to fill four open positions, your cost per hire would be $1250 ($5000/4000).
Keep in mind that the cost per hire includes more than just the money you spend on advertising and job board postings. It also includes the salaries of your recruiters, any agency fees you pay,
Secondly, another factor that needs to be included in the AI is the skills, experience, and even location of the employees. These factors, as we are going to look at it in detail in this article, are important in calculating the overall compensation for each employee.
Comparing The Traditional HR Process of Creating a Compensation Package to Using AI
The traditional HR process of creating a compensation package is very time-consuming. It involves collecting data (skills, experience, location, and even cost per hire) from various sources, analyzing the data, and then making a decision on what the compensation package should be. This process can take weeks or even months to complete.
On the other hand, using AI to create a compensation package can be done in a matter of minutes. All you need to do is input the data into the AI system and it will analyze it and make a decision on what the compensation package should be. This is a much faster and more efficient way of creating a compensation package.
The Benefits of Using AI to Create Employee Compensation Packages
There are many benefits to using AI to create employee compensation packages. Perhaps the most obvious benefit is that it can help businesses save time and money. Rather than spending hours conducting research or surveys, businesses can simply let AI do the work for them. In addition, AI can help businesses stay up-to-date on changing market conditions and employee preferences.
Here are more benefits of using AI for this purpose in detail:
You can use data from previous years to predict future trends in employee compensation. This means that you can be proactive in offering competitive salaries and benefits packagesYou can use AI to identify patterns in employee behavior. This information can be used to create targeted retention strategiesAI can help you automate the process of creating and updating employee compensation packages. This saves time and ensures that your employees are always up-to-date on the latest changesYou can use AI to create customized compensation packages for each employee. This ensures that each person is getting the specific benefits they needAI can help you identify which employees are most likely to leave your organization. This allows you to take steps to keep them happy and engaged with their workYou can use AI to monitor changes in the labor market. This helps you ensure that your employees are always being paid a competitive wageAI can help you create a system that automatically adjusts employee compensation based on performance. This encourages employees to always be striving for excellence in their work.
These are just some of the benefits of using AI to create employee compensation packages. As you can see, there are many advantages to using this technology in your organization.
How to Get Started with AI For Your Organization’s Employee Compensation Needs
Here are four tips to get started with AI for your organization’s employee compensation needs:
First, you need to identify what data you have available. This data will be used to train the AI systemNext, you need to determine what objectives you want to achieve with AI. Once you have these two elements in place, you can begin training your AI systemFinally, once your AI system is trained, you need to implement it into your organization’s compensation process
Conclusion
In conclusion, we believe that AI has the potential to help organizations create an ideal employee compensation package. While there is still some work to be done in this area, we are excited about the possibilities and look forward to seeing more advances in this field.
Data breaches are becoming more common than ever. The Thales Data Threat Report found that 45% of companies suffered a data breach in the past year.
Online businesses are among the most likely victims of data breaches. They are more easily exposed to hackers, which means they have to do more to protect sensitive data.
One of the ways hackers target online businesses is through social media. You need to implement the right data protection safeguards to stop social media hackers from committing a data breach.
How Can You Protect Your Online Business from Being the Victim of a Data Breach through Social Media?
Compromised passwords, stolen identities, and sensitive data falling into the wrong hands are just some of the common ways that the online world can prove detrimental to personal data. Companies worldwide experience the pressures of an online existence filled with hackers lurking around every corner. Every few years, billions of records are stolen through data breaches, which shows that the threat of online hacking is getting more and more serious.
Fortunately, as a business owner, there are cybersecurity solutions that you can implement in order to safeguard your data on popular social media platforms. Below, we will discuss 7 of our favorite helpful tips that will allow you to ensure a better sense of security across all platforms and deter hackers from even attempting to harvest your data.
1. Use Specialty Passwords
Not only should your business data have password protection, but your passwords should use at least 8 characters– including special characters, upper and lowercase letters, and numbers if possible. The first line of defense against hackers is always having a strong password!
2. Avoid Keeping the Same Passwords
The more social media platforms your business uses, the higher the chances of dealing with compromised data. Make sure to regularly change your passwords in lieu of new ones, and most importantly, don’t use the same passwords across all platforms! Having different passwords in use across platforms will make it harder for hackers to gain access to sensitive information.
3. Use Unique Security Answers
Passwords alone aren’t enough to bypass the efforts of hackers. Make sure you enable security questions on your accounts, but never select the easiest to answer ones. For example, questions that deal with maiden names or your DOB are easily found out by hackers, so select a question that has a wholly personal answer. This precaution can do a lot to stop a data breach.
4. Use Anti-Virus Software
Many hackers use malware to steal data from their targets. Have a solid anti-virus software installed on your computer system. By only accessing your social media accounts with improved security on your mobile devices and computers, hackers will have a much harder time compromising your information.
5. Use Encrypted Cloud Services
As most files are now stored on Cloud drives, it’s crucial to choose a host that handles security seriously. For example, only opt to deal with a company that encrypts the data as it is being transferred to its final destination. Microsoft Azure is a great example of a product that does just that!
6. Avoid Clicking Phishing Emails
Many of the worst data breaches of all time have been driven by phishing emails. The data breach of the U.S. power grid is a great example. John Podesta’s email was also hacked through a phishing attack, which led to another terrifying data breach.
It probably goes without saying that you should never click foreign links or download files from email sources you don’t recognize. Phishing emails are getting more sophisticated each year, but that doesn’t mean millions don’t fall prey to hackers in the process! Always check the originating email address before considering clicking on a link, even if at first glance it looks legitimate such as Twitter, Facebook or PayPal. Remain aware that phishing schemes don’t just take place via email, but across social media platforms that offer messenger services.
7. Keep Your Software Updated
Outdated programs and apps that don’t run the latest versions are easier for hackers to compromise, which is why you should keep your mobile devices and computers constantly updated according to Certum IT services. This is essential if you are serious about stopping data breaches. Bear in mind, this also goes for your operating system, as the older it is, the more loopholes that hackers will find in order to gain access. Most operating systems are automatically updated, as long as you have this function enabled on all of your devices. However, if you have an older model computer or device, you should still look into either purchasing a new one or getting ahold of new updates.
Data analytics technology is rapidly becoming a more integral part of many company cultures. According to the 2021 State of Data Maturity Report, 32% of companies have formal data strategies. Although they are still the minority, this figure has risen from almost nothing under a decade ago.
Data analytics serves many different purposes. We have talked at length about the benefits of using big data to improve financial management and implement more effective marketing strategies.
However, one of the most beneficial aspects of data analytics is with building an effective workplace. Many companies are using big data to get a deeper understanding of the nuances of their company culture. They are also using it to improve relationships between employees.
Using Data Analytics Can Help Create a Better Company Culture
We previously interviewed a number of company executives on the benefits of using data analytics to improve their company culture. The findings from these interviews is even more important today, as the need to improve company cultures is more pressing than ever.
The unexpected onset of a global pandemic has led to disruptive progress in workplaces all over the world. Companies have been experiencing painful and uncertain times that are making them re-evaluate their policies and welcome the concept of remote working. As the demand for flexible working increases and the vaccination drive is being accelerated across the world, it is time for companies to think of workplace transitions with a human-centric and hybrid approach in near future. Data analytics can help them bridge that gap.
Data analytics technology is making it easier for them to revamp their workplace management practices. This is going to improve employee satisfaction.
The change necessary for the future
The idea of human-centric, data-driven digital workplace solutions makes even more sense in the current scenario. After all, business models have to be simple and human to maximize productivity. Employee values have to be taken into account in this case.
Naturally, the industries, customers, and clients are all agreeing to shift towards a digital workplace experience predicated on big data. This comes with enhanced flexibility because of their loyalty to organizations promoting holistic workforce growth and sustainable actions. Simply relying on delivering good services or products doesn’t suffice anymore for a company. They must use data analytics to keep up with changes and overhaul their business models as needed.
Companies will have to take a data-driven approach to retain talent in the new normal. After all, with digital workplace services coming to the forefront, employees are focusing more on things like remote working, work-life balance, and integration, instead of only professional growth.
Using Data Analytics to Create Human-Centric Workplaces
What are specific ways that data analytics can help create a better workplace? Some of the benefits are listed below.
Monitoring employee wellbeing – The pandemic has made the emotional and mental health of the workforce take a hit. Thus, a digital workplace transformation has to involve checking on employees and ensuring that their expectations are met from the workplace. Companies can use data analytics to monitor employee sentiments, as well as indirect precursors of their happiness. This can include evaluating data like leave time used, which might be an indicator of stress. Flexible working hour – Most of the workforce has enjoyed this particular opportunity since the pandemic. They didn’t have to spend time commuting, spend more time with family, and balanced domestic chores. However, the value of the office is going to emerge as the future workplaces are away from home and take into consideration different tasks and working styles that cannot always be done from home. Data analytics can help companies determine what tasks must be accomplished and make sure they don’t schedule more employees than needed. This saves employees from having to come in for idle work. This data-centric approach allows for greater scheduling flexibility. Interpersonal Relations – As workplace managed services have made progress in recent years, it is necessary to consider that team building initiatives are affected in remote work scenarios. The future, hybrid workplaces that employees are soon going to come back to will offer more opportunities for dynamic interactions among colleagues and teams. Data analytics can help companies keep track of performance of employees during these team building initiatives. External and internal growth – Workplaces will have to create prospects for job and skill growth within the hybrid workplace. Organizations will even encourage employees to develop such skills that might go beyond their current position but be necessary for the future. This is arguably the best application of data analytics. Companies can use data tools and OKRs to see how various behaviors contribute to organizational goals.
Data Analytics is Crucial for Companies Striving to Improve their Company Culture
Showcasing empathy towards the employees is a crucial aspect of effective leadership, regardless of whether you have a software company or offer end user computing services. It is bound to encourage interactions and lead to positive communication. A workplace that upholds its values, while holding on flexibility and productivity, is going to go a long way in establishing the model of the future workplace. And the period of transition between this workplace transformation has to be all about welcoming the ‘human’ factor in human resources.
Artificial intelligence (AI) is all the rage now. It’s impacting numerous industries globally and changing the way we do things. One of the critical industries AI is making strides in is the financial technology “fintech” industry.
AI now plays a significant role in facilitating financial services, replacing what required manual work a few years ago. For example, banks now apply AI to assess credit risks with high accuracy. They also use it to detect fraud in massive financial networks with success.
AI in fintech is here to stay. It has already made a big dent and is simultaneously proliferating. According to P&S Intelligence, AI in the fintech market is expected to grow to $47 billion in 2030 from $7.7 billion in 2020.
What is artificial intelligence?
Artificial intelligence refers to intelligence demonstrated by machines instead of the natural intelligence displayed by humans. In the modern sense, it’s a broad subfield of computer science concerned with building machines exhibiting intelligence similar to that of humans.
Artificial intelligence has four common goals for machines;
Systems that think humanly;Systems that act humanely;Systems that think rationally;Systems that act rationally.
Humanity hasn’t yet achieved the ultimate goals of artificial intelligence, even though we’ve seen remarkable progress in the area.
How do fintech companies apply artificial intelligence?
Credit risk assessment entails estimating the probability of a prospective borrower failing to repay a loan. It’s critical to financial institutions such as banks and credit unions that earn revenue from lending money with interest. Every time a financial institution lends money, it bears the risk of the borrower being unable to pay it back. Hence, banks go through the pain of assessing every prospective borrower’s creditworthiness. They consider numerous factors such as outstanding debt, payment history, credit history length, and credit card utilization.
Conventionally, creditworthiness is approved manually by humans. But, no matter how clever a human is, they’re bound to make mistakes when assessing hundreds of thousands of customers daily. On the other hand, artificial intelligence can perform the task without getting tired.
Computer programs can automatically analyze multiple factors affecting a customer’s credit and generate a score immediately. Then, a human makes the final decision. The AI takes a lot off the human’s plate with its analysis. The human operator has to double-check only when the AI detects red flags in a borrower’s credit history.
Many new-age startups have built big businesses selling AI-powered credit checking software to financial institutions, e.g., Upstart, which handles consumer loans.
Fraud Detection
Fraud is a severe problem for every financial services company. Banks, payment and trading apps, middleman lenders, and the likes deal with unprecedented fraud daily. According to fraud detection firm Feedzai, banking fraud attempts soared 159% from the final three months of 2020 to the first quarter of 2021, with the majority performed online. The US Federal Trade Commission said consumers lost $5.8 billion to bank fraud in 2021, up 70% from 2020.
With bank fraud already substantial and still on the rise, financial institutions always seek new ways to tackle it. Artificial intelligence presents a sophisticated way to detect and prevent fraud in financial networks. You can use AI to analyze vast numbers of transactions to identify fraud trends. If the AI model detects any, it can flag them for further investigation or automatically halt them.
Computer programmers can apply machine learning (ML) techniques to detect unusual transactions in a bank’s network. You can train a machine learning model for fraud detection by feeding it with many transactions and assigning them to “fraud” or “non-fraud” categories. The model learns from this approach to flag transactions that resemble “fraud.” This method isn’t perfect because criminals can be very clever. Hence, you must constantly train the machine learning model to detect new types of fraud.
Customer Support
Customer support is essential to every financial firm. Unhappy customers tend to take their money to other firms, so most financial institutions work to offer excellent customer support. Customer support typically requires an army of staff to handle and is often hectic and expensive. You should notice that many companies outsource their customer support to other countries with lower wages to cut costs. AI can help with customer support alongside human operators.
The most common use of AI in customer service is chatbots. Chatbots are software apps used to conduct online chat conversations automatically. It’s programmed to send automated responses to specific customer inquiries instead of direct contact with a customer support agent.
Chatbots simulate human-like conversations, so enterprises use them for customer support. Financial institutions can program their chatbots from scratch, but this isn’t easy. The easier way is to adopt a ready-made chatbot platform and customize it to their specific needs.
Financial institutions can also use AI to assist human support agents. They can apply artificial intelligence to customer inquiries to automatically interpret what they’re asking and display the relevant materials. Doing so saves time for the agent and customer and reduces cost.
Cybersecurity
This use case is related to fraud detection but not the same thing. It involves securing the bank’s back-end platforms rather than the user-facing apps.
Artificial intelligence is bolstering cybersecurity for financial institutions and other enterprises in many ways. A unique way is automating many cybersecurity tasks that a human analyst would usually perform. These include scanning code repositories, servers, workstations, and other hardware for known vulnerabilities.
AI can consume billions of billions of data artifacts within a computer’s network. It then analyzes these artifacts to detect atypical behavior. AI also helps battle bots within a network, differentiating the good bots (like web crawlers) from bad bots and stopping the latter. Cyberattacks are growing in complexity and volume, so enterprises can’t use the cybersecurity tools of old to tackle new threats. Hence, the application of AI in the cybersecurity field has been welcomed with open arms.
Many new startups specializing in applying artificial intelligence to cybersecurity processes have propped up and built solid businesses.
According to MarketsandMarkets, AI in the cybersecurity market is projected to grow from $8.8 billion in 2019 to $38 billion in 2026.
Trading/Investments
Artificial intelligence has made its way into trading and investments in the financial sector. Fintech firms now use AI to help identify good investments and trading opportunities. A typical example of this use case is automated investment apps, also known as “Robo advisors.” Robo-advisors use AI to analyze millions of data points concerning stocks, bonds, commodities, or other investment assets. The analysis helps them execute trades at the most optimal prices.
Likewise, AI trading bots have become ubiquitous. Self-directed trading is hard (the majority of day traders lose money), so people often opt for algorithmic trading bots powered by artificial intelligence. Trading bots are computer programs that trade based on specified conditions, e.g., when a stock price falls below a certain level. According to Mordor Intelligence, the algorithmic trading sector is expected to grow at a compound annual growth rate (CAGR) of 10.5% from 2022 to 2027. There won’t be such significant growth if investors weren’t seeing profits from the sector.
There are a few dedicated marketplaces for buying automated trading strategies, e.g., the MQL5 Marketplace. These marketplaces enable traders to buy and use trading bots developed by experienced programmers of MQL5.community. The community responds to traders’ requests for custom development. If you have no programming skills, you can submit a task to the MQL5 Freelance Exchange and get a response from one of the 1,200 professional developers familiar with algorithmic trading.
Developers also use AI to backtest their trading strategies. Backtesting refers to testing trading models based on historical data. MetaTrader 5 Trading Strategy Tester is an excellent example of a platform that allows investors to backtest AI-based investment strategies.
Hedge funds, private equity funds, mutual funds, or other investment firms need research to make sound decisions. A lot of time and effort is dedicated to research in investment firms. But, manual analysis is difficult and stressful. Hence, such firms adopt artificial intelligence to help them analyze data to identify promising investments. For example, AI can derive the frequency of shopping at retail stores by analyzing traffic footage. With this information, funds can know which retail chain had good quarters and invest in them ahead of their official earnings statements.
Insurance
Insurance entails protection against risks no matter how low the probability of them occurring. Individuals and enterprises buy insurance policies, and the regulated firms selling these policies consider risk profiles to determine their prices. Now, insurance providers are using artificial intelligence to assess risk levels.
The auto insurance sector is the most typical use case for AI in insurance. Auto insurers can collate and process real-time data from in-car sensors to assess a vehicle’s condition and accident risk, thanks to artificial intelligence. They can also monitor a driver’s behavior and performance to determine accident risk. They can price their services better with this information at their fingertips, charging “good” drivers lesser premiums than “bad” ones.
Insurance providers also use AI to automate claims collection. AI bots can walk a customer through the process of filing and collecting claims in a conversational tone.
We barely touched the tip of the iceberg. Artificial intelligence is applied in many more ways in the fintech sector. Undoubtedly, AI and machine learning are here to stay, and any financial services firms not adopting them are doing themselves a disservice.
Data annotation helps the developers check each field after entering data into a database or creating a form. This is especially useful if no error checking has been implemented.
Data Annotation converts data from code to presentation format. Quality controls ensure the correct input of data into a table, record, or control. Performance affects speed since validation occurs before the submission of data.
Some companies specializing in services for data annotation can help you increase your productivity and efficiency.
What are the advantages and disadvantages of using these two methods?
Advantages:
Easy to implementCan be used with any language (VB.NET, C)No need to write custom logic
Disadvantages:
Requires more coding than data annotation aloneMay not perform as well as data annotation aloneNot all controls support it
How can I validate user input against an array of values?
You could use a ListBox, which allows users to select multiple items. Then you can loop through the selected items and compare them to your list of acceptable values. You can add a required attribute to the textbox that will cause the system to throw an exception when the value is blank.
What is the difference between a validator and a validator control?
A validator is a component that performs validation on a single property. Validators are often placed directly on the properties. A RequiredFieldValidator might be applied to a TextBox by setting its ControlToValidate property to “TextBox1”. The validator would then contain a Boolean expression that checks the value entered in the TextBox.
A validator control provides a way to group related validators so that you don’t have to apply them individually to every element. In addition, it lets you reuse common validators across different elements.
For example, you can define a general-purpose validator that applies to most of your fields and then apply that same validator to individual controls such as TextBoxes and RadioButtons.
Why is Data Annotation Important?
Data Annotations allow you to specify rules about how data must be formatted. These annotations are applied to classes, properties, and methods to enforce business rules at compile time, making it easier to spot errors.
What is a Data Annotation Tool?
Annotation tools are cloud-based, onsite, or specific software solutions designed to annotate data ready for machine learning. A cloud-based data annotation tool is a tool created on a cloud platform. Objects are stored in the digital server so that data is secure.
An on-premise data annotation tool is an application that resides on a company’s premises. It is preferred because it provides data security, instant responses to an issue, and better performance. These tools tend to require licenses, and their implementation and maintenance costs may be high.
There are three ways to create an annotation tool from scratch. You could buy one or lease them, make one yourself, or you could use a third-party service. Whatever you choose, spend some time analyzing your project needs before making this decision.
Essential Features that a Data Annotation Tool Should Have
There are a few crucial features that you should look out for when choosing a data annotation tool. Some of these include:
Quality Control of Data
If you want high-quality results from your machine learning and artificial intelligence models, you need high-quality training data. However, there’s no way to ensure that your training data has been annotated correctly without using an automated QC tool.
Staff Management
Though an annotation tool automates aspects of data entry, most require human input when dealing with exceptions and quality control. The Data Annotation tool that you choose must have strong staff management capabilities.
Security
Whenever working with a sensitive PPI or IP, you’ll want to ensure it’s protected from unauthorized access.
Management of Datasets
An important aspect of annotation is defining a comprehensive workflow for managing the data you want to annotate – this includes importing, searching, sorting, filtering, and merging.
Annotation
Data annotation tools usually offer one or more kinds of annotations, including building and managing ontologies, labels, classes, attributes, specific annotation types, and/or auto-labeling.
The best data annotation tools will provide all of the above features. If you don’t find what you need in a particular tool, you might consider switching to another tool.
Conclusion
Data Annotation allows you to specify rules about data format. These annotations are typically attached to classes, properties, methods, etc., but can also be applied to individual elements within the class hierarchy.
In short, Data Annotations allow you to add metadata to your code, which helps developers understand how the data is used by the system. If you have any questions or anything else don’t hesitate to leave a reply in the comment section.
The word ‘blockchain’ is more and more in use today, especially when it comes to cryptocurrencies. However, the applications of blockchain go beyond cryptocurrency networks. Applications of blockchain technology today aren’t restricted to the financial industry either.
This is as the distributed ledgers found in blockchain technology can be used across industries. Blockchain databases are distributed, as well as transparent and secure. Various users within a blockchain network will have access to copies of the database.
These users can also be considered as nodes. These nodes then work together with other nodes to ensure that changes are made to the database, when necessary. As a result, the process is made both transparent as well as secure. Businesses have found different ways of using blockchain technology applications,
The distributed and transparent nature of blockchain technology can be used across various industries. Companies make use of blockchain technology in different ways. Here are real words examples of blockchain technology being used in different industries:
1. Managing Supply Chains
You can make use of blockchain development tools to manage supply chains. It’s becoming more and more essential for companies to be able to move in a fast and efficient manner. There is a need for goods to be transported between locations as fast as possible. Goods may even need to be transferred to other parts of the world.
This is as there has been an increased demand for various goods, across the world. If there is a break in this supply chain, as witnessed during the Covid-19 pandemic, then this will result in a shortage. It’s necessary for supply management tools to both be fast as well as efficient. One way this can be done is by utilizing blockchain technology.
With the help of blockchain technology, it’s possible to trace what the trajectory of a project is. Businesses can trace a product from the start to when the product reaches the home of a customer. Blockchain technology can also be used to reduce delays, prevent added costs from piling up, as well as limit human error. Risk related to fraud is also mitigated, as there is no need for any central intermediary, when blockchain technology is used.
2. Developing a Voting System
There is a growing use of blockchain technology, for purposes that fall outside the traditional purview of this technology. The voting system of a country needs to be strong in order to ensure that the voting process goes ahead without a hitch. Electronic voting has turned out to be more and more popular. However, despite the many benefits of electronic voting, such a voting system can still be vulnerable to both attacks as well as breakdowns.
In order for voting infrastructure to be considered secure, it must be free from attacks, as well as transparent. This way, every user can play a role in verifying how legitimate the voting process is. Blockchain can play an important role in improving the voting process.
When blockchain technology is used, the database for voting can be found on a ‘chain’, which can also support tens of thousands of nodes at the same time. Blockchain technology also has great encryption as well as decentralization technology. The databases for voting using blockchain technology can also be difficult if not impossible to corrupt. Every single record of voting can also be verified easily. It’s also important to note that in such cases, the voting network cannot be affected by intermediaries or by third parties.
3. Loyalty Rewards in Retail
Applying blockchain technology outside the purview of traditional use for blockchain, has showcased how useful and versatile this technology is. For businesses, retaining customers is becoming increasingly important. The applications of blockchain technology extend to loyalty rewards in the retail sector as well. If a business wants to retain customers, then loyalty rewards become important.
With the help of blockchain technology, the structure related to loyalty rewards can be more accessible as well as easier to use. In the contemporary market, cryptocurrency can be seen as a valuable reward. It provides businesses with the power to leverage blockchain technology to get transactions speeds that are faster, through digital wallets. A customer’s rewards can be stored in these wallets in an environment that is both safe as well as immutable. There is no need to worry about centralized authorities that will need to approve the rewards. As a result, rewards can also be handed out instantaneously.
4. Protecting Ownership and Copyright
It’s becoming increasingly important to take ownership of different kinds of data, such as data related to music, videos, paintings, and more. It is becoming necessary to protect artists from entities that might try to claim ownership of work that doesn’t belong to them.
The verification systems used by third parties such as Meta and Google aren’t foolproof either. As a result, blockchain technology can be used to solve the problem. Digital data related to copyright can be stored in a blockchain. This will be both transparent as well as secure. Third parties will be restricted from claiming ownership as well. This is as they will need to prove ownership on the same blockchain first, which becomes difficult to do.
Technologies that are adjacent, such as non-fungible tokens, make use of digital certificates to provide ownership that is immutable. Artists can enjoy profiting from their work even if that work has changed hands many times.
Conclusion
The applications of blockchain technology go beyond what it was traditionally created for. Use this guide to learn more about the different applications of blockchain technology.
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