Businesses have long been using artificial intelligence (AI) for marketing purposes. However, with the recent advancements in AI technology, businesses are now able to do much more with marketing AI than ever before.  

Here are ten ways marketing AI can supercharge your business:

1. Automated Customer Segmentation 

One of the most powerful ways to use marketing AI is for automated customer segmentation. Segmenting your customers helps you to better understand their needs and target them with more relevant messages. 

With AI, you can automatically segment your customers based on their behavior, demographics, and other factors. It can save you enormous time and effort that you would otherwise spend on manually segmenting your customers.  

2. Predictive Analytics

Another powerful way marketing AI can be used is for predictive analytics. Predictive analytics can help you to identify trends and patterns in customer behavior. This information can then be used to make better decisions about future marketing campaigns.

3. Automated Lead Generation          

Lead generation is a vital element of any marketing campaign. AI can be used to automate the lead generation process by collecting data from various sources and then identifying potential leads. 

It can save plenty of time and effort that you would otherwise spend on manually generating leads.

4. Automated Email Marketing        

Email marketing is, without a doubt, a very important marketing channel. With AI, you can automate your email marketing campaigns. It can include sending personalized emails to customers based on their behavior and interests.  

5. Social Media Monitoring     

Social media is a terrific medium to connect with your customers and promote your brand. However, it can be time-consuming to manually monitor all of your social media channels.   

AI can be used to automate the procedure of monitoring social media. It can include identifying relevant conversations, analyzing Sentiment, and more.

6. Personalized Recommendations        

Personalized recommendations are a great way to improve the customer experience and boost sales. With AI, you can automatically generate customized recommendations for products and services.

It can be based on factors such as customer behavior, demographics, and interests. 

7. Chatbots       

Chatbots are a great way to provide customer support and promote your business. AI-powered chatbots can automate the process of responding to customer queries. 

It can save you a lot of time and effort that you would otherwise spend on customer support.

8. Measurement and optimization

You can use marketing AI for measurement and optimization. AI can help you to track the performance of your marketing campaigns. This information can then be used to improve future campaigns.   

9. Fraud detection 

Fraudulent activity is a major problem for businesses. AI can detect fraudulent behavior, such as fake reviews, click fraud, and more.

It can help you to protect your business from potential losses. 

10. Market research 

Market research is an integral part of any marketing strategy. AI can be used to automate the practice of conducting market research. It can include collecting data, analyzing it, and generating insights.


You can use marketing AI in various ways to supercharge your business. Automated customer segmentation, predictive analytics, automated lead generation, and automated email marketing are just some of the ways you can use marketing AI.

AI is one of the hottest topics in business today. But while many organizations are eager to adopt AI, they’re often unsure of where to start, what obstacles they may face, and the way to ensure their AI initiative is successful.

This article will explore 8 of the most common challenges businesses face when implementing AI and offer advice on overcoming them.

1. Defining The Business Problem

Before you can implement AI, you need first to identify a business problem that it can help solve. But many businesses are unsure what AI can do for them or where it could add the most value. 

It’s crucial to ease up, take a step back, re-look at your business goals, and overcome this obstacle. Once you clearly understand what you’re trying to achieve, you can start brainstorming AI use cases that could help you get there.    

2. Getting Buy-In From Stakeholders   

Once you’ve identified a problem that AI could help with, you need to get buy-in from key stakeholders. This can be a challenge, as many people are still unsure about AI and how it works. 

Once they understand the potential benefits, it will be easier to onboard them with your AI initiative.

3. Finding The Correct Data    

AI requires data to learn and function properly. But finding the correct data can be a challenge, as it needs to be accurate, timely, and relevant. Additionally, obtaining data that meets these criteria can be challenging, as it may be scattered across different departments and systems.

You need to implement a good data management strategy to overcome this. This will help you collect, clean, and organize your data, so that AI applications can use it. 

4. Building The Right Team

Implementing AI requires a team of skilled professionals with the right skills and experience. But building such a team can be a challenge, as AI is still a relatively new field, and there is a talent shortage. 

To overcome this challenge, you need to invest in training and education for your existing team members. Additionally, you may need to hire new team members with the right skills and experience. 

5. Implementing AI Applications  

Once you have the right team at your disposal, you need to implement AI applications. But this can be a challenge, as there are many different types of AI applications, and each has its unique implementation process.

To overcome this challenge, you need to carefully select the AI applications that are right for your business and then follow the implementation process for each one. 

6. Managing Expectations  

AI is often hyped up as a silver bullet that can solve all of your business problems. But this is not the reality, and setting unrealistic expectations can lead to disappointment and frustration.

To overcome this challenge, you need to manage expectations by setting realistic goals and objectives for your AI initiative. Additionally, it would help if you educated stakeholders on what AI can and cannot do to understand the technology entirely. 

7. Handling Failures  

AI initiatives sometimes fail, and when they do, it can be not easy to know where to turn. AI is still a new and evolving field, and there is often no clear path to success. 

To overcome this challenge, you need to have a plan to deal with failures. This plan should include a way to track progress so that you can identify early warning signs of trouble. Additionally, you need to have a way to quickly recover from failures to keep your AI initiative on track. 

8. Measuring Success  

It can be hard to accurately set the standard for the success of an AI initiative, as there are often many factors involved. Additionally, AI is often used to solve complex problems, making it hard to isolate the impact of the AI solution. 

To overcome this challenge, you need to set clear goals and objectives for your AI initiative. Additionally, you need to put a system to track progress and measure success. This system should include both quantitative and qualitative measures.


Implementing AI in your business can be a challenge, but it is possible to overcome it if you have a good plan and the right team. Additionally, you need to set realistic expectations, manage failures, and measure success. You can ensure that your AI initiative is successful by taking these steps. 

The payments industry is in the midst of a significant transition. The traditional players are under pressure from new entrants taking advantage of advances in technology to offer more innovative and convenient payment solutions. This is particularly apparent in mobile payments, where startups are developing new ways to use smartphones and other mobile devices to make payments, which is only one such example. 

FinTech, which is shorthand for financial technology, encompasses a wide range of new technologies used to create innovative financial products and services.

Below, we will look at the most pressing trends in FinTech and explore how they are likely to impact the payments industry in the years ahead. 

1. Mobile Payments 

As we mentioned, mobile payments are one of the most critical trends in FinTech right now. Using smartphones to make payments has proliferated worldwide.

There are several reasons for this. First, mobile devices are becoming more and more commonplace. Today, more people use smartphones than bank accounts. This means that there is a vast potential market for mobile payment solutions. 

Second, mobile payments are convenient. They allow users to make payments without carrying around cash or cards. This is particularly important in developed markets, where people make payments with their smartphones for everyday purchases. 

Third, mobile payment solutions are becoming more and more sophisticated. For example, the use of NFC (near-field communication) technology makes it possible for users to make payments by simply waving their smartphone near a contactless payment terminal.

2. P2P Payments 

Another significant trend in FinTech is the growth of P2P (peer-to-peer) payments. P2P payments are made between two individuals without the need for a third party such as a bank or payment processor.   

The most popular P2P payment solution is PayPal, allowing users to send and receive payments using their email addresses. Other popular P2P payment solutions include Venmo and Square Cash.  

P2P payments are growing in popularity because they are convenient and often accessible. They also offer an alternative to traditional banking products, such as credit cards and wire transfers.

3. Blockchain 

Blockchain is a fascinating technology that underlies Bitcoin and other cryptocurrencies. It is a distributed database that allows for secure, transparent, and tamper-proof transactions.

The use of blockchain technology is increasing in some different industries, including banking, supply chain management, and healthcare. This is because blockchain offers some advantages over traditional database systems. 

For example, blockchain-based systems are more secure because they are decentralized. This means that there is no central point of failure that hackers can exploit. 

Blockchain also has the potential to reduce the costs of financial transactions significantly. This eliminates the need for intermediaries such as banks and payment processors.

4. Artificial Intelligence 

Artificial intelligence (AI) is another area of FinTech that is growing in importance. AI refers to computers to perform tasks that typically require human intelligence, such as understanding natural language and recognizing patterns. 

AI is being used in many different ways in the financial sector. For example, it is being used to create chatbots that can provide customer support and help customers with managing their finances. 

AI is also being used to create predictive analytics tools that can be used to identify trends and make investment decisions. 

5. Biometrics 

Biometrics refers to the usage of physical or behavioral characteristics to identify individuals. The most common biometrics used for identification purposes are fingerprints and iris scans.

It is being used increasingly in the financial sector to improve security. For example, many banks are now using biometric authentication for customers accessing their accounts online or through mobile apps. 

Biometrics is also being used to create digital identity systems. These systems allow individuals to prove their identity online without physical documents.


Only a few of the most pressing trends listed above are currently shaping the future of payments. As the world of FinTech continues to evolve, we can expect to see even more innovative payment solutions that will make it easier and more convenient for us to make and receive payments.

In finance, artificial intelligence (AI) technology is used to create and interpret complex financial data, identify trends and make predictions. AI-based applications can help financial institutions automate tasks and processes, improve decision-making and reduce costs.

Applications Of AI In Finance 

There are several AI applications in the financial sector, including:

Automated financial advice: Also known as “Robo-advisors,” these AI-powered applications can provide personalized investment recommendations based on an individual’s risk tolerance and goals.

Fraud detection: AI can help financial institutions identify fraudulent activity, such as money laundering, credit card fraud, and insurance fraud.

Financial analysis: AI can analyze financial data and identify trends, helping businesses make better-informed decisions.

Algorithmic trading: AI can be used to automate the buy and sell orders of securities on financial markets. 

Personal finance management: AI-based applications can help individuals manage their finances by providing insights and recommendations on spending, saving, and investing.

How To Use AI In Finance

There are a few ways that financial institutions can start using AI-based applications:

1. Use “off-the-shelf” AI solutions: Several AI software platforms and applications can be adopted and implemented quickly and easily.

2. Partner with an AI provider: Working with an AI provider can help financial institutions quickly and efficiently start with AI.

3. Build an in-house AI team: Build an in-house team of data scientists and engineers for businesses that want more control over their AI solutions. However, this option requires more time and resources.

4. Invest in AI startups: Another way to get involved with AI is to invest in early-stage AI startups. This can help financial institutions gain access to new AI technologies and applications.

Benefits Of Using AI In Finance 

There are several advantages or benefits of using AI in the financial sector, including:

Improved decision-making: AI can help businesses make better-informed decisions by providing insights and recommendations based on data.

Increased efficiency: AI-powered applications can automate tasks and processes, freeing employees to focus on other tasks.

Reduced costs: Automating tasks and processes with AI can help businesses save money on labor costs.

Improved customer service: AI can provide personalized and proactive customer service, leading to happier customers.

Risks Of Using AI In Finance

As with anything, there are also some risks associated with using AI in the financial sector, including:

Job loss: Automating tasks and processes with AI can lead to job losses as businesses require few employees.

Financial crimes: AI can be used for financial crimes, such as money laundering and fraud.

Bias and errors: AI systems can be biased if they are not trained on a diverse data set. This means they can make errors or mistakes if they are not calibrated correctly.

Data privacy: AI-based applications rely on data, which means there is a risk that personal data could be leaked or stolen.

How To Choose An AI Solution For Finance

When choosing an AI solution for finance, there are a few things to consider, including:

1. The type of AI technology: Several types of AI technology, including machine learning, natural language processing, and computer vision. It’s important to choose a solution that uses the correct type of AI technology for the task at hand.

2. The data set: The data set is the foundation of any AI system. It’s vital to ensure that the data set is high quality and diverse.

3. The use case: It’s essential to choose a well-suited solution for the specific use case. For example, if the goal is to automate tasks, a solution that uses machine learning would be a good choice.

4. The provider: It’s essential to choose a reputable and experienced AI provider. A good provider will offer support and guidance throughout the implementation process.

5. The cost: It varies for an AI solution, depending on the provider, the features, and the size of the data set. It’s essential to compare costs before making a decision.


AI is a powerful tool that can be used in several ways to improve the efficiency and effectiveness of financial institutions. When choosing an AI solution, it’s important to consider the type of AI technology, the data set, the use case, the provider, and the cost.

The healthcare business is about to undergo a significant upheaval. Rapid technological breakthroughs are transforming how care is provided and received, and the industry is beginning to realize the possibility of even more dramatic changes in the years ahead.

In particular, three major trends are poised to reshape healthcare as we know it:

The rise of artificial intelligence (AI), the proliferation of wearable devices, and the increasing use of 3D printing. Here’s a more in-depth analysis of each of these directions and how they’ll affect healthcare in the future. 

Artificial Intelligence in Healthcare

Artificial intelligence is already beginning to make its mark on healthcare, with applications that range from diagnosis and treatment to administrative tasks. 

For example, IBM Watson is being used by doctors and hospitals to help with everything from cancer diagnosis to finding the best course of treatment for a patient. AI is also being used to develop new drugs and create personalized medicine.

In the future, AI will become even more ubiquitous in healthcare, taking on even more complex tasks such as surgery and diagnosis. Additionally, AI will likely play a role in preventative care, helping to identify health risks before they become serious problems.

Wearable Devices in Healthcare 

Wearable devices are another trend that is starting to transform healthcare. These devices, which can be worn on the body or even implanted, allow for constant monitoring of a person’s health.

For example, there are now wearable devices that can monitor heart rate, blood pressure, and other vital signs. This data can then be sent to a doctor or hospital in real-time, allowing for more immediate treatment if necessary. 

Soon we can expect to see even more sophisticated wearable devices that are able to monitor a wider range of health metrics. These devices will become increasingly small and comfortable to wear, making them unobtrusive and easy to use on a daily basis.

3D Printing in Healthcare 

3D printing is another transformative technology that is starting to make its way into healthcare. 3D printing is also used to create prosthetics, implants, and even human tissue.

This technology is already being used to create custom-made prosthetics for patients who have lost limbs. Soon we can expect to see even more amazing applications of 3D printing in healthcare, such as the ability to print organs for transplant. 

The Changing Norms 

The future of healthcare is poised to be transformed by the rise of artificial intelligence, the proliferation of wearable devices, and the increasing use of 3D printing. These technologies will change the way care is delivered and received, making it more personalized, efficient, and effective.

The healthcare businesses are on the verge of major changes, thanks in large part to advances in technology. From new treatments and cures to more efficient ways of delivering care, the future of healthcare looks very different than it does today. Here are just rare ways that technology will shape the future of healthcare:

New treatments and cures: With technology becoming more advanced, we are able to develop new treatments and cures for diseases that were once thought incurable. In addition, we can now create personalized treatments based on a patient’s specific genetic makeup.

More efficient care: Technology is also helping us to deliver care more efficiently. For example, telemedicine allows doctors to consult with patients remotely, which can save time and money.

Improved patient outcomes: By leveraging technology, we can also improve patient outcomes. For example, electronic health records allow for better coordination of care, which can lead to fewer complications and better overall health for patients.

Lower costs: Technology can also help to lower the costs of healthcare. For example, by automating tasks and using data analytics, we can reduce the need for manual labour, saving money in the process.

Greater access to care: Technology can also help to increase access to care, especially in rural and underserved areas. By using telemedicine, we can con1nect patients with doctors regardless of location.

The healthcare industry is transforming quickly, and technology is playing a major role in that change. By staying abreast of the latest developments, we can ensure that we’re providing the best possible care for our patients.

As we move further into the 21st century, software development is constantly evolving. Businesses must stay up-to-date with the latest market research trends to stay ahead of the competition.

This article will explore the top 5 software development market research trends for 2022. Remember that these are only trends, and they may not all come to fruition. Nevertheless, it is crucial to know about these trends to be prepared for whatever the future may hold.

  1. The Rise Of Artificial Intelligence (Ai)

One of the most significant market research trends for 2022 is the continued rise of artificial intelligence (AI).

AI has the potential to revolutionize the software development industry by automating many tasks that are currently performed manually. This could lead to increased efficiency and productivity and reduced costs.

AI is also expected to impact how we live and work significantly. For example, businesses already use AI-based chatbots to communicate with customers and provide support. In the future, AI could automate customer service, sales, and marketing.

Moreover, AI will play a substantial role in the future of healthcare. AI-based systems are already being used to diagnose and treat diseases. Later, AI could personalize treatment plans, provide early disease diagnosis, and even predict epidemics.

  1. The Growth Of Cloud Computing

The growth of cloud computing is also expected to impact the software development market significantly.

Cloud computing is an internet-based computing type that allows users to access and use applications and services hosted on remote servers. This lets the businesses save money on hardware and software costs and reduce their IT infrastructure.

The cloud is also expected to play a significant role in developing new applications and services. For example, the cloud can create and test new applications quickly and easily. It can also provide services such as storage, computing power, and software.

  1. The Rise of DevOps

A third trend that is expected to have a major impact on the software development market is the rise of DevOps.

DevOps refers to tools and protocols used to automate the software development process, including the development, testing, and deployment of applications.

DevOps can help businesses improve their speed and quality of delivery and reduce their costs. In addition, DevOps can help to strengthen the collaboration between developers and operations staff.

  1. The Growth Of Mobile Computing

Another significant trend that is expected to impact the software development market is the continued growth of mobile computing.

Mobile devices are becoming increasingly popular, and they are being used for various tasks such as email, browsing, and social networking.

As a result, businesses are increasingly developing mobile applications to provide their customers with a better experience. In addition, companies are also using mobile devices to create new applications and services.

  1. The Rise Of Big Data

A final trend that is expected to impact the software development market is the continued rise of big data.

In simple terms, big data describes large volumes of data generated by businesses and organizations. This data can be used to improve decision-making, as well as to create new products and services.

Big data is also expected to play a significant role in developing new applications and services. For example, big data can create predictive analytics applications that can identify patterns and trends.


The software development market is expected to experience significant growth in the coming years. This growth is being driven by several trends, including the continued rise of artificial intelligence (AI), the significant growth of cloud and mobile computing, and the rise of DevOps. In addition, big data is expected to play an essential role in developing new applications and services.

The software development industry is constantly changing and evolving, thanks to the many innovative minds constantly developing new and better ways to create software.

Here are ten of the most impressive innovations that are currently changing the industry

1. Big data and analytics

This is a big one. With the ever-growing popularity of big data, it was only a matter of time before software development began to use it. Big data and analytics are being used to help developers create better, more efficient software by understanding how users interact with it.

2. Cloud computing

Cloud computing is another big trend that has a major impact on software development. Cloud-based applications are becoming more and more popular, and developers need to be able to create software that is compatible with this model. Thanks to cloud computing, developers now have access to massive amounts of computing power and storage, making it feasible to construct much more complex applications.

3. Mobile development

With the advent of smartphones and tablets, mobile development has become a huge growth area in the software industry. Developers need to be able to create applications that will run on various devices and operating systems, and they also need to make sure that their applications are user-friendly and efficient.

4. The Internet of Things

IoT is another big trend that is currently changing the software development landscape. The IoT refers to the growing network of physical devices connected to the internet, and it is having a major impact on how software is developed. Developers need to be able to create applications that can take advantage of this growing network, and they also need to make sure that their applications are secure and reliable.

5. Augmented reality

Augmented reality is another hot trend that is starting to make its way into software development. This is the process of adding digital elements to the real world, and it has a lot of potential for use in various applications. Developers need to be able to create software that can take advantage of augmented reality, and they also need to make sure that their applications are user-friendly and efficient.

6. 3D printing

3D printing is another technology that is starting to have a major impact on the software development industry. This is the process of creating three-dimensional objects by printing them layer by layer. 3D printing has a lot of potential for use in various applications, and developers need to be able to create software that can take advantage of it.

7. Automated testing

Automated testing is one of the most important tools developers have at their disposal. This is the process of using software to test other software, and it is essential for ensuring the quality of applications. Automated testing is becoming more and more famous, and developers need to be able to take advantage of it.

8. Big data management

Big data management is another important area that developers need to be aware of. This is the process of managing and processing large amounts of data, and it is becoming more and more important as big data becomes more popular. Developers need to be able to create software that can take advantage of big data management, and they also need to make sure that their applications are secure and reliable.

9. DevOps

DevOps is another hot topic in the software development world. This is the process of integrating development and operations, and it is becoming more and more popular as organizations strive to improve their efficiency. Developers need to be able to work with DevOps tools and processes, and they also need to ensure that their applications are compatible with this model.

10. Blockchain

Blockchain is a new technology that is starting to make its way into the software development world. This is a distributed database that permits secure and transparent transactions, and it has a lot of potential for use in various applications. Developers need to be able to create software that can take advantage of blockchain technology, and they also need to make sure that their applications are secure and reliable.

These are just a few of the many trends currently shaping the software development landscape. Developers need to be aware of these high trends and how they can impact their work. They also need to make sure that they are keeping up with the latest developments to stay ahead of the curve.

Artificial Intelligence (AI) is getting increasingly important in our modern world. AI techniques find applications in robotics, face recognition, machine translation, and self-driving cars. This is the best time to learn about AI software development! 

This article will introduce the basics of AI software development by presenting five common challenges for people just learning the ropes. Each section will cover the challenge, why it’s a problem and how you can start overcoming it today! 

  1. Not Knowing Where To Start 

Most people interested in learning about AI software development find the field too vast and intimidating. The best approach is to take your first step! If you’re looking for a good first project, try building an X-Men character classifier bot. This simple text classification bot can classify mutants as good or evil. It’s an excellent way to learn about text classification, natural language processing, and even reinforcement learning!

  1. Knowing What To Study But Not Knowing How

One of the most significant challenges in AI development is deciding where to start. Many APIs, libraries, frameworks, and tools are great for making excellent products but can be a nightmare for learning. Because of this, it’s essential to have a good plan on what you want to learn and how you’re going to do it. Try using something like the AI Planning Framework as a starting point for your projects!

  1. Handling Data Collection And Cleaning

One of the most time-consuming steps in AI development is gathering and cleaning your data. There are many datasets for classification tasks, but they can be hard to find through all the information available online. Try using ImageNet if you want to get started quickly with a dataset. 

  1. Using Libraries Correctly

Libraries are great resources for any developer to use when making applications. However, it’s important to remember that they are just tools meant for you to use! Always try to understand their underlying logic to learn something from them.

  1. Being Afraid Of Making Mistakes During Development

As with any new skill or technology, AI software development can be tricky at first. It’s important to know that mistakes are the best ways to learn! Try not to be hopeless or frustrated when you receive error messages left and right, your code isn’t working, or even if your dataset was complete junk. If anything, this means there is more for you to do and learn in AI software development! 


The list only contains a few of the common challenges that beginner AI developers face. Fortunately, overcoming them is easy if you’re willing to put in the time and effort needed to become an expert! The goal of this article was to present five common roadblocks that many AI developers run into when first starting. Each section presented the challenge explained why it’s a problem and how you can overcome it!

It’s becoming increasingly evident that artificial intelligence has great potential when it comes to the future of software development.

This holds in nearly every aspect, from the development process to robot hardware used in devices and machines.

But, there are some very important considerations when it comes to how these two things relate to each other.

Software Development vs. Artificial Intelligence:

The relationship between artificial intelligence and software development is a complicated one.

The reason for this is that both fields are constantly advancing – thus, it’s difficult to say whether the two terms are linked because of their similarities or despite their differences.

This difficulty makes it hard to determine what the future will hold for each and if they’ll eventually merge into the same field. However, some very important differences give us a good idea of where each is headed in the short and long term.

What’s The Difference?

The process followed in both fields is largely similar, especially at the beginning stages. This is logical – after all, a piece of software is a form of artificial intelligence. 

However, as the process continues and the code becomes more complex, the differences become apparent.

One difference that stands out between software development and artificial intelligence is that AI is largely focused on creating technology that has human-like capabilities – from self-driving cars to machines with seemingly infinite knowledge.

However, software development is focused more on creating technology that works for humans rather than with them. 

A remarkable sample of this can be seen in Facebook’s chatbots – they may have some smart features, but they’re not trying to replace you as a person.

The future of these two fields depends largely on the projects developers and AI researchers take on. If this continues as it has been, the two will likely merge into something that is simply called “technology.”

The Reality of the Relationship:

One really important thing to consider in this area is that software developers and AI experts don’t always talk in the same terms.

For example, suppose someone in the AI field discusses a robot’s ability to “understand” something. In that case, they may be talking about its problem-solving capabilities, such as how it will transit a road without getting hit by traffic.

On the other hand, if they’re talking about an AI’s “understanding” something in terms of language and semantics, this may not mean anything to someone more familiar with software development and programming.

It’s very important for those who work with AI to understand what those who work with software development are talking about.

Beyond the Development Process:

As mentioned before, there is more to this than just how programming and development fit into things.

For example, even though software firms may outsource some tasks to firms specializing in artificial intelligence, they may not always be as efficient as those developed and programmed by hand.

However, this isn’t to say that AI cannot make the process more efficient; rather, it depends on what they’re doing and how much time and other resources must be used to accomplish their task.

There’s also the fact that artificial intelligence is better equipped than software developers because it can think and solve problems more quickly.

However, this takes training and programming to be done correctly (though most AI systems are becoming smarter with time).

So, the concise answer to whether or not they’re related is: yes.

It’s important for those who work with AI to understand what those who work with software development are talking about and vice versa.

There’s also the fact that both fields stand to benefit from each other in various ways, such as how artificial intelligence can help make software testing more efficient.

But as with any symbiotic relationship, it’s important for those involved to interact and collaborate. Otherwise, they’re not benefitting each other as much as they could be.

Data is present everywhere and can be found through many different sources.

After the concept of Big Data came into existence, all that was required to generate it was a click or tap on a screen, and voila, terabytes worth of data could be generated just by having one-time access to the internet.

Information is available in abundance today. From grocery stores to smartphones, data is generated at a large scale and in many different formats.

This led to the generation of a new buzzword called Big Data which refers to vast pools of information that might be structured or unstructured, raw or refined, in any form whatsoever.

Big Data in healthcare involves analyzing data at a mega-scale to establish patterns, derive insights, and predict outcomes. Big Data has evolved since Doug Laney first coined it in the last decade.

He defined three-dimensional characteristics of Big Data in his paper called ‘Integrated Information Theory. According to him, these characteristics are Volume, Variety, and Velocity.

Big Data present in healthcare covers a wide range of sources, including but not limited to Electronic Health Records (EHRs), Electronic Medical Records (EMRs), data from genomics, health care provider information, clinical trials, and research.


Data can be generated anywhere by anyone at any time. Doctors do a lot of data collection in the medical field by prescribing tests and procedures to patients that involve medical devices, blood tests, medications, etc.

These medical records are then transferred to different healthcare providers like clinics, hospitals, etc. And finally, they end up at an EHR, an electronic system for storing and using health information.

The analysis of these enormous pools of information is known as Big Data analytics, which needs to be performed with high accuracy and speed.


There are many types of data generated every day by healthcare providers, organizations, new technologies, etc.

These different sources produce different types of data that must be adequately managed. Big Data usually contains multiple sources that can be structured, unstructured or semi-structured.


Not all data is created equal. Some types of data need to be analyzed immediately to make quick medical decisions, for instance, data from diagnostic equipment that needs to be dealt with immediately.

This variety of data requires a different format for its extraction and storage, which has to be correctly handled by healthcare organizations if they want to carry out the analysis successfully.

On the other hand, certain information requires time before being analyzed and used for deeper insights.


Today’s Big Data Analytics techniques have evolved over the years by incorporating algorithms that perform real-time analysis and help make quick decisions.

‘Real-time’ refers to the elapsed time between data acquisition and information usage that takes no longer than a few seconds. This process is called ‘Streaming Analytics.’

Shortly, healthcare providers will make medical decisions using these patient profiles and providing personalized care.