The world’s most successful companies set their focus on customer satisfaction. The reason being that customers leave organizations where they are not satisfied with the service.
New products with unique and improved features will continue to pop up in the market. Still, the customer would rather continue doing business with companies that serviced them well over time. This is why companies have to pay apt attention to customer loyalty and advocacy.
Data Science in Improving Customer Satisfaction
The advent of new technologies and the utilization of data science methods on huge amounts of data makes it easier for companies to place laser focus on the factors that cement customer loyalty for their products.
Companies across the world now invest time and money in data science, analytics, and statistical testing. Data scientists help businesses navigate their way through the vast ocean of data available to them in a bid to make the right, timely business decisions.
How B2C & B2B Companies Use Data Differently
Data analytics is a source of valuable insights that can inform how both B2c and B2B companies make decisions about products, marketing, and sales. Though they each have a unique set of challenges, B2c and B2B businesses both collect, visualize, and analyze their most valuable asset – customer data.
Both B2B and B2C companies use data analytics to unlock new pathways to increase customers, more profits, and better decision-making. But they access these pathways in totally different ways. So let’s go over the differences between how B2B and B2C companies use data.
B2C businesses often have shorter sales cycles, with a large part of their revenue coming from advertisements. This implies that the customers need to be engaged for longer and the sales cycle optimized. Leveraging data on the customer’s experience in making a purchase can help point decision-makers in the right direction.
B2B companies, on the other hand, have much longer sales cycles. Here, the goal is to minimize the amount of time the customer spends making a purchase. Using data science, the company can improve efficiency and shorten the sales cycle. Data scientists can analyze sales data for insight into improvements in customer experience.
Since B2C companies typically have more customers than their B2B counterparts, there is usually no shortage of data to analyze. This allows data scientists to analyze several different customer data points related to their experience with the business. Data scientists can use customer data to segment customers accurately and outline better user personas to guide product and marketing initiatives.
B2B’s fewer customers present both an advantage and disadvantage. Fewer customers equal fewer customer data for analysis. Conversely, fewer customers make it easy for B2B companies to develop meaningful relationships with their customers. Data scientists can leverage data from real-world customer feedback to develop and inform their product and marketing strategies.
Data Science: Transforming Data into Value
A 2018 study by MIT Sloan found that 59% of businesses use data analytics to gain a competitive advantage, and this figure is up from previous years. This shows a shift towards a more data-based approach to customer service, and more companies are utilizing analytics to get closer to customers.
The enormous value in data science and analysis is becoming more evident by the day. Which begs the question, what exactly are the benefits of data science to a business?
1. Mitigating Risk and Fraud
Data scientists typically have advanced training in statistics, math, and computer science. This training helps them identify data that stands out. Then, they create statistical processes that can predict the propensity of fraud and alert the data scientist in a timely fashion when any unusual data is found.
2. Helping Management make Better Decisions
Competent upper management setups like to have an experienced data scientist as an advisor to maximize their analytics capabilities. A data scientist analyzes, communicates, and demonstrates the real-world implication of company data, thus facilitating better decision-making across the entire company.
Data scientists track and record key performance metrics then recommend actions that will help the business level up its performance, improve customer engagement and increase revenue.
3. Defining Target Audiences
Most companies have some sort of data collected, from customer surveys to Google Analytics, but if this data cannot be used to identify demographics, its purpose is defeated. Data science is all about being able to take existing data that, on its own, is practically useless and combine it with other information to reveal insights about customers.
Data scientists, by thoroughly analyzing separate sources of data, can precisely identify key groups of customers. The company can then use this in-depth knowledge to tailor its products and services to customer groups.
4. Recruiting Talent
Data science provides a major solution to one of the most monotonous tasks in the life of a recruiter– having to go through resumes. Data science experts can leverage the vast amount of information available on potential employees; social media, recruitment websites, corporate databases, and so on.
Using this information, they can sort out which candidates best fit the company’s requirements. As a result, data science can help your company make faster and more accurate hires.
5. Case Studies of How Bad Data can affect your business
Bad data refers to inaccurate data. Bad data could be missing key elements, fraudulent, irrelevant for the purposes it is intended for, duplicated, poorly compiled, and so on.
Research shows that bad data costs businesses large swathes of their revenue. For example, Gartner found that the average cost of poor data quality on businesses is between $9.7 million and $14.2 million annually.
IBM estimated the yearly cost of poor quality data in the US alone was $3.1 trillion in 2016. In other words, bad data is bad for business. However, how much damage can bad data do to a business? Let’s examine real-life examples of the adverse effects of bad data.
The 2001 Enron Scandal
In the early 2000s, Enron Corporation reached dramatic heights only to face a dizzying fall. Wall Street giant’s fall from grace due to fraudulent financial data affected thousands of employees and shook Wall Street to its core. Unfortunately, the data that was being provided to shareholders was entirely made up.
So much so that towards the end, auditors began to shred documents to cover their tracks. But it was too late. Enron’s executives and their auditing firm delivered fictional data to stockholders and the Board of Directors in annual reports and financial statements for years.
Independent ethical auditing of the data Enron put forward could have prevented a financial fraud of this magnitude from occurring.
Lead (Tetraethyllead) in Gasoline in the 1920s.
In the 1920s, Tetraethyllead was commonly added to gasoline on the premise that it could control knocking in engines. This unfortunately contributed to over 5,000 deaths in the US alone from lead exposure.
The leaded gas industry and the US government at the time conducted and made use of data from inconclusive tests to ratify this decision.
The lead paint and leaded gas industries traded the blame for the sudden rise in fatalities from lead poisoning for decades, each claiming their products were safe for people. If an independent analysis of these industries’ bad data and the government had relied upon it was conducted, perhaps so many lives would not have been lost.
How Data Science can Improve Customer Experience
Organizations around the world now approach the issue of customer service and experience from a data-driven standpoint. Customer service has evolved to meet the needs of customers in the digital era.
Customers have unique needs and expectations for their customer service experience. They don’t want to be asked the same questions over and again. They don’t want to be kept on hold for hours, or talk to a bot, or be transferred from one person to another.
The more issues a customer has to face, the more their frustration builds up, and effective communication between the customer service agent and the customer is hindered.
Data science comes in handy at this juncture by providing deeper insight into what a customer wants. With data analytics, machine learning, and artificial intelligence, companies can now meet customer needs, resulting in a powerful improvement to the customer experience. Let us explore how businesses can use data analysis to improve customer service.
- Collecting and Using Customer Data
Many businesses use multiple customer service platforms that allow customers to communicate via different mediums such as phone calls, emails, and live chat. This creates multiple streams of data that now need to be integrated. Without bringing these different sources together, you only get an incomplete picture of your customers.
Data science collects and integrates data across multiple communication channels, painting a complete image of the customer. For example, integrating your data can tell you what products a particular customer purchased in the past, what mode of communication they prefer when they are most likely to respond, and many other details that come together to improve the overall customer experience.
- Improving Agent Productivity
Customer service agents that are productive create happier customers, and a happy customer is a buying customer. Data analysis and reporting can be used in-house to score agents’ performance and see which agents perform best and in which areas.
This allows your company to know the best agent to touch with your customer and measure the agent’s skill progression concerning their career goals and company requirements.
- Acquiring and Retaining Customers
The probability of selling to an existing customer is between 60% and 70%, while the probability of selling to a new customer is 5-20%. Data science can help you audit your sales and marketing strategies by telling you which strategies are most successful with new customers and which work best for existing customers.
A competent data scientist on your team will enable you to reach for the top of both probabilities as you put the customer’s needs first. In addition, continually using data science to manage your customer service strategy will help you decide what should stay and what needs to be changed.
- Setting Your Company Aside From The Competition
The majority of businesses want their customers to think first of them before the competition for several reasons. It could be that your products and services are cheaper, or they are of higher quality, or because you offer a superior customer experience.
Data science helps companies pinpoint what features customers love about their products and services so they can focus on them. In doing this, your company can outpace its competition and strengthen customer loyalty.
- Improving Products and Services
Data science is one of the most powerful tools companies use to understand the position of their products and services in the market. This understanding is essential to staying relevant to both customers and competition. In addition, data science helps businesses find when and where their products and services sell best.
With data science in their corner, companies can deliver the right products that meet their customers’ needs at the right time.
Data analysis will show you how your products and services help people improve their lives and how they use these products to solve problems in their daily lives. Through this, your company can identify areas for improvement and birth ideas for new features.
We live in an age where data is every company’s greatest asset. Data science has the potential to bring tremendous value to your business, boost customer satisfaction, and in the long run, increase ROI.
All you have to do to unlock your data’s full potential is to take advantage of the several data analysis tools available and invest in the services of an experienced data scientist.
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How to Manage Your Time if You’re Going to College
A college schedule is not as rigid as a high school schedule. In high school, administrators plan nearly every moment of every day. Sure, it’s possible to do whatever you want, whenever you want. But, it would help if you didn’t overlook the consequences this presents. And, most notably, managing your time effectively.
Most of you have probably never given much thought to time management. Yet, without it, college would be more challenging and less rewarding. For this reason, managing time is significant for all college students. And, if you can master college time management in your first semester, you’ll be unstoppable.
Also, college time management is a skill you can use for the rest of your life. For example, it can help you manage your future work and personal responsibilities — aka work-life balance.
For this reason, we have prepared a guide to help you effectively manage your time as you enter college.
1. Be aware of time-wasters and set goals.
Getting distracted is something we all experience from time to time. However, you should pay attention to what causes you to lose focus on your studies and assignments.
- Do you spend too much time on social media playing Fortnite?
- Are you constantly texting and answering personal calls while studying?
- Are you finding that you spend a lot of time aimlessly browsing the web?
Set a goal not to do anything that wastes your time during dedicated study time. Instead, use those activities as rewards for staying focused and completing your tasks.
2. Get ahead.
There will be more significant assignments in college, such as research papers. When you’re still in high school, experts recommend starting these larger projects well ahead of their due dates rather than cramming at the last minute. It is also essential to study for tests that are scheduled in advance.
If you’ve already graduated, make this a summer priority. An example would be getting a check-up before starting college. Before registering for classes, most incoming first-year students must provide a recent physical exam and vaccination history. Also, if living on campus, don’t wait to select your housing, meal plan, or purchase dorm essentials.
“If you have a research paper due in two weeks, find your research within the first two or three days, then work on reading it for the next four or five days, and then write the paper,” says Jodi Bahr, a science teacher at Harvard Middle School and Harvard High School in Nebraska – also known as Harvard Public School – and high school science teaching division director for the National Science Teaching Association.
3. Work straight from your calendar.
You don’t have to ghost your to-do list. To-do lists are great and all. But it would help if you tried working directly from your calendar instead.
Your mindset changes from a task-based to a time-based one when you work straight from your calendar. With a calendar, you can see when deadlines are and how much time you have left. Additionally, it can increase the sense of urgency for time-sensitive items to be completed first.
Using your calendar view, you can see when you have a lot of work due and when you have fewer things to do. And, if your social schedule needs to be adjusted for a due date, you’ll know immediately.
ISO of a calendar app? You have plenty to choose from. But, I would recommend Calendar, Google Calendar, and My Study Life.
4. Create a dedicated study time.
I already briefly mentioned this. However, it’s worth repeating.
Establish a time for studying or homework only. Don’t pick up the phone and respond to notifications until you’ve finished your work. In addition, avoid checking email or surfing the Web during this time — except when necessary.
If you can, this block of time should be when you feel most energetic and productive. So, if you’re a night owl, this would be later in the day or evening.
5. Leverage technology.
Technology makes college time management easier. For example, you can use time management apps to help you stay on top of things. Some of the best time management apps out there are Todoist, TimeTree, and the already mentioned Calendar.
As previously mentioned, even simple and existing tools like timers and calendars on your handheld device can be used to manage your time.
6. Separate work time from fun time.
There are times to work and times to have fun. So invest your time accordingly. While there are exceptions, like throwing a study pizza party with a classmate, it’s not always possible to combine working and having fun.
7. Only do one thing at a time.
Those who multitask have a drop in IQ like someone who didn’t sleep the night before, according to a University of London study. In other words, you’ll be less productive if you’re juggling multiple tasks and assignments.
When you feel tempted to multitask, ask yourself:
- What is your most common distraction?
- Is it possible to turn off the devices or applications?
If you can, turn off all devices. And never start another task until the one before it has been completed.
Breaking this habit may be challenging, but it’s worth the effort.
8. Always keep your eye on the prize.
To manage your time effectively, you need to be able to handle stressful situations effectively. For example, if you’re feeling a lot of pressure because of your school obligations, you can get overwhelmed. Even worse, you may forget which assignments to prioritize.
A simple way to avoid this is to keep your eye on the prize. But, first, don’t forget that college is only a temporary stopover. The knowledge and skills you’re gaining will be helpful one day in your career and personal life.
9. Ask for help.
Asking for help often takes the form of delegation, which is a method of time management. Imagine taking turns cleaning if you’re living with roommates, for example. If you do this, you will be able to focus on schoolwork (or social activities) a bit more.
You could also ask a classmate the hacks they use to stay on top of everything. And, if you’re really struggling, find out what resources are offered by your school. There are also plenty of free mental health resources for college students.
10. Don’t be afraid to say “no.”
If you have a test the following morning and your friend asks you to go to a movie one night, it’s okay to say no. You could instead plan a time that works for both of you to see the latest MCU movie.
Published First on Calendar. Read Here.
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Retirement Planning Tips for the Self-Employed
For the self-employed, retirement planning can be a bit more complicated than for the rest of the population. They don’t have the benefit of an employer-sponsored plan and an HR department where they’ll find counseling and advice on planning for their retirement years. However, that doesn’t mean the self-employed can’t have a comfortable retirement or that they should work until they drop. With a little bit of planning and creativity, self-employed individuals can save enough for a laid-back retirement full of fun, travel, and well-earned rest. Here are eleven tips to get you started.
Tip #1: Start saving for retirement as much and as early as possible
When we’re young, it’s hard to focus on long-term goals like retirement, and we tend to focus much more on our immediate needs. This includes things like buying a house or a car, taking care of student debt, paying monthly bills, and more. If you’re a passionate entrepreneur running your own business, even things like housing and a car may come second in terms of priorities; you usually focus all of your time and energy on business management and growth, so retirement planning falls way behind.
However, if you want a comfortable retirement, the best time to start saving was yesterday; the next best time is today. At this point, what matters is not how much you save for retirement every month or year (we’ll cover that in a moment). What really matters is to get started.
However, if you want to retire comfortably, you must start saving as much as possible as early as possible. The sooner you start contributing to a retirement account, the more time your money has to grow through compound interest.
Why does this matter?
Most people don’t grasp just how much of an impact starting to save one, two, or three years earlier can have on the size of your nest egg by the time you retire.
Let’s run some simple numbers. Suppose you put $10,000 in a 401(k)when you’re 35. It will grow at a 5-8% interest rate. Taking the lower 5% as interest rate, by the time you reach retirement age, those $10,000 will have grown to $43,219. If you wait one year and deposit the money when you’re 36, after 29 years, your balance will be $41,161 instead. That’s $2,058 less you’ll have at your disposal for waiting just one year. Start saving when you turn 40, and you’ll end up with $33,864. That’s over $9,000 less, even though it’s the same $10,000 you started with.
Now imagine you don’t just save $10,000 in total, but save roughly that amount every year, which is what most people saving for retirement do. If you run the numbers, the difference can be tens of thousands of dollars for waiting a couple of years instead of starting to save right away.
Tip #2: Even if you’re your own boss, pay yourself a salary
Just because you don’t have an employer doesn’t mean you can’t pay yourself a salary. This is especially important if your business is doing well and you’re reinvesting most of the profits back into the company and forget to take some of them out as income. When it comes to retirement planning, you need to know how much you’re making every month so you can budget accordingly and set enough money aside for the future. The best way to ensure this is to pay yourself a salary.
How much should you pay yourself?
The answer to this question depends on several factors. The most important ones are:
- Your current expenses
- How well your business is doing
- The long-term financial goals for your business
- How much money you’ll need to live comfortably once you retire (more on this later).
Tip#3: Choose the right retirement account
When you’re employed by someone else, there’s a good chance your employer will offer you access to a 401(k) retirement account. If they don’t, other options are still available, like an IRA. For the self-employed, the options are a bit more limited, but there are still several retirement accounts you can choose from. The most common four are:
- One-participant 401(k): This is also known as a Solo 401(k), and it’s perfect for self-employed individuals or business owners with no employees. The contribution limit for 2022 is $20,500, but if you’re over 50, you can contribute an additional $6,500 as a catch-up contribution.
- Simplified Employee Pension Individual Retirement Account, or SEP-IRA: This account is another tax-deferred retirement account available to small business owners and the self-employed. The contribution limit in 2022 is the lesser of 25% of your net earnings from self-employment or $61,000.
- Savings Incentive Match Plan for Employees Individual Retirement Account, or SIMPLE IRA: This retirement account is available to small business owners with 100 or fewer employees. The contribution limit in 2022 is $14,000, but if you’re over 50, you can also contribute an additional $3,000 as a catch-up contribution to reach $17,000.
- Keogh plan: This account is also known as a qualified retirement plan, and it’s available to self-employed individuals or unincorporated businesses.
Each of these retirement accounts comes with its pros and cons, so you must do your research to find the best one for your specific situation. They all share one trait: they’re funded with pre-tax dollars, meaning you’ll be able to defer paying taxes on them until you retire.
However, if you expect to reach a higher income bracket as time passes, choosing a Roth IRA or a Roth 401(k) may be wiser. These accounts are funded with after-tax dollars, meaning you won’t get the tax break now but will when you retire and start withdrawing from the account.
After making your decision, though, the most important thing is to start contributing to one of these accounts as soon as possible.
Tip #4: Estimate how much you need to save for a comfortable lifestyle during retirement
When you start saving for retirement, what matters most is that you start early and save as much as possible without disrupting your short-term plans and lifestyle. But, eventually, you’ll want to begin crafting a real retirement plan. That means:
- Setting clear and ambitious yet achievable long-term goals and breaking them into smaller, more manageable ones.
- Drafting a clear strategy that’ll serve as a roadmap to achieve those goals
- Acting on that strategy and sticking to it as closely as possible
- Performing annual controls to see how far you have come, what you’ve accomplished, where you fell short, and what needs to change in the following year to get back on track or reach an even more ambitious goal.
When it comes to setting goals, these need to be specific and measurable. Therefore, you’ll have to define what you expect your retirement to be like so you can estimate how much income you’ll need to pay for that lifestyle without outliving your savings.
This estimate doesn’t have to be perfectly accurate, but rather an assessment that will help you see a ball-park figure of how much you should be saving every month from your income to enjoy the retirement you want.
Tip #5: Invest in a diversified mix of assets
When most people think about retirement, they picture themselves sitting on a beach sipping cocktails or playing golf. But to make that dream a reality, you need to have enough money to cover your living expenses for 20, 30, or even 40 years.
The previous tip was about determining how much your living expenses add up to. However, once you run the numbers, you’ll likely find that your current income isn’t enough to save the amount you need every month. If that’s the case, don’t despair. You can dramatically lower the money you’ll need to set aside every month if you manage to increase the return on your savings, even if only by a little.
This means investing your savings, not just leaving them to grow in a savings account. Regarding investing, stocks and bonds are the two most common asset classes. But there are also other options like real estate, mutual funds, exchange-traded funds (ETFs), and even NFTs and crypto trading. The key is to invest in a diversified mix of assets to minimize the risk of losing money while still giving yourself the chance to earn a higher return.
For example, let’s suppose you invest the same $10,000 as before when you’re 35, and you manage to get an average of 6% growth instead of 5%. In that case, instead of $43,219, you’ll have $57,435 when you retire. That’s a difference of over $14,000 for that extra 1% return, without saving a cent more than what you were saving in the first place!
Tip #6: Secure a minimum level of income
No matter how much money you have saved for retirement, it’s crucial to have a plan in place to ensure you’ll have a minimum income level every month. There are several ways to do this, but the most common is to purchase an annuity.
An annuity is a contract between you and an insurance company. In exchange for a lump-sum payment, the insurance company agrees to make regular payments to you for a set period of time or the rest of your life. There are different types of annuities, and you can customize contracts to your heart’s content by adding contract riders.
Annuities are a way to protect your nest egg and to make sure you have a minimum level of income every month, but they’re not without their drawbacks. For one, annuities are complex financial products, and it can be challenging to understand all the different features and benefits. Additionally, annuities come with fees and commissions that can eat into your investment returns, which is something you need to watch out for.
In any case, what matters most is that you set up a safety net you can fall back on in retirement, so you don’t have to worry about running out of money or outliving your savings.
Tip #7: Live a healthy lifestyle
No list of tips about preparing for old age would be complete without this important piece of advice. One of the best ways to reduce your medical expenses in retirement is to live a healthy lifestyle when you’re young. This means eating healthy food, exercising regularly, and getting regular check-ups. Of course, this isn’t always easy, but it’s worth it in the long run.
A healthy lifestyle will help you avoid costly medical bills down the road and help you feel better and enjoy your retirement more. After all, what’s the point of saving for retirement if you can’t enjoy it?
So make sure to take care of yourself now, and you’ll be thankful later.
The bottom line
There’s no one-size-fits-all answer regarding retirement planning, especially if you’re self-employed. However, following these tips should give you a good start. Remember to invest in a mix of assets, secure a minimum income level, and live a healthy lifestyle. And most importantly, don’t wait until the last minute to start planning and saving for your golden years.
Published First on Due. Read Here.
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Why is Artificial Intelligence Crucial for Biotechnology?
Biotechnology lies in the middle of biology and technology. Through modern technologies, it uses biological processes, organisms, cells, molecules, and systems to create new products for the benefit of humanity and the planet. In addition, it contains laboratory research and development through bioinformatics to explore and extract from biomass through biochemical engineering to develop high-value products. Biotechnology operates in various fields, such as agriculture, medical, animal, industrial, and others.
White biotechnology, related to creating products demanding chemical processes from biomass, can also be one of the solutions to the energy crisis by producing biofuel. The latter can be used for vehicles or heating.
Each organization working in the biotechnology sphere maintains voluminous sets of data stored in databases. This data must also be filtrated and analyzed to be valid and applicable. Such operations as drug manufacturing, chemical analysis, enzyme studies, and other biological processes should be backed by computerized solid tools for high performance and accuracy, as well as helps to reduce manual errors.
One of the most helpful technologies that help to manage the biological processes, drug production, supply chain, and deal with data within biotech is Artificial Intelligence.
It interacts with data received through scientific literature and clinical data trial. AI also manages incommensurable clinical trial datasets and enables virtual screening and analyze the high volume of data. As a result, it reduces clinical trial costs and results in discoveries and insights for any field in which biotech operates.
More predictable data makes it easier to build work processes and operations, enhances the speed of performance and the accuracy of the procedures, and makes decision-making more efficient. 79% claim that AI technology impacts workflows and becomes crucial to productivity.
All of these results are becoming more cost-effective solutions. The estimated revenue gained with the help of AI grew by $1.2 TN in the last three years.
Advantages of using artificial intelligence in biotechnology.
AI applies in various fields, but the most significant is the use of AI in medical care. Although such technology’s ability as data categorization and making predictive analyses are beneficial for any scientific sphere.
Managing and analyzing data
The scientific data is constantly expanding and has to be arranged in a meaningful way. This process is complicated and time-consuming: scientists must go through repetitive and heavy tasks, which must be performed with great attention.
The data they work with is a big part of the research process, which results in high cost and energy loss in case of failure. Moreover, many kinds of research don’t result in practical solutions, as they fail to be translated into human language. AI programs assist in the automation of data maintenance and analysis. Open source platforms empowered by artificial intelligence help reduce the repetitive, manual, and time-consuming duties lab workers have to perform, enabling them to focus on innovation-driven operations.
Gene modification, chemical compositions, pharmacologic investigations, and other critical informatics tasks are thoroughly examined for shorter and more reliable outcomes.
Effective data maintenance is indeed crucial to every scientific sector. However, the most significant advantage of AI is its ability to organize and systemize data into forms and make predictable outcomes.
Driving innovations in the medical sphere
Over the past ten years, we faced the urgent need for innovations in the manufacturing and deploying pharmaceuticals, industrial chemicals, food-grade chemicals, and other raw materials connected to biochemistry.
AI in Biotechnology is essential for fostering innovation throughout a drug’s or chemical compound’s lifecycle and in labs.
It assists in finding the right combination of chemicals through computing permutations and combinations of different compounds without manual lab testings. In addition, cloud computing makes the distribution of raw materials used within biotech more efficient.
In 2021 the research lab DeepMind developed the most comprehensive human protein map using AI. Proteins fulfill various tasks in the human organism – from building tissue to conquering diseases. Their molecular structure dictates their purpose, which can have thousands of iterations—knowing how protein folds help to understand its function so that scientists can figure out numerous biological processes, such as how the human body works or create new treatments and medicines.
Such platforms give access to data about discoveries for scientists all over the world.
The AI tools help decode data for uncovering the mechanisms of particular diseases in different regions and help make analytical models accurate for their geography. Before using AI, time-consuming and costly experiments were performed to determine the structure of the proteins. And now, about 180,000 protein structures made by the program are available through the Protein Data Bank for free to be used by scientists.
Machine Learning helps make lines diagnosis more accurate, using actual findings to enhance diagnostic tests. And the more tests are performed, the more precise results are generated.
AI is a great tool to enhance electronic health records with evidence-based medications and clinical decision support systems.
Artificial Intelligence is also frequently employed in genetic manipulation, radiology, customized medicine, medication management, and other fields. For example, according to the current study, AI improved breast cancer screening accuracy and efficiency compared to a standard breast radiologist. As well as another research claims that lung cancer can be spotted faster by neural networks than by trained radiologists. Another AI application is to detect diseases more accurately through X-rays, MRIs, and CT scans through AI-driven software.
Reduces time of research
New illnesses spread quickly across countries due to globalization. We witnessed it with COVID-2019; as a result, biotechnology has to speed up its production of necessary medications and vaccines to stand against such illnesses.
Artificial intelligence and machine learning maintain the process of detecting the proper compounds, assisting in their synthesis in labs, helping to analyze data for effectiveness, and supplying them to the market. The use of AI in biotech reduces the time in operations performance from 5-10 years to 2-3 years.
Boosting harvest production
Biotechnology is critical in genetically engineering plants to generate richer harvests. The role of AI-based technologies is increasing in studying crop characteristics, comparing qualities, and projecting realistic output. The agricultural biotech also uses robotics, a branch of artificial intelligence, for manufacturing, collecting, and other critical tasks.
By combining such data as weather forecasts, farming characteristics, and the accessibility of seeds, compost, and chemicals, AI aids in planning future patterns in material circulation.
AI in Industrial biotechnology
IoT and AI are widely used in producing vehicles, fuels, fibers, and chemicals. AI analyzes the data collected by IoT to transform it into valuable data for improving the production process and product quality by forecasting outcomes.
Computer simulations and AI come up with the expected molecular design. Strains are being produced through robotics and machine learning to test the accuracy of developing the desired molecule.
To sum up
Though this is just the start of using AI in biotech, many improvements can already be offered to various spheres. Moreover, the growing development of the software empowered by Artificial Intelligence in biotech demonstrates that it can be used for multiple processes, operations, and tactics to obtain a competitive advantage.
It can not only drive innovations but also be a valuable tool to reduce costs by making more accurate tests and predicting results without the actual performance of the experiments in the lab.
As well as find the future necessities of humanity in healthcare and agriculture, forecast potential losses, and make prognoses for companies where they should target their resources for more effective production and supply.
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