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Category: Analytics

What are the Big Data Use Cases That Explains Digital Transformation?

Posted on by Imarticus Learning

The business world is transforming. Big data is influencing business decisions, marketing optimizations, business predictions, and day-to-day operations. Big data is expected to reach $300 billion by 2023. Though the term ‘Digital Transformation’ is loosely used as a euphemism for the modernization of data infrastructure, the true meaning of the word is becoming vital for the business world.

big data analytics courses in IndiaIt is true that the organizations are increasingly becoming data-centric, that they allot nearly 40 percent of their technology budget for digital transformation.

Data analysis courses facilitate faster, better, and smarter business decisions helping to achieve better operational efficiency, building a loyal client base, and leading to higher profit. Let us have a look at some use cases that explain this transformation.

Banking

Banks need to deal with a massive amount of financial information. Big data is used in banking to structure the collected data, organize them based on the business needs and analyze the data to derive insights on spending patterns, storing historical data like the transactions, payment records, etc. It also helps in identifying fraudulent actions and ensuring preventive measures are in place to block such frauds.

Another area of big data application is to prevent identity thefts. The banks use big data to keep a tab on customer actions. For example, if a customer follows regular spending patterns using the credit card such as groceries and general shopping, and all of a sudden there is a change in the behavior, such as spending money in gas stations and convenience stores from multiple locations across the city, the bank suspects something unusual. In this case, the bank may try to call the customer and find out if the card is stolen.

Online Shopping

Imagine you visited an online store and added some products to your cart. On second thought, you abandoned the cart and signed out without making any purchase. After some time, upon casual browsing, you see an advertisement for the same product you had planned to purchase.

The next day, you receive a mail with a discount offer for the same item. Targeted advertising is another application of big data. Online stores watch every movement of their customer from the moment they enter the store. They keep track of the pages you visited, the product you intended to buy by adding to the cart, etc. They use all this data to show targeted ads with irresistible offers that finally nudge you to purchase them.

Travel and Logistics

Big data facilitates the smooth running of logistics and transportation. It helps in scheduling flights, to show the booking status and seat availability, seasonal fluctuation in booking, etc. It also collects data regarding weather across the globe to predict flight delays. Big data is also used to analyze user trends and to predict the number of airplanes needed to cater to the increasing number of passengers. It could also be used to perform competitor analysis.

big data analytics courses in IndiaGiven above are some use cases that explain the application of big data in various industries. Apart from this, education, healthcare, digital advertising, environmental studies, human rights, etc. are some prominent areas that use big data to streamline their actions.

If you aspire to start a career in big data, this is the perfect time to enroll in a big data analyst course and secure a lucrative career.

Credit Underwriting: Robotic Process Automation Challenges And Solution

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Robotic process automation (RPA) has been creating a buzz for a while in the marketing and financial sectors. In simple terms, it can be defined as a technology based on software where you can find robots emulating human behavior while executing business processes. The software robots perform the same tasks that any human would do while working on an operating system to do his/her job.

But, you may wonder what purpose could RPA have in credit underwriting and the loan process? Well, you would be surprised to know that RPA training is a major player in the loan and credit sector. This is due to RPA solving many of the challenges faced by banks and financial institutions while sanctioning loans.

The Challenges

Credit underwriting is basically the procedure through which financial institutions judge giving credit or a loan to someone after reviewing the risks involved. However, the manual process of underwriting loans is long and tedious. There are many changing regulations, forms that need to be filled, and requirements that have to be met, along with an enormous amount of paperwork.

With so much red tape, a person or business in need of immediate monetary assistance will not be able to get a loan in time. Even when someone meets all the requirements, a simple factual error in the whole process might lead the bank to ask you to apply again.

This can be irritating, to say the least. Employees of the bank who do the paperwork for the customer also spend a large part of their production time performing this time-consuming process. The opportunity cost of this could be really high in the long run as those same employees could have been somewhere more helpful. And, this is where robotic process automation comes into the picture.

The Solution

Using robotic process automation can be very effective in dealing with credit underwriting. There are several solutions and long-term effects that RPA offers in this section:

  • Firstly, it automates the entire process. This includes data entry jobs, task assignments, and risk compliance, among others.
  • Loan and credit requirements are constantly changing. Automating the system can help customers get the updated regulations.
  • RPA also dramatically reduces the time taken to process a loan from the bank. If you have the proper documents and you meet the requirements, then you will get the loan immediately. So, no more waiting around for months to have your loan sanctioned!
  • Tailoring RPA to suit the needs of your customer is also possible. This helps build a better loan portal for countless applicants.
  • The data and certificates from customers are also stored securely within a database in RPA. That way, one can easily access the said documents whenever required.
  • Accuracy in data collection is also very high in RPA. Automation eliminates the chances of almost all the factual errors that are made when a person records them.
  • RPA can be an important tool in maintaining compliance with loan requirements, erasing corruption within the process. As a result, there is a much greater possibility of fraud detection while running RPA.

To sum it up, RPA is an important tool and will be even more important in the finance sector with each passing day. Many aspirants who want to have careers after graduation in the credit risk and underwriting sector often look for a certificate course in banking and finance.

For those thinking what after B.Com or M.Com, do not fret. The answer to this question of what after B.Com is going for a reputable certificate course in banking and finance. Pursuing such courses can lead to some great careers after graduation!

How A Security Camera Vendor Exposed User Data While Performing Analytics?

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A company responsible for manufacturing security cameras recently cost a huge number of its users their privacy as well as personal data which was accidentally exposed onto the internet. After admitting and owning up to their mistake, they went on to explain exactly what went wrong.

According to the company data belonging to users like their email address, body metrics, and IDs belonging to their WiFi networks were left exposed while critical information like financial information and the user’s passwords were left unaffected. This took place in December of 2019.

This incident occurred as a result of a misconfiguration in the database of Elasticsearch. This contained data that was generated by millions of the company’s customers, which they blamed on one of their employees. After explaining the issue, the company promised and was determined to keep looking into the database’s lack of proper security standards.

For those wondering what Elasticsearch is, it is a scalable analytics and search engine that is both open-sourced and includes full-text searches. It allows its users to analyze, look for and store large volumes of data efficiently and in real-time.

How exactly did the incident take place?

During an internal project which was meant to determine ways to measure metrics related to businesses in a more efficient manner, the incident took place. The method was to be used to measure activations of various devices, failed connections, and so on. This, however, would require data replications from the main or original production servers and into a database that would be more flexible.

This would make it easier to query. Bigger volumes of queries may possibly impact the customer’s product experience if it is done on the central database as it is very compute-intensive. Processing without there being any system lag required an individual data subset to be moved into a database that was different.

In doing so the user data was exposed in an unencrypted manner while it was being transferred to a novel database for querying. An employee had cleared out all prior made security protocols due to an error that led to the unfortunate incident. The new database only held a data subset and did not include any government-regulated or vital data such as passwords and financial intel.

What was exposed to the web exactly?

Until now, the company has only owned up to exposing the data which has been revealed and denies any evidence of data breaches. The company informed the public about the customer data that had been exposed which included data from their artificial intelligence assistants like Alexa, data pertaining to their health, their email addresses, and IDs to the networks of their Wifi’s.

Users have been asked to be careful against possible phishing ambushes because hackers could possibly have access to their user names and email addresses. These could still potentially be used to thieve credit and financial information.

The company also claimed that there was no evidence of exposure to their tokens required to log in. Users, however, have been logged out of their accounts as a precautionary or safety measure, allowing new tokens to be generated for logging in. The company has been working towards ramping up its security. This may include the addition of a two-factor method for user authentication. For aspirants looking to know more about the world of data science, a data analytics course would be perfect.

best Data Analytics courses in India Not only would allow aspirants to learn more about the field but it would also equip them with all the necessary information and tools required to succeed in a data analytics career.

Data Analytics and Other Skills Which Can Boost Banking Careers

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Data Analytics and Other Skills Which Can Boost Banking Careers

We are living in a fast-paced world that is constantly changing with the advent of new and progressive technology. What’s relevant today might be of no use in the near future, such as the frequency and intensity of change. In the contemporary scenario, the digital world has taken over the real one and the world is moving online.

This totally changes the equation for businesses that were earlier accustomed to traditional methods. Now it possesses another challenge for the employees who were trained as per the traditional methods.

This evolution and challenges also apply to the banking industry. With the advent and proliferation of e-commerce, more people like to purchase goods and services using an online method that requires a digital banking facility to make payments. Also, the digital banking system is an evolved version of traditional banking that tackles the challenges faced by traditional banking methods. It makes it more convenient for people to avail and use banking services from the comfort of their home.

An important observation about the digital space is that it runs on data. Tons and tons of data are generated and recorded from various data points. Most of these data are related to customer transactions that help the business to gain valuable insights about the market and the customers.

Earlier this goldmine was left untapped as businesses didn’t have the tools to explore the benefits of data. This has changed over the past decade and businesses have realized the latent potential of data science, data analytics is indispensable for businesses in almost all industries, especially banking.

Banking and underwriting course can help you easily break into the banking industry. Also, wealth management training online can help you obtain a comprehensive understanding of the industry and give you an edge over the competition in finance and banking-related jobs.

Let’s explore some of the most prominent data analytics skills that can boost the banking career for people aspiring to make a career in this industry.

Skills needed in the contemporary banking industry

  • Analytical Skills

In today’s data-driven world, analytical skills top the list of must-have skills for the banking sector. Data analytics skill plays a key role when it comes to analyzing large chunks of data and deriving valuable insights from it. You should be able to identify trends and patterns hidden in the data sets and derive a conclusion from the same.

You should also develop your technical analytics skills by learning software such as Python, excel, etc. Your decisions should not be based on a hunch and it should be backed by reliable data. Data-driven thinking along with strong analytical skills is the only way forward when it comes to excelling in the banking and related sectors.

  • Commercial Awareness

Commercial awareness is all about being up to date with the latest trends and developments in the business world, especially for businesses in the banking and finance sector. The ability to anticipate a financial trend distinguishes a highly successful banker from a mediocre one.

You have to keep up with the market and be able to predict future movements. It’s vital to understand how organizations function and the primary driver for their growth. You need to know everything that might have a financial impact on a business.

  • Stress Management

Technical skills are important but what’s even more important is your soft skill. Emotional intelligence is an interesting concept that emphasizes the need to manage your emotions in the workplace. When you are working in the banking industry, you will be exposed to a lot of uncertainty that might induce and cause stress.

Stress management is critical to succeeding in the banking world. Burnout and stress from work are far too common and can reduce your productivity drastically. Being able to manage stress is one of the most important skills that you can hone.

How does Data Analytics help to Detect, Assess and Prevent Fraud in 2021?

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Fraud is a major problem in today’s digital world. Whether it is data analytics or data sciences, data analytics has become an integral component of fraud prevention. Data analytics is the process of data manipulation to identify patterns and predict trends based on data sets. The data can be in various formats like text, audio, video, images, or even data generated by sensors.

data analytics coursesIn simple words, data analytics is a data-driven approach to help you make better decisions. It helps organizations detect, assess, and prevent fraud in 2021. Data analytics courses are the latest trend among MBA students because data is so important to every aspect of life today.

This blog post will discuss how data analytics can help us detect, assess, and prevent fraud in 2021!

How does Data analytics help to detect, assess and prevent fraud in 2021?

In today’s data-driven world, data analytics has become an integral part of financial services. While data is not new to the industry, collecting and processing it at scale did pose a number of challenges in past years. However, with advancements in technology and data storage capabilities, data analytics as a tool for fraud detection has gained prominence within banks, especially after the 2008 economic meltdown.

Nowadays, organizations are generating large amounts of structured/unstructured data from various sources like customer service calls, social media platforms, etc., which can be analyzed using tools like machine learning, artificial intelligence (AI), predictive modeling, or deep learning algorithms, among others to predict potential threats on their business operations including risk management strategy thereby reducing operational costs & improving profitability by increasing revenue streams through better data-driven decisions.

However, data analytics is not just about data storage and analysis of unstructured data to identify anomalies within the data sets; it also focuses on methods that can be used by organizations to prevent frauds in real-time – a key area where financial institutions are looking at for growth opportunities as demand from millennial customers increases for mobile banking and increased online transactions via e-commerce portals among other channels.

In order to support their digital transformation journey & build a robust risk management strategy, banks and other institutions have started investing heavily into analytics capabilities by hiring data scientists and data analysts to leverage the power of data for fraud detection in real-time.

According to a recent report by MarketsandMarkets, the global data analytics market was valued at $16.06 billion in 2016 is expected to reach $73.47 billion by 2021, growing at a CAGR of 28% during the forecast period from 2017-2021.

Some key areas where data analytics is being used extensively include: 

Customer profiling– understanding customer behavior patterns using unstructured data sets such as social media posts or email communication logs;

Fraud Detection– monitoring transactions on data sources across data systems and data formats to identify suspicious activity;

Risk Assessment– showing the correlations among data sets of different entities within an organization or between organizations.

This is a great opportunity for anyone who wants to learn data analytics, as there are plenty of job opportunities for data specialists.

Learn data analytics with Imarticus Learning

Imarticus Learning offers data analytics courses in India that help professionals from diverse backgrounds understand concepts of data analysis. This course enables you to analyze large amounts of data using various methods such as statistical tools, pattern recognition, etc.

data analytics courses

Thus, you’ll be able to solve complex problems related to web intelligence, predictive modeling & simulation techniques or extract information quickly with minimal time lag through the Business Analytics course.

For more insight, connect with us through the Live Chat Support system or visit any of our training centers based in – Chennai, Mumbai, Thane, Pune, Bengaluru Hyderabad, Delhi, and Gurgaon.

Analytics & Sports – How Data Analysts Be Asking in The Glory in The Sports

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Sports have seen their fair share of analysts and pundits ever since the first game was played. Traditionally, the analysis made by these pundits and analysts is based on hunches rather than concrete mathematical and statistical foundations. Even if they tried to factor in the statistics, there is no way that a human brain would calculate the entire complex, behind the scene calculation, which goes into a typical mathematical or statistical model.

So, it was thought that data analytics in the sector of sports had reached saturation. It all changed with Machine Learning and Artificial Intelligence.

In case you are not aware, machine learning and artificial intelligence are the fields that employ complex statistical models with a colossal amount of data to analyze the data, derive insights, and make predictions from it. So, when these advanced modeling techniques are fed the data generated from sports, we derive more accurate results, wisdom, and forecasts than all the pundits and legacy sports analysts.

Let us now see precisely what makes data analysis so lucrative in this sector.

How is sports analytics changing the paradigm?

There are only two ways in which sports analysis is being used in today’s paradigm. One is when the sports teams ask for data analytics, and the second is when it is used for sports betting.

data analytics certification course in IndiaThe first step of the Data Analytics course in sports involves mining various data like stats of any individual player, match conditions, and the teams’ recent performances. Then, all the information is passed down to Data Analysts.

It is their job to run machine learning models on this data to generate new insights. Now, these insights could be as simple as whether the team would win the next match or not.

To something more specific like, will Ronaldo be able to score his third goal with his left foot in the 69th minute?

Analytics is not limited to in-game events. By analysis of the data generated by the fans, the team would be able to cater to their needs better and thus improve their following. The sports team can also use data analysis to enhance merch sales.

How are big teams using data analytics?

While there are many examples available out there, we choose the ones from football.

Manchester United: We all have seen the Red Devils wear the AON branded shirts for a long time now. ManU has been using AON’s data analysis to stay ahead of the curve.

Real Madrid: This might come as a surprise to many, but Real Madrid has partnered with Microsoft (the Azure platform owner) to provide the Madridistas with all the analytics they need. They use Microsoft services to plan out the training, performance, and even fitness regimes of their players.

Conclusion:

There are a lot of apparent benefits when it comes to using data analytics training in the paradigm of sports. We have barely touched the surface here. However, we hope that we were able to provide you with some new information about data analysis in sports today.

Explainable AI: Escaping The Black Box of AI and Machine Learning

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With the introduction of machine learning, the vertices of Artificial Intelligence (AI) developed manifold and established their presence across multiple industries. Machine learning helps understand an entity and its behaviors through interpretations and detections of patterns. It has endless potential. But its difficulty is in forming a decision in the first place through a machine learning algorithm.

artificial intelligence and machine learning coursesThere are often concerns about the reliability of machine learning models because of the questions about processes adopted to arrive at an anonymous decision. AI and Machine learning courses help in comprehending extensive data through intelligent insights.

It is useful in applications like weather forecasting, fraud detection, etc. But there is a crucial requirement to understand the processes of ML because it can form decisions using insufficient, wrong, or biased information.

This is where Explainable AI comes into the picture. It is the bridge between the ML Black Box and AI. Experienced AI is a model that explains the logic, goals, and responsible decisive process behind a result to make it understandable to humans.

As per reports by Science Direct, certain models of AI developed early in the process were easy to interpret since they had a certain amount of observability and clarity in their processes. However, with the advent of complicated decision systems like Deep Neutral Network (DNN), the process has become more difficult.

The success of DNN models is a result of productive ML models and their parametric space. It comprises muliple parameters that result in making DNN a black-box model too complicated for users. The search for an understanding of how this mechanism works is at the other end of the black-box model.

A machine learning course makes the process a lot easier. As the need for transparency is rising, the information utilized in ML is no longer justifiable, as it does not provide any detailed explanations for their behavior. Explainable AI along with ML helps in addressing the partial innate of AI. These biases are detrimental in industries like healthcare, law, and recruitment.

Explainable AI consists of three basic core concepts, which are:

  1. Inspection
  2. Accurate predictions
  3. Traceability

Accurate predictions refer to the process of explanation of models about the results and conclusions reached that enhance decision understanding, and trust from users. The traceability factors help humans to intervene in the decision-making of AI and control their functioning in case of need. Because of these features, explainable AI is becoming more and more important these days. A machine learning career is on the rise. In recent predictions from Forrester, it was reported that 45% of AI decision-makers find trusting an AI system is very demanding.

To assist developers to understand ML and explainable AI in detail, IBM researchers open-sourced AI Explainability 360. Google also announced an advanced explainable AI tool. The field of explainable AI is growing. And with it, it will bring enhanced explainability, mitigation of biases, and greater results for every industry.

How Artificial Intelligence Training Can Be Helpful For Any Voice Content Purpose?

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Introduction

The technological change and digitization that is taking place across the globe are being accelerated by the day. Growth in Innovations and Inventions in all the major domains is Multi-fold. This major chunk of this growth can be attributed to the advances that are being taken in Artificial Intelligence. Artificial Intelligence had contributed to almost all the major domains in some or another way.

Artificial Intelligence as a domain is very comprehensive in both depth and breadth. Some of the major domains that comprise artificial intelligence are Neural Networks, Fuzzy Logic, Expert Systems, Natural Language Processing, Robotics, etc., In these domains, one of the fastest-growing domains is Natural Language Processing.

Natural Language Processing

The ability to understand and read human language through artificial intelligence is Natural language processing (NLP). NLP can be classified in terms of Text, Voice, and Audio. In all three, voice recognition is the fastest growing and widely known through various voice assistants in our smartphones.

Not only on the consumer side but also there are many applications on the enterprise and industry domains. Voice recognition/speech recognition is used for many purposes such as customer insights, in meetings, Security features, etc.,

Speech Recognition and its multiple applications:

Speech recognition is the Comprehension of the voice by the Computer to perform a task. It involves various modules such as speech to text, text to speech, Speech processing, and many more. Speech recognition can be found in many applications such as

  • Car Navigation
  • Navigation and Control such as GPS
  • Voice Assistants

Considering the advantages let’s consider how Artificial Intelligence Training in Speech Recognition/Voice recognition will help us to pursue a career in Artificial Intelligence.

 Artificial Intelligence Training

To Pursue natural language processing, there must be a holistic knowledge of Artificial Intelligence. Artificial Intelligence is one of the most important skills for any high-paying job. To have a holistic knowledge of artificial intelligence some key topics are to be concentrated upon such as

  • Python and Statistics
  • Data Analysis and Python
  • Machine Learning
  • Deep learning
  • Natural Language Processing
  • Computer Vision

To have a successful career in Artificial Intelligence there should be a basic understanding of python, TensorFlow, and other similar technologies. Though not all the domains are equally important, if an individual prefers to have a career in natural language processing then knowing other domains listed above will complement.

Skills Required to become an NLP Expert

Skills like Fundamentals in Machine Learning such as regression, probability estimation, data integration can be learned through training in Artificial Intelligence. In addition to this, there are additional skills that help an individual to become an NLP Expert. Skills such as parts of speech, Sentence Fragmentation, Information Extraction are needed from the linguistics point of view.

When seen from the programming side, NLP research scientists should be well equipped with at least one of python, java, ruby as a known language. Also, knowledge of probability & statistics and Recursive Neural Networking are essential for NLP. Artificial Intelligence Training will develop all these necessary skills

Prospects of Artificial Intelligence and Natural language processing

Rapid Growth in the technology space had made way for many new opportunities. Artificial Intelligence is one such field where there are umpteen opportunities in multiple domains. There are many High paying, exciting jobs that are available when Artificial Intelligence skills are possessed. Especially in Natural Language processing, the job prospects are surging. The job profiles that are offered for a career in Artificial Intelligence and Natural Language Processing are

  • Machine Learning Engineer
  • Natural Language Processing- machine learning Developer
  • Data Scientist
  • Software Engineer
  • Research Scientist
  • ML Specialist

Conclusion

Artificial Intelligence is a growing field and the opportunities that it offers are ever-increasing. In artificial intelligence, Natural Language Processing (NLP) is an essential part of high job prospects. A wholistic Artificial Intelligence Training and NLP will help an individual in pursuing a great career in Artificial Intelligence.

Artificial Intelligence is Modernizing Restaurant Industry

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John McCarthy coined the term ‘Artificial Intelligence’ more than 6 decades ago. We have been dreaming of a technology-enabled future for decades and it is turning into reality now. Artificial Intelligence is the programming of machines to imitate human intelligence and their actions. It helps in minimizing human efforts along with improving speed, accuracy, and effectiveness.

Today, there are huge numbers of data created in almost every sector. There is a need for Advanced Algorithms, huge data management, storage, analysis, and several other complex tasks. Artificial Intelligence and machine learning training is gaining popularity due to this increasing need for accuracy, precision, and effectiveness.

It’s interesting how Artificial Intelligence has grown over a small period in several sectors. Education, Transportation, Sports, Media, Marketing, Banking and Finance, and so on goes the list of industries that use Artificial Intelligence.

How Artificial Intelligence Helps in the Restaurant industry

Below are a few examples of the application of Artificial Intelligence in the Restaurant Industry.

Forecast Sales. People indeed make orders based on a specific climate. You would not go for a hot cup of cocoa on a hot humid day. You would go for something that is cooler. The climate makes an impact on the food and beverages sold by an eatery. AI with Machine Learning helps the restaurant to project their sales based on the orders made, current weather conditions, and past sales performance.

Scheduling Capabilities. Predictive Scheduling is fundamental these days. Staffs need to be informed in advance of their schedules, whom to be switched with, shifts, etc. Doing this manually can get quite tiresome for the manager. Artificial Intelligence provides help with this kind of function. It can perform a lot of scheduling tasks in an efficient time that would normally take up a lot of human effort and time.

Smart Virtual Assistant. Virtual Assistants and related technologies have been gradually growing over the past few years. It gathers various information related to the restaurant and presents it to the user when they search about it. Based on the information customers pick a suitable restaurant. Therefore they play a direct role in the growth and sales of the restaurant.

Robots. Is it possible? Yes, definitely. It won’t be a while before we may actually be delivered food from robots. A restaurant chain in Pasadena named Caliburger was one of the firsts to bring in a robot to their kitchen. Though it is yet to come into effect in India, it could take the restaurant sector to a whole new level of functioning.

AI-Driven Kiosks reduce waiting time and improve the eatery experience for customers. These systems act as POS systems and make the ordering process simpler for the customers.

Inventory and Purchasing. Pos systems integrated with Inventory and Purchasing can improve efficiency and helps in reducing losses. It helps to track and order based on sales, menus, and recipes. One can cross-verify between the actual food preparation and the portion of food left, thereby helping to find out sources of waste and theft in your kitchen. AI-powered restaurant management solutions ensure the right supply of materials and help to remove one more tedious work from your list.

Mapping And Management Tools. Delivery is gaining popularity these days. It is important to optimize the delivery routes for quick, efficient, and quality food delivery. Management Tools helps to keep a check on the drivers’ progress, guide on routes and on how to save miles and time, and keeping the customers in the journey throughout.

Self Serve Options powered by Artificial Intelligence could work wonders in the Restaurant Sector. Though it has not come into effect in India, self-serve kiosks could speed up the services without actually sacrificing the human touch. Customers can browse through menus, make orders, and make payments online without having to stand in long queues. This helps in improving customer satisfaction.

Artificial Intelligence is changing our lives in more ways than we know in this 21st century. So is Artificial Intelligence helping to reshape the Restaurant Industry. The AI-powered tech advancements these days are critical in boosting sales and increasing customer satisfaction to a much higher level. Since most individuals have already embraced digital technologies, it would only make it smarter and easier to inculcate Artificial Intelligence in the Restaurant Industry.

Data Analytics in Healthcare: Can a Techie Succeed in The World of Medicine?

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In the modern-day, Information Technology has seeped into all sectors. Industries have adopted Data Science and Analytics to drive their work and have reaped the rewards for it. In the paradigm where Data Analysts are highly sought after in all sectors, how do they fare in the field of Healthcare?

 

Big Data Analytics courses are the perfect way for techies to break into the medical sector. It has become a tool with unlimited potential, and a Data Analytics career in healthcare is a very real and prospective opportunity.

Data Analytics is generally used to draw meaningful interpretations, find trends and predict possible outcomes from Data.

Healthcare Analytics specifically aids in avoiding preventable diseases, conducting an accurate diagnosis, predicting and combating epidemics and coming up with effective treatment strategies for diseases.

A Healthcare Analyst contributes to improving the quality of healthcare and reducing the treatment costs by automating tedious processes.

Applications of Data Analytics in Healthcare

There are many applications of Data Analytics in healthcare, and a career in the field works to implement these uses. Some of the most popular applications are:

Patients’ prediction: Healthcare Analytics can be leveraged to improve patient predictions. The past admission records can be used to discover hospitalization trends, peak times and deploy personnel accordingly.

Strategic Planning: The existing data can be used to identify patterns, conduct studies and map out strategies for extensive care and in community medicine.

Electronic Health Records:

It is one of the most popular uses of Data Analytics in Healthcare. It is used to track patient medications, treatments, progress, and medical history. Data Analytics on these can reveal more details about their medical conditions and lead to more accurate prognoses.

Predictive Analytics: Predictive Analytics is the practice of recognizing patterns and predicting probable outcomes by studying the data presently available. This can be used to improve care delivery for patients with complex medical history by using history to try and predetermine the conditions they may face in the future.

Data Analytics Careers in Healthcare

The applications of Big Data analytics in medicine are vast, and more are being uncovered every day. In this scenario, techies are being welcomed into the sector with open arms. Healthcare employs techies as Data Analysts, Informatics Consultants, Clinical Data Managers in the Quality and Performance improvement sector.

One can also get promoted to leadership positions for other Analysts as Informatics Director or Chief Medical Information Officers. All these positions come with their responsibilities as well as perks. The roles and responsibilities would also entail assistance in the proper integration of data analytics within specific healthcare areas.

Conclusion

A career as a healthcare analyst is a door that opens infinite possibilities. Not only do you get to save lives as a part of the field, but you also get to innovate and change medicine in your way.