All Posts By

celbridgescience farfry

How Big Data Analytics is improving customer experience?

By | Blog

Today’s market is a buyer’s market where the customer is GOD. Every company is trying to be customer-centric by providing a rich customer experience throughout the customer journey. Each step in the customer journey generates large chunks of data with volume, variety, and velocity. This big data is being analyzed and processed to further the customer experience in every aspect.

To improve customer experience big data is analyzed to know more about the customer behavior, motivations, and touchpoints across the buying and retention process. Here are the following areas where big data analytics is improving customer experience.

1. Personalized Recommendations

With the help of big data analytics, companies can build solutions that provide a more personalized customer experience. Predictive analytics methods use historical customer data for analysis, based on that recommendation engines can be built which deliver personalized & accurate recommendations to the customer during the buying journey. This helps to reduce the bounce rate and increases customer engagement.

2. Security and Privacy

The increased flow of data has resulted in various privacy and security issues. Customers are now more aware of their data being kept secure. Big data is a boon but handling it poorly can increase data threats. Big data analytics solutions through AI and Machine Learning helps companies to analyze and classify cyber threats based on historical data and patterns and it helps to build predictive solutions for future security threats. It can be said if the data of your customer is secured, your business is secured.

3. Customer Retention and Support

Whenever a customer faces problems after a purchase, the time and efficiency of supporting the customer determine the chances of retaining the customer and building word-of-mouth publicity. It’s the fact that you can’t guess what problems customers may face and what will be the best solution. But big data analytics can be a savior. It helps you to predict what your customers may face and helps you build solutions to retain them.

For example: Through diagnostic big data analytics, using past events the root causes for any problems can be figured out. Similarly, through churn analysis companies can pinpoint what has gone wrong and how it can be prevented in the future. This helps to prevent customers from facing the problem that can arise and keeps you ahead of the competition.

To be a brand and not just a name, every interaction point with a customer matter and it builds the perception in the customer’s mind. The use of big data analytics solutions helps to better understand customers, their behaviors and helps companies to be perceived as “Best” in the ocean of competition.

To discover the potential of Big Data Analytics in AI, please check out at Celbridge Science. To connect with us at patrick.hogan@celbridgescience.com or call us anytime on 636-594-2242.

CIO Magazine Machine Learning Company of the Year 2021

By | News

Celbridge Science has been featured in CIO Applications Magazine as The Machine Learning Company of The Year 2021.

Celbridge is proud of being recognized for the immense hard work and the results we have delivered over time for various industries.The feature in the magazine presents a platform for the company to showcase its stories and expertise in AI and Analytics.

The digital version of the magazine is now available for you to read by clicking the button below.

Read the magazine

Big Data Analytics in Pharma Industry

By | Blog

In the current world, where COVID 19 has showcased the rate of transmission of a virus worldwide, there is a clear ask for innovative technology solutions to speed up the processing of drug discoveries. As pharma companies raced towards minimizing timelines for vaccine discovery, which usually takes 5-10 years, it has now been done within a couple of years. What changed exactly? The answer is Data Analytics.

Data has always been the pedestal for drug discoveries to identify the sequences and theories about the efficiency of the treatment. But as more and more factors are introduced in the picture and huge amounts of data are being generated, to an extent where it is humanly impossible to process it within smaller time frames, technology comes into play. The term is coined as ‘‘big data’’, which means large and complex data sets that are difficult to process using traditional database technology. But big data is not just about the size or amount of data, and it comprises 4 dimensions, Volume, Velocity, Veracity, and Variety.

The Four Dimensions of Big Data

Big data unlocks the true potential for data analytics, from accelerating drug discoveries to understanding patient trends and behavior. Big data unlocks the true potential for data analytics, from accelerating drug discoveries to understanding patient trends and behavior.

Here are 4 ways that pharma companies use Data Analytics to drive innovation.

1. Drug Discovery and Development

Applying predictive analytics to the search parameters should help them hone in on the relevant information and also get insight into which avenues are likely to yield the best results. Pharmaceutical Companies like AstraZeneca, Celgene, Bayer, Janssen Research and development, Sanofi, and Memorial Sloan Kettering Cancer Center, started a data-sharing initiative under the name Project Data Sphere to share previous research data on cancer to help experts in their research on treatments against the disease.

2. Targeting specific population segments

Scrutinizing data sets from various sources allows researchers to identify patterns that provide them with key feedback on how infections perform concerning specific population segments.

3. Customized patient care

Companies can leverage data from devices, which provides insights into the current patient behavior through analytics models. According to the formulated insights, the medical practitioner or companies can use this information to design services targeted to different demographics and curate treatments.

4. Streamlining Clinical Trials

Big data can assist the appropriate candidates for clinical trials by analyzing demographics and historical data, remote patient monitoring, due diligence of the previous trails track record, and even predicting outcomes of the trial. In addition, big data analytics can further narrow the patient funnel by considering more factors such as genetic information that help streamline clinical trials and drive down costs.

As the pharma companies are looking to drive maximum ROI before their patents expire, data analytics which substantially reduces timelines plays a key role in driving value. Not only does technology derive enhanced results, but it also provides a competitive advantage.

Artificial Intelligence Trends in Biotechnology

By | Blog

‘Data is the new oil’, we can hear this term being thrown around. Generating large amounts of data was never a problem. The difficulty was in making that data organized and available in an actionable format – in shorter time duration. However, the real challenge has been driving meaningful insights from that data – which is being addressed with the help of AI and Machine Learning.

The application of AI has moved past the general applications as we can see it making groundbreaking advances in the pharmaceutical and healthcare industry.

1. Drug Discovery

The COVID pandemic highlighted the need for faster drug development procedures, and the pharma companies delivered. In a global setting where disease transmission is quick, the need to expedite drug discovery procedures is important. Pharma and Biotech companies are leveraging the abilities of AI for rapid drug development. Once the genes and proteins associated with diseases are identified, the cognitive ability of AI allows thousands of chemicals to be screened to recognize potential drugs that affect the disease. The vaccine or drug development that would previously take on an average of 5-10 years has been reduced to 2-3 years with the help of AI and Data Analytics.

2. Digital Biomarkers

Digital biomarkers are making it easier to analyze data and predict or influence healthcare outcomes. As patient data is being increasingly harvested digitally, the opportunities to implement artificial intelligence (AI), especially machine learning, are increasing exponentially. Recently, the FDA issued the emergency use of the first machine learning-based COVID-19 non-diagnostic screening device called the Tiger Tech COVID Plus Monitor. The device recognizes certain biomarkers that may be indicative of SARS-CoV-2 infection as well as other hypercoagulable conditions or hyper-inflammatory states in asymptomatic individuals over the age of 5.

3. Gene Editing

Gene editing has made it increasingly possible to alter DNA sequences in living organisms in turn customizing the gene expression. The main applications of AI in gene editing are the identification of harmful genes and the treatment of diseases. AI reduces concerns regarding human errors with gene editing and is recognized to improve the procedure accuracy and yield better results.

While there are drawbacks to gene editing, AI and Machine Learning have tremendous potential to make gene editing more efficient and accurate. This will influence pharmacogenomics, genetic screening tools for newborns, enhancements to agriculture, and more.

4. Precision Medicine

he role of AI in Precision Medicine development is promising a new future of personalized healthcare. AI-enabled reinforcement learning allows sophisticated computation to generate insights, and empower clinicians to make well-informed decisions. On the other hand ‘precision therapy’ can be coined as a term as well, where we can see AI and ML algorithms being trained according to certain common health conditions, which enable caregivers to outline a therapy for patients suffering from mental health issues based on insights.

Data is critical for drawing insights, and technology is bridging the gap between this data and actionable intel. While the above-given trends are only the tip of the iceberg, AI & ML are expected to dramatically improve health outcomes – whether in the research of new drugs or the delivery of clinical care.

Artificial Intelligence in Bioinformatics

By | Blog

Alan McCarthy, also known as the father of Artificial Intelligence coined the term back in 1955. Since then AI has seen steady growth until recent years. In the past few years, businesses have started incorporating AI more and more in their operations for ease of functioning. We can see it being used in a myriad of business activities such as building personalized customer experiences, cyber-security, predictive service and maintenance, and smart assistants, to name a few. Machine Learning and Deep Learning, which form the subsets of Artificial Intelligence, have also opened several avenues in the research community.

Let’s Focus On Bioinformatics – Connoting our niche focus area of Bioinformatics and its aspect of Clinical Research and Drug Development, let’s look at AI applications that have reshaped the sector.

AI, Computing & COVID 19

Integration of AI models into supercomputers is enabling them to run complex calculations in epidemiology, bioinformatics, and molecular modeling. We have seen IBM leverage this with IBM Summit to enable the experts at Oak Ridge National Laboratory and the University of Tennessee to screen 8,000 compounds to bind the main spike protein of COVID and come up with 77 possible drug compounds which could be experimentally tested. These procedures, if carried out in the traditional ways, could have taken months or even years.

Machine Learning And Bio-sciences

Machine Learning can be leveraged in decrypting large datasets such as DNA sequencing, Protein classification, and Gene expression.

For example, AI ingestion allows easy analysis of voluminous patient data for DNA sequencing, to predict and personalize medicines. Researchers are also extracting protein data with advanced techniques in Natural Language Processing (NLP). Neural network applications such as Bidirectional Long Short-Term Memory (BLSTM) is helping in the study of critical features of protein sequences. Deep learning tools such as ProDec BLSTM, DeepSea, and Basset are proving to be useful.

Data Collation & Analysis For Drug

Data collection is a crucial part of drug development, which can be done with the help of AI tools. Natural Language Processing (NLP) uses machine learning to analyze large data sets and recognize biologically applicable terms. It helps scrutinize the copious amount of data and identify previously missed patterns or relationships concerned with a disease and its drugs. The ability of NLP-mined data can be enhanced by compiling results from multiple sources. We can see pharmaceutical companies identifying trends in diseases and compiling data sets that can garner better and quicker results in drug discovery.

Enterprises Are Looking For Activation Partners

While the future holds a lot of scope for advances in the bioinformatics industry, with AI, ML, and Analytics, stakeholders still need experienced implementation partners. What also matters is their industry know-how and the ability to streamline the research timelines and in many ways make it cost-efficient as well.

DNA analysis, new drug development, artificial organ generation are picking up the pace. Here, the right AI solution provider can be a game-changer providing insights that would have taken a lot of years or to generate if done the traditional way.

If you’re looking to develop perfect drug discovery, correct analytical research, and accurate methods for integrating medical science bioinformatics, please check out at Celbridge Science. To connect with us at patrick.hogan@celbridgescience.com or call us anytime on 1636-594-2242.

Advances In Using AI In Drug Development

By | Blog

Artificial Intelligence has transformed biomedical research in terms of Drug Discovery and Design. It has made the process more efficient and has paved a path for the discovery and development of inexpensive and efficient drugs and rational structure-based drug design, with reduced time and costs. Pharmaceutical companies are using Machine Learning Models and Advanced AI Insights to analyze and use complex datasets as a solution to the increased cost of new drug development.

Drug discovery using AI

On average, a new drug coming to the market has to go through phases that involve testing the drug on sample groups for years, and with costs involved up to billions. It also involves the standard approaches of testing how different molecules of the drug composition interact with different targets. The preclinical development phase of drug discovery tests potential drug targets on animal samples.

During this phase, trials can be accelerated with minimal errors using AI models, enabling researchers to easily find the permutations and combinations of the drug interaction with the animal test sample model. AI can also facilitate participant monitoring during trials, generating a larger set of data more quickly, and aid in participant retention by personalizing the trial experience. With advancements in AI, the automatic feature extraction ability of deep learning supports models with better accuracy and delivers more reliable results. Secondly, deep learning models’ generative ability can be utilized to speed up the drug discovery process with better prediction capability and a low failure rate.

Drug design using AI

Drug design is the process of finding new medicinal drugs based on the knowledge of biological targets. How strongly a molecule can bind to a target is the main goal of a drug design phase. With the computational machine learning models, the computational affinity of a compound for synthesis can be predicted, and hence only one compound needs to be synthesized saving enormous time and cost.
Artificial intelligence (AI) proves very efficient in various fields of drug design that include virtual screening, activity scoring, quantitative structure-activity relationship (QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion, and toxicity (ADME/T) properties.

Many pharmaceutical companies are riding the wave of Drug Design and Discovery using AI. For example, AstraZeneca using AI is researching new drugs on chronic kidney diseases. Bristol-Myers Squibb (BMS) has deployed machine learning models in finding data patterns that are associated with CYP450 inhibitors, these patterns help to reduce adverse side effects and interactions of the drug-in-development.

GSK Pharma has its in-house AI unit which has developed a machine-learning algorithm to identify protein crystals. Not only the big pharma players but the mid-sized pharmaceutical companies and startups are also taking advantage of these advancements of AI in drug discovery and design.

The next decade will see a transition from traditional drug design and discovery methodologies to highly intelligent AI-based algorithms that will deal with the new scale and complexity of the data used in drug design and discovery.

please check out at Celbridge Science. To connect with us at patrick.hogan@celbridgescience.com or call us anytime on 636-594-2242.

A Learning Path Towards Data Science Future

By | Blog

As many industries are utilizing analytical data to improve business practices, big data and data science career opportunities are exploding in parallel. The U.S. Bureau of Labor Statistics (BLS) reports that the demand for data science skills will drive a 27.9% rise in employment by 2026 and data science-related career is estimated to grow 11% from 2014 to 2024. The good news is that there are several paths that a data science career can take. The challenge is to understand how these careers differ and what kinds of skill sets are required individually for them.

The question now is, where and how to start?

J.P. Morgan –
“The first step towards getting somewhere is to decide you’re not going to stay where you are.”

But have you ever wondered what Data Science is? “Data Science is used by computing professionals having skills for collecting, shaping, storing, managing and analyzing data for organizations to leverage accurate data-driven decisions generating values”. Almost every interaction with technology includes data. Amazon purchases, Facebook feed, Netflix recommendations, and even the facial recognition required to sign in to your phone.

      1. First Step – The 3WWhere can your data science skills take your career? What job title is offered? Which path is right for you? A Data Scientist has many hats in his/her workplace. Not only are Data Scientists responsible for business analytics, but they are also involved in building data products along with developing visualizations and ML algorithms. Finding answers to these questions should be the first step in your data science journey. We can’t cover every potential job title, but we can surely find some leading roles in the data science universe and how they differ with the progression of a career in the field if you’re starting.
      2. Second StepChoosing a Tool / Programming Language. The most probably question asked by the beginners. Straightforward answer: Any of the mainstream tool/languages. Tools are just a means for implementation; understanding the concept puts you a step ahead. But then also to give you an idea, start with the simplest or the most familiar or GUI based tools. Also, you should look into attributes like availability, cost, ease of learning, advancements in tools, support for data handling, etc. Currently, the two programming languages used widely are Python and R. In case if you are familiar with these languages you can prefer to skip this step.
      3. Third Step – Communicate with databases. If data science is the art, a database is an artist. Data is the heart of data science. Whenever you work on a data science project, you need to have data that can be analyzed, visualized to build a valid database. To do so and communicate with a database, you need to speak its language where SQL plays its part. A Structured Query Language used to communicate with a database. If you could design a simple database, then this will take you to the next level.
      4. Fourth Step – Understanding the Math. Math is the core of data science, without math no data processing can be taken forward. You need to understand the basics of probability theory, statistics, and linear algebra to comprehend data science. You not only need to understand how it works but also you need to know how and when to use it. Here are some useful courses you can enroll in and get your math solved. Data Science Math SkillsMathematics for Data Science Specialization.
      5. Fifth Step – Collaboration & Interaction. Controlling versions across systems to track changes in the source code during the development and testing process. Git is used as a version control system to harmonize the group of programmers, developers, and testers. It allows you to coordinate among teams without needing to interact much with the command line via GitHub or GitLab.
      6. Sixth Step – Exploring Machine Learning. The fun part starts here. Relate what you have learned so far, discover and implement different Python and R packages using SciPy and NumPy. Here you’ll not only get to experience how data science works but also the impact it has on our daily lives.
      7. Seventh Step – Deep Learning. It is a subset of machine learning concerned with artificial neural networks, inspired by the structure and function of the human brain to learn from large amounts of data. Deep learning clarifies its actual usage when it comes to your daily life via Virtual assistants, Chatbots and service bots, Facial recognition, personalized shopping, and entertainment and to solve complex problems without human intervention.
        Now you are almost at the finish line.
      8. Eighth Step- Natural language Processing (NPL). A branch of artificial intelligence that helps computers understands, interpret, and manipulate human language and processes via software. NPL includes Speech Recognition, Text To Speech Application, Sentiment Analysis, and Virtual Assistance like Google, Siri, Alexa, and other different kinds of conversation bots.

So here we reached our destination. You being a data scientist always need to be in a continuous stage of development and learning. This field is rapidly growing with new techniques, algorithms and research. The most important thing about this journey to know is that you can do it. You just need to dedicate yourself enough time, efforts and be open-minded to achieve your goals.

Integrating Bioinformatics, Medical Science and Drug Discovery

By | Blog

Healthcare in the 21st century faces a unique set of challenges such as rising healthcare costs, analytical research, productivity in curative discovery and development. Reducing costs with the time required for steps in the drug discovery pipeline is crucial to deliver better drugs promptly. Succeeding these odds bioinformatics industry has turned to computational approaches to overcome the traditional development in drug discovery and medical science. The use of biomarkers in drug development has shifted the trend toward more quantitative, evidence-based drug development integrating bioinformatics, medical science and drug discovery.

Integrating Bioinformatics, Medical Science and Drug Discovery promises to equip researchers with tools and resources for efficient capture and analyze the structure of medicine while allowing medical field and doctor’s access to evidence-based insights for efficient patient care. Here we highlight some of the areas individually in which bioinformatics resources and methods are being developed to support the drug discovery pipeline.

  • Bioinformatics
    Bioinformatics now is integrated with computer science which is now emerging as a crucial element with a standard shift in modern biology and biomedical research to use the computers, software tools and computational models for accurate and precise discovery. Bioinformatics deals with the exponential growth in biological data have led to the development databases available as publicly across the world. Bioinformatics plays a vital role in the integration of broad disciplines of biology and aids how biomedical investigators use the information in their testing. The complete process of data collection to analysis of the results of such tests is also known as Clinical Informatics.
  • Informatics and Medical Sciences
    It is a known fact that most of the doctors are opposed to computers. To overcome this problem one of the solutions proposed is to introducing Palmtops personally customized for physicians that fit easily into the pocket of a lab coat, helping the doctor to feed in the medical data sequentially. This leads to addressing the basic need of any medical analysis, data capture creating Electronic Medical Records (EMR) eventually, developing a database for reference and analysis and routine clinical record in the form of charts or specialized datasheets. This concept drastically reduces the possibilities of manual errors due to frustration and other emotional disorders. Shortly the complete information of the patient can be accessed from the EMR ranging from drug trial data, tests performed with accurate outcomes and research analysis.
  • Bioinformatics and Drug Discovery
    Nowadays infectious diseases are the world’s biggest killers. WHO states that “there occur more than 13 million deaths a year in a developing country due unavailability of efficient drugs and availability leading to the high cost”. The major problems faced by mankind are the discovery and development of cheap and efficient drugs. Rational Drug Design using Bioinformatics is a one-step solution to this problem to end the trial and error process of drug discovery to rational structure-based drug design with reduced time and cost. Taking into account all factors we have to develop an effective structure of drugs with potential targets identifying current scenario.
  • Need for Integration
    Rapid advances in the field of computers coupled with increasing technology favor the implementation of computer applications in medical science and bioinformatics. Added, the availability of large databases on the internet has revolutionized the way a doctor addresses a strategy for treatment. Human Proteomics Initiative is a classic example, showing the necessity of integrating Bioinformatics to predict structures and functions of proteins. Medical science to identify proteins in metabolic or other disorders. And drug discovery to identify novel drugs against the predicted targets.

Thus all three areas must work in synchronization to achieve the ultimate goal of the precise drug development process and apply it for the betterment of human lives with the early characterization of a drug that can make the next blockbuster. If you’re looking to develop perfect drug discovery, correct analytical research, and accurate methods for integrating medical science bioinformatics, please check out at Celbridge Science. To connect with us at patrick.hogan@celbridgescience.com or call us anytime on 636-594-2242.

Health Care of Tomorrow, Today: How Artificial Intelligence is Fighting the Current, and Future COVID-19 Pandemic

By | Blog

Consider the recent COVID 19 pandemic, that has caused the world to come at a halt in itself and continues. Countries across the globe are assembling all their resources and are deploying cutting-edge technologies to fight against the effects of COVID-19. During such unprecedented times, healthcare technologies have pushed themselves for agile innovation and have increased the volume of services. It is well said that ‘Amid every crisis, lies a great opportunity’; the deadly crisis has not only opened up new vistas of opportunities for the technology but has also given rise to Artificial Intelligence in particular. AI minimizes the need for human interaction which is the optimal way to prevent its spread in the community. AI is demonstrating its capabilities through diagnosing risks and assisting in medical advancements to tackle the pandemic.

Communities of peoples join the battle to fight for their lives against the pandemic. AI and technology are leveraging their tools and solutions to help in combating the crisis and assist in prediction, notifications, data dashboards, social control, and medical diagnosis & therapy to make accurate decisions quickly. Artificial Intelligence has lowered the burden by automating the processes, organizing tons data, and creating automatic clinical notes which resulted in the enhanced ability of doctors to focus on treating an increased number of patients.

Wearable, such as smart suits which researchers have designed, can observe human body parameters and alerts them when body temperature goes beyond normal one. Biometrics that measures respiration patterns, cardiac diagnosis and other human activities provide statistics for the doctors to minimizing visits to infected patients. Hospitals across the world have implemented AI to support medical professionals and their staff to treat infected patients and effectively monitor and manage the COVID-19 pandemic. Such real-time data for remote monitoring creates a unique dataset to understand the disease progression and develop predictive measures. Such a platform would offer a chance for researchers and companies to get ahead of the disease and develop more effective treatment plans and vaccine quickly.

Celbridge Science takes an opportunity to help scientist and organization to integrate with a laboratory information management system to accelerate the drug development process to discover efficient and effective AI-powered tools to address COVID-19. The company ensures that the clients to transform the scientific process that would power their enterprise. We use an Artificial Intelligence-based approach to help clients with real-time analytical solutions, utilization of your critical data using our big data tools, and advanced cutting edge analytics in medical science. We help them in diagnosing medical errors with deep learning AI algorithms for targeted treatment to scale better results. Our services enhance your business processes with the power of artificial intelligence to transform, simplify, and enhance performance across your value chain for optimum results.

Our services allow scientists to conduct their research in an environment supported by powerful analytics, ensuring agile, clean, and efficient delivery. We work on the raw data sets with the most advanced algorithms for maximum efficiency and continue leveraging Artificial Intelligence to derive meaningful results to enable science to achieve breakthroughs in the next-generation.

Leveraging the potential of AI provides a tool to the global community in tackling the pandemic. With the rapid development of AI solutions during the COVID, the medical science of tomorrow is already addressing the challenges we are facing today. If you’re looking to leverage such services during COVID-19, please check out at Celbridge Science. To connect with us at patrick.hogan@celbridgescience.com or call us anytime on 636-594-2242.

Data Science – Dealing Pandemic Globally

By | Blog

Looking at the post-COVID-19 scenario through a data lens, that has brought societies to halt demands for an increase in investing in technologies. Ongoing lockdowns, uncertainties in businesses and project recessions are struggling to cope up day to day operations and decisions through all the domains possible. While the current situation seems bank and the future seem uncertain organizations need to be recalibrated to new normal. Amidst this pandemic, we are also dealing with changing consumer demand, supply chain disruptions, modification in healthcare diagnosis and medical science and many more. The only solution that will be of value to adapt and uplift crises is going digital and having strong data science capabilities.

With most of the organizations adopting remote working, they have to deal with certain challenges and multiple operation disruptions. Many companies are adopting agile business processes, new operational models for long term impacts via data science and data-driven simulations using artificial intelligence and machine learning. Also, organizations have already begun to respond by investing in technologies like Augmented and Virtual Reality, many companies have started to ensure contactless transaction for 24/7 and 365 customer support via intelligent chatbots and RPA to ease complex and rule-based digital task such as filling multiple and repetitive data. Apart from these responses company are building IoT devices with Natural Language Processing (NLP) to analyze the format of data and gather real-time data, remotely monitor pulse rate, glucose levels to detect possible outbreaks with intelligent healthcare systems.

Data Science is continuously evolving with its ambiguous nature to play a major role in reshaping the structure of data and analytics in such a volatile environment. Data Science offers a very agile and reinforced approach in the following areas:

  • Prescriptive Modeling:
    Logic-based models with machine learning approach would lead to stable decision making with high priorities for contextual information. Currently, organizations are facing risk due to lack of concrete information, with prescriptive models coming in play company would be able to handle the risk of uncertainty and implement the best course of action.
  • Data Management:
    Managing data is a strategic resource before conducting its analysis. It’s imperative to have a well defined and documented data management process that smartly monitor and improve the accuracy of data patterns with a change in every data added.
  • Advanced Artificial Intelligence:
    Enhancing your business processes with the power of artificial intelligence helps companies to pivot to a vastly improved competitive position. Advanced AI and advanced analytics algorithms are used to transform, simplify and enhance your business performance across domains to bring the best in them and accelerate business capabilities to reduce manual burdens with valued analysis
  • Data Analytics:
    Understanding the structure of disease, creating treatment plans and improving the existing plans in medical science, data analytics is coming up with diagnosis which more reliable and faster. With accurate data results and analysis, a scientist can perform and analyze medical test easily speedily, freeing the extra resources and gaining valued insights from the extracted data.
  • Machine Learning and Big Data Processing:
    The value of data is directly linked to its quality. The insights derived from the complex and unstructured data cannot be trusted. With Machine Learning and Big Data coming into the picture can detect missing values, finding duplicate records that have the same entity with different terminology and normalizing the data to advance business processes and data mapping.

Most of the organizations are still crawling and learning to combine the new possibilities and outcomes of data science. The future is an intelligent industry that leverages all the data and provides all optimized outcomes that would efficiently work for us in every domain of Information Technology and Medical Science. Moreover, the companies developing solutions not only need to rigorously test their solutions but also must ensure that the solution leveraged should work post-COVID-19 world with an added extra layer to the one that would be built in for the future.

Optimizing the potential of Data Science addresses the global community with solutions to tackle the pandemic. With the rapid development of data science during the COVID-19, the data science of tomorrow is already addressing the challenges we are facing today. If you’re looking to leverage such services during COVID-19, please check out at Celbridge Science. To connect with us at patrick.hogan@celbridgescience.com or call us anytime on 636-594-2242.