Author: Rajnandini Das

Predictive Data Science in Food and Beverages
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Predictive Data Science in Food and Beverages

Introduction: Predictive Data Science

Predictive data science is no longer limited only to data scientists and engineers. Interested to explore what you can do in the food & beverage industry? Read this article to know how your competitors are leveraging predictive data science to improve their operations. Progress happens with each step taken. With the market becoming more competitive than ever, everyone is eager to find a breakthrough solution. According to a news report on CISION PR Newswire, the global food and beverages market reached a value of nearly $5,943.6 billion in 2019, having increased at a compound annual growth rate (CAGR) of 5.7% since 2015. The market is expected to grow at a CAGR of 6.1% from 2019 and reach $7,525.7 billion in 2023. Meaning that there are massive amounts of data just waiting to be analysed and processed for meaningful insights using predictive data science. Let us now see what data science and analytics can do for the food and beverage industry.

Predictive Data Science in Restaurant Industry:


When talking about the benefits of predictive data science in food, we cannot leave out restaurants. Restaurant owners do not seem to realize the tremendous amounts of data that is generated from their customers. Therefore, there are chances that they miss opportunities to decrease costs and improve customer experience. With the explicit and precise implementation of data science, restaurant owners can obtain real-time analysis of their customers’ data and make the required improvements. For instance, owners and founders can point out their highest selling or most expensive items, the quality of food offered, and more. Based on this data, they can make informed choices, and also fix their mistakes.

Predicting shelf life:


Each type of food has its own shelf-life, causing it to expire over time. However, there are certain items of consumption that only grow better with time. For instance, wine gets better with time, but fresh produce will expire. Different items of food and drink have different shelf lives and managing all of them independently is a major challenge for this industry. The procedure for dealing with wine is very different compared to the procedure for dealing with expired products. But, by incorporating predictive data science into the picture, data engineers can predict the shelf life of produce, thus ensuring pre-emptive action is taken to reduce the amount of waste and saving money and time.


Sentiment Analysis:


Social media, review websites and food delivery apps have allowed the food industry to do something that was not possible in the past, i.e., sentiment analysis. Using NLP, organizations can analyse their social media channels and discover patterns and trends in the data. This will allow them to discover the most popular foods and beverages of any season. It also allows them to discover the popular foods during special occasions and other festivities. Brands, restaurants, and organizations can, in turn, be more receptive to people’s demand and act accordingly. Google analytics can be a helpful medium in this case.


Better Supply Chain Transparency:


Let us now look at another example of how data analytics can benefit growers, transporters, processors, and food retailers:

  • Information about the weather is also entered into the database. Inputs about precipitation and temperatures can be automated.
  • The farmers enter the test results of the soil, along with the planting and harvesting data into a database used by a particular software program.
  • The logistics company that transports the farmer’s crop from the farm to the processing mill, inputs the start and end times for the trip in the database.
  • The food processor enters the start and stop times for various stages of the processing, sorting, washing, packaging, and placing in cold storage can all be tracked with automated sensors.
  • The product is then monitored from the processor to the retailer. Any delays that could cause the food to spoil can be easily identified.
  • At the destination, the vendor can record the quality of the food when it arrives,
  • Customer feedback on social media can also be added to the collected data, to provide further insight to the food supply chain.

The entire supply chain can access this information. If there arises any problems, changes can be made to this process to prevent a further recurrence. Moreover, retailers can choose to accept or reject the shipment based on this data. The software performs an analysis of the data and provides intelligent and accurate conclusions to all parties involved in the supply chain. Analytics software takes the help of a large number of sources for making its analysis, including social media. Both structured and unstructured data is used for the analysis. This collection of data is known as big data.


Measuring Critical Quality Attributes:


There are certain primary attributes against which the food and beverage industry measures the quality of its products. These attributes can be a great asset in marketing them – for example, the alcohol concentration in beer. However, conventional methods of measuring primary attributes are time-consuming. In case of beer, the alcohol level is measured using a method known as near-infrared spectroscopy. This method, however, is time-consuming and delays the production process. Predictive Data science and analytics allows organizations to explore other methods that are faster and more cost-effective, like the Orthogonal Partial Least Square Regression and multiple regression models to measure alcohol content and colour.


Better Health Management:


Consumers wish the food industry to be more transparent. The leading firms of the multi-billion-dollar beef industry realised this when they gathered for Beef Australia 2018, a convention that sees over 90,000 visitors. Consumers expect restaurants and organisations to be more forthright with them. They expect to be completely aware of how the food was produced, how the livestock was treated and what chemicals, if any, were used in the food. They want to be completely aware of what they are consuming. Data science and analytics helps incorporate transparency within these supply chains, so that organizations can be more honest with their customers. Transparency also assists in solving problems related to logistics and supply. For instance, it becomes easier to track contaminated food supplies to their storage locations, thus eliminating the chances of spread of food-borne diseases.
Predictive data science and analytics allows organizations to protect food health and prevent cross-contamination. Geographical data, along with satellite data and remote sensing techniques, allows data analysts to ascertain changes. This information, along with data on temperature, soil property, and vicinity to urban areas, can predict which part of the farm is likely to be infected with pathogens, and take immediate action beforehand. Another excellent example is when cities are short on food inspectors – data analytics can analyse historical data on 13 key variables to help pinpoint the riskiest establishments, making better use of limited food inspectors.

Predictive Data Science for Food Innovations


Organizations need to keep pace with the changing demands of the consumers. With the fluctuation in their tastes according to season, time of day, weather, mood, etc, it becomes crucial for the organizations to take the assistance of predictive data science. This data is then converted into meaningful information which aids in making important decisions, as well as to improve sales and overall performances.

Food Marketing


Predictive data science also assists businesses in improving their marketing campaigns, developing creative and high demand products, and empowering firms to stay updated over their competition’s growth rate, control quality as well as assess decisions regarding purchasing and prices. The data also helps businesses keep track of certain crucial factors, like the quality of their products, by gauging if the composition of the product has been altered in any way.

Conclusion


Predictive data science and analytics has definitely brought about a positive growth in certain industries, including the food and beverage industry. This industry is prone to its fair share of difficulties. With the ever-growing population, consumers are always looking to choose the best option that they can get. Since the consumer is the key, organizations need to make decisions revolving around the consumers’ tastes. Data science enables businesses to derive conclusions about which option will be best suited for the consumers. It allows organizations to collect and analyse data and derive at interesting patterns and trends over a period of time. The technology can also be used to conceive several creative solutions to problems plaguing the industry while bringing positive developments to food and beverage.

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Data Science in Healthcare Industry

Introduction


Data is everywhere. From small businesses to large multinational organizations, data is used in almost every area of study and work. From the small mathematical problems solved by a child to the complex functions executed in large organizations, data is used almost everywhere.

Data is one of the most important components of any organization, because it assists leaders in making decisions based on absolute certainty, comprising of facts, statistical results and trends. Any result based on correct and concise data tends to be correct. Data can reveal a lot about an organization, and organizations rely heavily on this data.

Due to the growing relevance and importance of data, data science came into the picture. Data science is a multidisciplinary field. It uses algorithms, scientific procedures and approaches to derive conclusions from massive amounts of data. This data can be either structured or unstructured. In this article, we shall be looking at data science in the healthcare industry.


Data Analytics in Healthcare

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Medicine and healthcare are two of the most important components of our lives. Traditionally, medicine and medical advice was given solely by the doctors based on the patient’s symptoms. However, this was not always accurate and was prone to errors. With the advancements in the field of data science, it is now possible to obtain a more accurate diagnosis. The AI is being used not only as a tool for diagnosis but also for break through discoveries. In a latest advancement Google has achieved a huge success in unfolding protein structures. The very core of the problem that many biochemical Scientists were trying to solve from many decades!

Scientists have also developed ‘DNA Nanopore Sequencer’ which is a tool that helps patients before they suffer from septic shock. It provides genetic sequences mapping, which abbreviates the time span of the information preparing activity. Moreover, this tool recovers genomic information, BAM document controls, and provides calculations.
The new health data science perspective allows applying data analytics, that are collected from various fields, to augment the healthcare sector. There are several areas in healthcare, such as drug discovery, medical imaging, genetics, predictive diagnosis and others which make full use of the results derived at through data science techniques. With ERM’s, clinical trials and internet research, there is so much data being accumulated every day. With the majority of people seeking healthcare advice online, gathering data has become increasingly convenient.

How can it work?

Let us now try to derive an insight into how data science and healthcare can become mutually beneficial.

  1. Data management and Data Governance: The opportunities derived from managing data efficiently are extensive. When data is managed effectively, it makes information easily accessible to all those in the healthcare industry. When data is analysed and shared effectively among doctors and healthcare providers, it will enable them to be more personal and humane in their approach towards treatment. Since the healthcare sector has its fair share of risks, data analytics should always be at the top of its game; it should be up-to-date and acute. The Data related to Medical records, ongoing condition charts of patients, medical database, genetic research, medical image diagnoses can be effectively leveraged to unfold valuable information.
  2. Each patient’s medical records can be combined into one dataset, and then analysed and utilised when needed, to derive at the required conclusions.
  3. Data management also involves data sharing. Data can be shared across several datasets, eliminating the need for excessive office work.
  4. When data is analysed repeatedly, it will bring out any and all errors in clinical data.
  5. Cloud-based clinical software enables faster processing of data, leading to time saved when deciding on treatment or obtaining test results.
  6. Machine learning assists in shortening the process of drug discovery.

Challenges ahead


While data governance has been recognized as crucial to healthcare, there are opportunities to expedite the prioritization of data governance, so that data is accurate, complete, structured, precise and available. Data governance plays a pivotal role in patient engagement, care coordination, and looking after the overall health of the community. If data is not governed properly, different healthcare companies will release inconsistent data which will prove to be a major hindrance. Healthcare data science apps exist in order to avoid such inconveniences.

Workflow Optimization and Process Improvements: Big data analytics is not as profound in healthcare. Hence, certain decisions are taken based on the ‘gut instinct’. Apart from this, lack of coherent healthcare information exchange between the systems and shortage of skilled workers to fill knowledge gaps are other two challenges involved in the process.


Opportunities Genetics/Genomics

  • Treatment personalization: With the introduction of new technologies, including new forms of genomic profiling or sequencing, it provides a new look at the world of genomics. The massive amounts of data today produce genetic data faster than ever. This is partly because the techniques of structuring data, lag behind the ability to actually get the data. Healthcare data science produces copious amounts of data, but that data needs to be made sense of. Some of the challenges in the field of genomics are:
  • Studying human genetic variation and its impact on patients
  • Identifying genetic risk factors for drug response

Opportunities in Medical Imaging

  • Medical Imaging: Medical imaging refers to the process of creating a visual representation of the body for medical analysis and treatment. If is a non-invasive method for doctors to look inside the human body and decide on the required treatment plan. With the swift growth of healthcare and artificial intelligence, this process of medical imaging becomes easier. Some of the types of medical imaging include tomography, longitudinal tomography etc. The primary methods of medical imaging are X-ray computer tomography (CT), PET, and MRI. Medical imaging needs the images to be absolutely accurate. Even minor discrepancies might lead to disastrous results, which can be catastrophic to the patients. The images need to be precisely viewed and interpreted. Data analysis refines these images by enhancing their characteristics like

Opportunities in Predictive Analytics

Predictive analytics refers to a technology that learns from experience, i.e. data, to predict a patient’s behaviour. It builds a connection between the data and the consequent actions which need to be taken based on that data. Predictive analytics allows healthcare to use predictive models or models found specifically in health data science. This allows identification of risks even before they occur. However, there are some drawbacks to predictive analytics.

Predictive analytics is already being used in healthcare manufacturing to meet safety and efficacy requirements of drug products and medical devices.

Opportunities in Drug Research

If we look back to the time of another major pandemic, the Spanish Flu, we see that drugs and vaccines took a considerable amount of time. But now, with the help of data science, data from millions of test cases can be processed within weeks. Development of vaccines and other drugs has become easier and less time-consuming.

How can Let’s Excel Analytics Solutions help here          

We at Let’s Excel develop easy-to-use software interfaces using Artificial Intelligence and Machine Learning algorithms to take healthcare research to next level with data science.  Below is an example of the diagnosis of a tumor as benign or malignant using DataPandit‘s MagicPCA solution.  

Advantages

  • Lesser time taken and more precise outcomes lead to more effective work processes.
  • Healthcare providers and other staff get the chance to perform more tasks in limited time.
  • More effective work processes lead to higher recovery rates, faster reactions to crises and, in turn, less fatal results.
  • Patients get more personalized treatments.

Conclusion

Healthcare has a vast amount of data being generated every day. This data needs to be made sense of, it needs to be structured and organized so that meaningful conclusions can be derived at from the data. The healthcare industry needs to heavily utilize this data so that patients’ lifestyle can improve, diseases can be predicted before their inception. Moreover, with medical imaging analysis, it is now possible for doctors to find even the most microscopic tumours. Doctors can also monitor the conditions of their patients from remote locations.

Data science is already doing wonders for the healthcare industry. It is only a matter of time before it proves itself to be invaluable.