Data Science Products

Data Science Products: Top 3 Things You Must Know


Ever wondered why Clive Humby famously coined the 'Data is new oil' phrase?  Well, this blog article tells you exactly what he meant. The latest advancements in data analytics, cloud infrastructure, and increased emphasis on making data-driven decisions have opened up several avenues for developing Data Science Products.  People can build amazing data-based products that can generate revenues. In other words, the data is the new money-making machine. In this article, we will discuss the top 3 things that you must know about data science products.

# 1: What is a Data Science Product?

Data Science Product is a new era money-making machine that is fueled by data and built using machine learning techniques. It takes data as input and gives out valuable business insights as an output.

#2: Examples of Data-based Products

Classic examples of data products include Google search and Amazon product recommendations, both these products improve as more users engage. But the opportunity for building data-based products extends far beyond the tech giants. These days companies of all range of sizes and across almost all sectors are investing in their own data-powered products.  Some inspirational examples of data science products that are developed by non-tech giants are as below:


It mimics consumer choice in Medicare Advantage. The product compares and contrasts more than 5000+ variables across plan costs, plan benefits, market factors, regulatory changes, and many more. It helps Health Plans identify the top attributes that lead to plan competitiveness, predict enrolments, design better products and create winning plans. 

Cognitive Claims Assistant

Damage assessment in vehicles is an important step for insurance claims and auto finance industry. Currently, these processes involve manual interventions requiring a long turnaround time. Cognitive Claims Assistant (CCA) by Genpact automates this process. The data product not only reduces cost and time in the process but also accurately estimates the cost of repairs.

#3: How to Build Data Powered Products?

Steps in making data science product

Do you want to build a data science product too? Here are the five steps that will help you to build a good data science product:

Step 1: Ideation and Design of Data Product


The first step of building a data science product is Conceptualizing the product. Conceptualization starts with identifying potential opportunities. A good data science product is the one that solves a critical business need. An unsolved business need that can be solved using data is an opportunity for building data products.


Design the data structure that you will need to solve the business need. This often involves brainstorming on various data inputs and their corresponding valuable outputs that will solve the business need.

Step 2: Get the Raw Data

The second step in building data products is getting the data. If you already own the data, you are already covered for this. All you have to do is move on to the next step. If you don’t have the data then you need to generate or gather it.

Step 3: Refine the Data

As they rightly say, data is the new oil but it is of no use until it is refined like an oil. Understand the structure of your data. Refine, clean, and pre-process it if it is unstructured. Always remember the golden rule-‘Garbage in is Garbage out!’ Knowing the data helps you clearly define the inputs and outputs from your data science product.

Step 4: Data Based Product Development

This is the most tricky part in data science product development and needs a strong knowledge of the business process, business needs, statistics, mathematics, and coding. This knowledge forms the backbone of the data product. In the majority of the cases, this step involves building a machine learning model using domain knowledge. In some cases, it could also involve simple graphical outputs for exploratory analysis of the data. No matter what is the output the codes developed for executing the desired process need to be tested and validated for real-life use of the data product.

Step 5: Release!

This is the last step in data product development. In this step, tried, tested, and validated data science product is deployed on a cloud. The data product buyers can simply log in from anywhere in the world and use the product.


Anybody who owns the treasure trove of the data should develop a ‘Data Science Product’ or a ‘Data Product’. Now the question arises, is it possible to build data products without coding knowledge? And the answer is, absolutely yes! You can use our data analytics platforms that are specially built for non-coders. All you have to do is arrange your data meaningfully and just make few clicks to build your base model described in Step 4 of How to build data products as described above. When you deploy the model on the cloud your money-making machine becomes a reality. If you don’t like the idea of doing it all yourself, then you always have an option to outsource.

Like any other product, the success of the data product is dependent on its usability. Half the battle is won with a strong business case. The remaining battle can be won with mathematics, statistics, and computer science. This is exactly where we can contribute. Our aim to accelerate the data product development process. Let's unite your domain knowledge and data with our data modeling capabilities. Let's build amazing data science products!

Data Science

Data Science in Healthcare Industry


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.  


  • 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.


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.

Data Science Journey

Data Science Journey: Guidance for the New Bee

Considering the fast paced development in the world of Data Science his words are likely to become true. We live in the age of information and it’s quite usual to get overwhelmed with the amount of data we process each day, both in our professional and personal lives. The Internet these days is full of buzzwords related to machine learning, artificial intelligence, deep learning and the Internet of Things. Have you been wondering, if you can really make use of all these techniques in real life? Do you wish to begin your data science journey too? Then read this article to know where you can begin as a new bee!

Bill Gates once said, “A breakthrough in machine learning would be worth ten Microsofts”

Data Science Journey is based on the foundation of mathematical and statistical concepts which are universally applicable to all the sciences. That is the reason why data science is not limited to any specific field of study. It finds applications in numerous fields such as Healthcare, Food and Beverages, Petrochemicals, Agriculture, Defence and Space. To back these claims, let’s take a look at some common applications of artificial intelligence and machine learning in above mentioned fields:

Field NameCommon Applications
HealthcareClassification and Quantification of raw materials: Non-destructive testing of raw materials using spectroscopic sensors like IR, NIR, Raman etc.Distinguish between materials: Innovator Vs. Generic ProductDrug Discovery: Quantitative Structure Activity Relationship, Molecular modellingGenomics: Personalised medicines or dietMedical diagnosis: Cancer PredictionMaterial selection: Composition of materials that results in desired quality
Food and BeveragesAutomating sensory evaluation of productsClassification and Quantification of raw material: Identifying the source of raw materials and nutritional profile of the material (% of carbohydrate, fat and protein)Similarity between materials:Identifying substitute for an ingredientMaterial selection: Composition of materials that results in desired qualityShelf life: When is the product likely to degrade
PetrochemicalsClassification and Quantification of raw materials: Non-destructive testing of raw materials using spectroscopic sensors like IR, NIR, Raman etc.
AgricultureBetter crop yield: Identifying seeds with superior qualityCrop quality/ harvesting: Is it best time to harvest crop Shelf life: Predicting shelf life of harvested cropSoil texture using sensors
Defence and SpaceMaterial selection: Composition of materials that results in desired qualitySpace exploration: Is there water on mars?
Data Science Applications in various fields

I am sure you must have gotten interested in this new age Mantra and be wondering will this be applicable to you and how?

To know this let’s begin by answering below questions:

  • Are you dealing with large sets of data that do not make real sense to the human eye?
  • Are you currently using some tools to sort and analyze your data but still struggling and thus looking for a viable alternative?
  • Have you been told that the buzzwords of machine learning, artificial intelligence or the Internet of Things could solve a problem that you are faced with today?
  •  Are you very much fascinated by this new avenue seen all over the internet, but taking the first steps seem too daunting to make any real progress?
  • Do you believe that, trust is good but evidence is better?
Trust is good, evidence is better.

If you answered yes for any of the above questions, then yes, Data Science Journey is for you! Peter Sondergaard has once famously said that, ‘“Information is the oil of the 21st century, and analytics is the combustion engine”.

The best part is that anyone can use the data science techniques and benefit from them. You need not have to be a coder or an expert mathematician. Various software tools have been developed by experts in the field which can be purchased as per your requirements. 

Our cloud-based DataPandit software solutions is one such simple and user friendly interface developed by Let’s Excel Analytics Solutions.These softwares enable you to get appropriate insights out of your data and lead you in the right direction.

Data science can be learnt not just with theory but with hands-on experience. It can be said that Data Science is a habit, not a skill. The more you practice it, the stronger you get.