Business Analytics: Helping Healthcare Industry In Decision Making

There is a huge gap between demand and supply for skilled professionals for data analytics. People with domain knowledge with the needed technical skills are rare and in demand

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Business analytics is one of the most promising organisational processes within the Healthcare and Pharmaceutical industry and crucial for any company that wants to preserve its competitive advantage in the market, where most of the organisational structures are already struggling with the right skills and knowledge to fully support existing business needs for storing and processing and even analysing information.

Business analytics is playing a huge role in helping companies taking informed decisions, within different therapeutical areas, markets, and regions to reach up to decisions within the stipulated timeframe, and get exposed to real-world insights from competitors, payers, regulators, patients,  etc.

Data analytics and business intelligence are used to empower decision-makers who are championing life-changing and life-saving innovations for people worldwide.

Data analytics is helping companies in various ways:

Reducing Research and development cost

With the cost to launch a new drug in the market surging and the patents high selling drugs expiring, there is a need to expedite this process.

Here pharmaceuticals analytics can majorly benefit in shifting large data sets of research papers, publications, and scientific information by aiding in predictive algorithms and contributing in making decisions to escalate the process of finding and innovating new drugs.

Better clinical trial development and outcomes

The use of big data technology in the pharma/healthcare industry sure can reduce costs and increase efficiency by accelerating clinical trials, analysing and identifying a large number of data points like historical, patient monitoring and demographic data. Also, enhancing the process of examining clinical trial events and making disease diagnosis efficient while also designing more well-organised groups.

Precision Medicine

In today's world where there's so much data, it becomes challenging to handle complex data in the pharmaceutical industry.

So, big data analytics can help solve this problem by combining data from various mediums like medical records, medical sensors and genomic sequencing to identify patterns and create need-based medications for patients. This also contributes to the learning because of direct access to real-world evidence.

Providing directional insights for better sales and marketing strategies

Data points help in understanding new markets, as well the sales reps' performance by adopting new technology. It helps analyse various marketing channels and make quicker decisions, witness the effectiveness of big data analytics in the pharma/healthcare industry.

Data is growing exponentially, and speed matters. In today’s competitive market, it becomes vital to have cutting-edge technologies – with agile database management and insightful market research. The best part of data analytics is predicting the future, by using historical trends/data. As per the industry need, various professional skill is needed to fulfil the demand. Various skills have been identified for data analytics like:

  • Data structuring

Organising, managing and storing data creates efficiency. Data is used everywhere in the pharma/healthcare industry. To evaluate medicines, their future usage, potential in the market, clinical trial funding and much more. Data structuring creates data-oriented formats to organise, process, retrieve and store data in different types of structures to access and work with them in ways to enable efficiency.

  • Data mining

The processes of finding and discovering patterns in large data sets. With the use of algorithms and modelling techniques patterns and relationships in data are found aiding to accurate predictions in R&D, marketing and problem-solving in clinical trials. Using various tools like clustering, associating segmentation and classification data is manipulated to improve the quality of drug development and delivery methods.

  • Artificial Intelligence / Machine Learning (AI/ ML)

AI/ML is now being used to manage the huge amount of data that is being collected to mine and interpret them on being discovered. AI uses human intelligence such as predicting, problem-solving and learning with the help of ML algorithms. Nowadays pharma/healthcare industries across the globe are using AI and ML to facilitate drug discovery and solve challenges linked with complex biological networks.

  • Visualisation

Representing data or information in a graphical or pictorial format is used quite a lot in data analytics to make it easier to read the trends, outliers, gaps and patterns in large data sets. Data visualisation like timeline and network visualisation aids analysts and clinicians to join the dots, making better and quick decisions, cost-efficient, avoiding duplication

There is a huge gap between demand and supply for skilled professionals for data analytics. Also, people with domain knowledge, plus technical skills are rare in the space. Hence there is a huge need for training professionals on these skills. Various online portals have come up with such training programs, but they are not industry-specific, they are generic. You need to understand pharma industry-oriented data, before you apply the technical skills to that, to get the insights. A proper skill mix is needed for the data analytics job; hence scalability will always be a challenge, which can be solved by pharma business analytics focused training programs.


Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house

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business analytics data mining healthcare pharma

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