How Data Analytics and Reporting is Driving Value for Pharma Industry

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In today’s dynamic world, technology is advancing at a breakneck pace. Most industries have adopted disruptive technologies like Machine Learning, AI, Big Data, and Analytics. This has helped them reduce costs, enhance efficiencies, optimize their processes, and ensure customer engagement. The pharmaceutical industry is no exception. With an expected market size of $1,250 billion in 2021, growing at 1.8%, data analytics is adding immense value to the pharma industry.

In this article, we’ll share some crucial challenges that the leaders in pharmaceutical industry are facing. We’ll also list 8 important use cases and solutions for complex business challenges.

Data is a Crucial Enabler in Pharma Industry

The advancement of pharma data analytics in AI, ML, cloud computing, etc., assures many pioneering innovations. The insights generated help plan a fact-based strategy via big data analytics in pharma in the global market. Pharma data analytics offers many advantages to pharmaceutical firms, like performing advanced monitoring, improving the in-house processes, and in-depth competitor analysis with data-backed insights.

Data is considered to be the lifeblood for the pharma industry. From identifying patterns, gaps, experimenting with theories, and understanding the value of treatments, they rely on data. As a consequence, pharma analytics remains at the forefront of driving value among the pharmaceutical industries. The leaders starting their data science journey in pharma companies have a lot to explore and implement. 

Data analytics and big data support pharma companies in gaining access to more data and analyzing large volumes of data. Hence we can say pharma data analytics helps in generating insights presenting more prospects for the companies.

Takeaway: We build analytics and AI/ML solutions to tackle complex business problems of the Pharma industry. Check out our pharma analytics brochure to know our solutions in detail.

Pharma Analytics Use Cases

Drug Research and Development

One of the core strategies driving a drug company’s financial aspects is the drug discovery and development processes. This process is also one of the most time-consuming, demanding, and costly procedures. With the patents for blockbuster drugs expiring, the pharma industry seeks to fast-track this process of announcing new drugs in the market.

The process of drug discovery is highly complex, lengthy, and the costs involved are huge. As Forbes reported in one of their findings, the estimated cost of introducing a new drug is $5 billion. On average, it takes 10 to 15 years for the entire process of drug development to complete.

Data analytics can help pharma companies to speed up their drug discovery and launch process. Also, companies can extract the correct information quickly from the available literature. They can avoid blind spots in research, build actionable insights, and finally advance their decision-making.

Companies can benefit from predictive analytics to analyze more significant molecular and clinical data chunks to recognize compounds. The compound can become the next new drug based on different factors like the molecule’s chemical structure, targets, diseases, etc. This can save a lot of time and energy that companies would have used to screen potential compounds for drug formulation.

Time consumption in pharma drug research is one of the biggest roadblocks. We built an NLP-based literature mining solution for pharma biologists that sets up a relationship between target, drug, and disease. This use case of NLP in healthcare accelerates drug discovery and development

Clinical Trials Analytics

A drug’s reliability and efficacy for the targeted population are checked through clinical trials. The drug passes through several phases before it reaches the FDA for final approval. These phases are lengthy, time-consuming, and if extended, can result in heavy expenses.

Pharma analytics can help evaluate various data sources such as drug databases, health databases, scientific publications, public information sources, human genome, sensor data, social media, etc. The data generated can be used to select the correct target population for the trial. The authorities and the clinical scientist can use this data to arrive at conclusions and make more data-driven decisions to accelerate the clinical trial.

A vast amount of data is collected in the process of clinical trials with heavy regulations. Ensuring compliance and quality becomes a must here. For optimizing the clinical trial operations, pharma companies can use advanced analytics and big data for risk-based monitoring and site selection to enhance and optimize their clinical trial operations.

One of our leading pharmaceutical clients was having trouble manually counting the number of biological cells. It is a frustrating job for a scientist who so badly wants to focus on crucial research work rather than sit and manually identify shapes for days without end. Gramener’s Deep-Learning solution has automated the process, reducing days of effort to seconds & improving the accuracy to a large extent. We built a crowd-counting-based Deep Learning model that counts biological cells from microscopic images with an accuracy of over 90% and reduces hours of manual effort.

Cost-Effective Drug Manufacturing

The current processes involved in drug manufacturing are time-taking and very expensive. The pharmaceutical industry has been under tremendous pressure regarding its cost constraints and expenditure during manufacturing. But companies should also consider other factors like generic competition, increasing regulations, development models, reimbursement challenges, recruitment, and retention.

Some of the critical challenges associated with drug manufacturing are optimized batch production, ensuring the use of defined compound composition, packaging, logistics, etc.

Pharma data analytics can define the future of drug manufacturing. It can guide in overcoming the associated challenges. From reducing development times to cutting costs to developing and manufacturing new drugs at each step of the chain, data analytics, AI, ML, and robotic technology are pharma’s future.

Effective Pharma Sales and Marketing

Sales and marketing play an essential role in driving revenue for the pharma industry. According to one of the reports, pharma companies spent $6 billion on marketing in the US alone.

The complete pharma marketing ecosystem is much more intricate with numerous target stakeholders like physicians, patients, hospitals, etc. This requires effective targeting and an integrated and comprehensive strategy for accurate targeting.

Tracking the sales and marketing pipeline’s effectiveness and competence is of prime importance for pharma companies to compete. For more prominent companies, it is more difficult to track these performances. However, modern data applications and dashboards powered by pharma data analytics and predictive algorithms can aid these companies.

Pharma analytics supports better resource distribution and boosts sales and marketing efforts. Firms can analyze patient trends and data, sales, and distribution data. Also, these companies can check their performance against their competition and line up their sales and marketing strategies accordingly.

At Gramener we do analytics-driven Ad Stock Optimization to identify key indicators driving sales from different marketing mediums and channels.

Social and Search Engine Pharma Analytics

To remain in the race, every business should have an online presence. It is essential to have an eye on the competitor’s moves, check their social presence, keep up to date on public conversations on recent product launches, etc. One can have a lot of unstructured data through search engines, social media, and wearable devices.

Generating data and then performing analysis for pharma plays a vital role in delivering a product. By doing real-time research of this data, pharma companies can be alerted about new drugs’ safety and the associated health risks.  An online presence can help address the issues quickly so that the company’s reputation is not affected.

We developed a digital marketing dashboard for one of our leading pharmaceutical clients. The solution-focused on illustrating the quarterly sales trends and PubMed mentions of their competitor pharma companies. Moreover, the solution derived data from search engines to build word clouds and charts for popular phrases the competitors ranked for.

Compliance with Regulatory Bodies

Failing to comply with regulatory laws not only harms the drug maker’s reputation but can also result in paying huge violation charges.

Advanced analytics in pharma can help drug makers operating in complex legal environments and multiple geographies to uncover insights. This will help streamline governance judgments and find the gaps in the safety and efficacy of current drugs. In order to save time and effort, companies are beginning to use data anonymization tools or NLP in data processing. The advantage of using these tools is that they ensure high quality data analysis while decreasing the amount of manual work required.

Apart from managing daily tasks, raising alerts on any particular issues and reducing the risk of compliance features can be easily managed with pharma data analytics.

Pharma Adverse Event Analytics

Every new drug is developed to fight diseases. But these life-saving medications have side effects or adverse events on the human body. Pharmaceutical companies are spending considerable amounts to track and overcome these unfavorable events.

Pharma analytics can predict adverse events (if any) before they become a reality. Adverse event reporting analysis helps pharmaceutical companies to ensure the safety and effectiveness of a drug before marketing. Pharma companies can thus improve and re-optimize the entire process. In short, we can say life science analytics, external indicators, and big data analytics in pharma are contributors forecasting the risks. 

We used the Treemap visualization technique to visualize the adverse events reported by patients. The solution shows two dimensions – Size & Color, where size varies with the total number of reports of adverse events. It could be Deaths, Life-threatening injuries, Hospitalization, Disability, and many more. Whereas, color shows the percentage of severe reports amongst the total adverse events.

Check out our detailed analysis on adverse event reporting and see the surprising insights we were able to locate from the sea of FAERs data.

Targeted Patient Population Analytics

Pharma analytics are crucial tools for pharma marketers, allowing them to connect the power of both real-world and traditional data. Data analytics can help pharma companies analyze patient data and trends, sales, performance and align with their sales and marketing strategies. Healthcare organizations can successfully use data analytics to categorize or target populations as per the disease burden or health risks.

We Specialize in Building Pharma Analytics Solutions

Analytics is essential for pharma companies, and most of the players in the field have realized its need and criticality. Companies are and have already made notable investments in these technologies, tools, and people. They have acknowledged areas wherein they can harness the power of pharma data analytics for stellar results.

At Gramener we’ve travelled long roads with clients such as Novartis, GVK Biosciences and DRL to build data and AI solutions. We’ve tackled complex problems such as automated cell counting through AI, insightful dashboard reporting, optimizing tablet manufacturing with low-code applications and more.

Connect with us for a quick 30 minutes call and allow us to hear your problem. We promise to deliver a sure shot solution with a robust Proof of Concept (POC).