Data Analytics for Utilities

Using data to understand the effectiveness of treatments, test hypothesis that become theories and identify patterns has always been a go-to for the pharmaceutical industry. For centuries, data analytics for pharma has propelled us forward, allowing for drug discovery, development, production and so much more.

As technology advances, data analytics for pharma has advanced with it. In the 15th century, for example, the invention of the moveable printing press arguably accelerated healthcare forward. Now, in the 21st century, artificial intelligence allows for the progress of big pharma faster than ever before.


How are Data Analytics for Pharma Utilized?

You may be wondering how pharmaceutical companies currently use analytics. The digital transformation in pharma has arrived in several facets. It improves virtually every aspect of the healthcare industry. We want to look more in-depth at how data analytics for pharma is utilized, and how it can be used better as technologies advance.

Reduce Cost of Drug Discovery and Development

Trying to introduce a new drug to the market can be a costly experience for pharmaceutical companies. The patents for blockbuster drugs are expiring, and the price for new pharmaceuticals continues to skyrocket. Fortunately, observing data analytics for pharma helps to accelerate the process of bringing a drug onto the market.

Through machine learning and other AI technology, we can sift through vast networks of data much faster than with the human eye. Predictive analytics run through these massive swathes of data to allow a pharma company to make more intelligent, informed decisions. This new process of data discovery unlocks improved financial performance for big pharma.

Improve Efficacy and Better Track Clinical Trials

Data analytics for pharma can also reduce the time and cost of clinical trials. First, the algorithm sorts through data from past clinical trials, a participants’ historical and demographic data and remote patient monitoring data. Then, pharma companies can design more efficient clinical trials by optimizing control groups and identifying test sites with high patient availability.

Target Medications for Personalized Patient Experiences

Every human has a unique biological makeup. Technically, medicine should be personalized based on that genomic makeup. This was something considered impossible on a mass scale before artificial intelligence in pharma.

Now, data analytics for pharma can comb through data like electronic medical records, a patient’s medical sensor data and even genomic sequencing. With this data, healthcare organizations can spot patterns that build a personalized, more effective medication and plan for patients.

Manage Safety Concerns and Predict Health Risks

The age of digital transformation brings forth personal wearable devices, social media posts and Google searches that can be used as big data. This helps pharma companies know sooner about the product safety of new drugs as well as potential health risks. That means improving or eliminating side effects and identifying risk factors before they ever become a reality.

Increase Drug Utilization and Affordability

The world puts increasing pressure on the pharmaceutical industry to increase its efficiency in every stage of the process. By using data analytics for pharma, these pharma companies can reduce costs and increase revenue with smarter decision-making.

Key metrics include the percentage of total drug spending as a rebate, the individual cost of each drug ingredient and even savings per member per year of each drug utilized. Through AI pharma, these can all be made more efficient.

Data Analytics for Pharma on Social Media & Search Engines

How a drug is received in the eyes of consumers plays a massive role in pharmaceutical success. And how a drug gets received is broadcasted online through social media networks and search engines. Pharma companies can tap into this info being broadcasted and use it to make real-time decisions about a product or drug.

Healthcare organizations can use a deep learning algorithm to scrape through internet data on any given topic. From there, they can find safety-related information and other vital knowledge that may have been otherwise overlooked.

Evolve Marketing and Sales Effectively

Through big data, pharma companies can identify new markets and analyze current marketing channels. This helps them increase efficiency and prioritize efforts that allow them a competitive edge. Thanks to this data, organizations can witness improved marketing strategies that are more effective and less costly.

Streamline FDA Approval and Legal Compliance

The pharmaceutical industry is plagued by some of the most stringent of government regulations that increase more each day. Data analytics for pharma can uncover novel insights that help streamline compliance of governing regulations. It can highlight gaps in the safety of drugs, which helps to accelerate FDA approval.

Enhance Quality Control and Improve Operations Overall

Pharma companies can also reduce costs by lowering downtime reduction and focusing on data analytics. They can improve existing operations through data insights and pharmaceutical analysis. With this data, pharmaceutical organizations can predict substantial changes in demand, as well as other risks, like machine failures and quality issues. 



How do pharma companies get data?

Pharma companies use surveys, interviews and experiments to get industrial data for analytics.

How do pharmaceutical companies use big data?

Pharmaceutical companies use big data and data analytics for pharma to predict which drugs will be successful.

Do pharmaceutical companies share data?

Some healthcare organizations do share data. In fact, four of the largest pharma companies, Johnson & Johnson, Novo Nordisk, Novartis, and Roche, have data-sharing measures in place. However, on the other hand, some pharma businesses don’t have any data-sharing policies, so they are not sharing data with other companies currently.