The definition of data analytics (also known as data analysis) is the qualitative and quantitative processes which improve productivity and business gain. Analysing data in different ways allows patterns to be identified and behaviours to be noticed. The way data is extracted is different, related to what is being focused on or researched.
The history of data analysis can be linked as far back as the 19th Century and is associated with Frederick Winslow Taylor, referred to as the “father” of scientific data management. He wrote The Principles of Scientific Management in 1911 and his implementation of “Taylorism”, striving for optimum efficiency by analysing the most productive methods of manufacturing and time management. Henry Ford is also cited as being influential in data analysis for his focus and success with the speed of automobile assembly in the 1960s.
Fast forward to the advent of the internet and to the Economist reporting in 2017 that data is now the world’s most valuable resource – it is no longer oil. They describe data as a new commodity which “Spawns a lucrative, fast-growing industry, prompting antitrust regulators to step in to restrain those who control its flow.”
With the growth of big data, the cloud, data and warehouse storage, the variety of information is becoming vast and complex. In A Brief History of Analytics by Keith D. Foote, he breaks down the different data analytics we have currently (and this is growing exponentially). The list as it stands is: Predictive Analytics, Big Data Analytics, Cognitive Analytics, Prescriptive Analytics, Descriptive Analytics, Enterprise Decision Management, Retail Analytics, Augmented Analytics, Web Analytics and Call Analytics.
The focus of Pharmaceutical Data and Analytics drills down to another layer of complexity and includes mining data on clinical trial management, competitor intelligence, market research and predicting the market, drug development, approval of pharmaceuticals, pricing, biotech, healthcare trends, tracking of products and overall pharmaceutical statistics.
Here are four specific ways in which we’re seeing pharmaceutical companies apply data analytics to improve productivity and profit.
- Boosting Drug Development
As physician and scientist David Shaywitz notes, “Car companies know how to make a car, soft drink companies know how to make soda, yet drug companies really have no reliable way of knowing where their next products are going to come from.” So probably more than other industries, pharmaceutical companies can make exceptional use of data analysis. This would aid in bringing drugs to market more quickly as well as to “kill” R&D projects which are unlikely to be approved – saving huge amounts of money.
Big data knowledge can also help in terms of visibility of second line products. Oracle & Science writer Tatiana Sorokina points out; “As more treatment options lose patent protection and become generic, it can be harder for second line products to compete for the attention of healthcare professionals.” She uses the example of oncology when market leaders in the first-line targeted therapies went off patent with the result of a more lines of treatment per ailment and physicians receiving more information.
- A Deeper Understanding of Patient Behaviour
As more data on patients is gathered, pharmaceutical companies can mine information to tailor-make their products and services to meet the customers’ behaviour and needs. Details about patients can also be analysed in terms of demographics or high risk patient groups which would help in the direction of better treatment for patients. As Jon Markman writes in Forbes Magazine, “By studying the genetic profiles of patients with diseases, scientists are starting to understand what makes humans ill in the first place.”
Big pharmaceutical companies are strategising their future with a heavy focus on data analytics. Last year, for example, GSK invested a US$300 million investment in a “personal genomics” business in Silicon Valley which has collected the data on five million people. Big Pharma are teaming up with the innovative tech companies which build the machines and processes genomic information, especially those who get data from diverse areas, including social media, demographic information, insurance claims and electronic medical records.
- Speedier Diagnostics
Though big data solutions based real-time analysis has arrived. Trials can now be monitored live which means faster safety implementation, quicker knowledge on price and less delay in treatment. In the case of a speedy diagnosis with data linked to R&D trials there is more likelihood of positive results for patients participating in trials.
According to Daniel Faggella, CEO of Emerj, in the next ten years there will be more utilisation of micro biosensors and devices, mobile apps and more in-depth health-measurement and remote monitoring capabilities. This will be an additional injection of data and improved speed and personalisation of treatment from the pharmaceutical companies.
- Shared Trials
Not only are partnerships growing between Big Pharma and tech but the pharmaceutical companies are also knowledge sharing between each other – particularly in the area of clinical trials. As Jennifer Miller in the Pharmaceutical Journal notes, “Disclosing clinical trial data is a step in the right direction towards transparency, which benefits both the public and the pharmaceutical industry.”
Miller goes on to note the benefits of the deeper data analytics being shared, these include; “Helping appeal to socially conscious investors; keeping activists and reactive regulations at bay; attracting consumers, partners, and talented employees; boosting the morale of existing employees; and protecting the value and safety proposition around an industry, institution, service or product.”
The future of pharmaceutical companies and data analytics will be as innovative as self driving cars or The Internet of Things. In a research study by the Deloitte Centre for Health Solutions, analysts envisage radical shifts in the near future. In fact they say that by 2022, medicine will be “predictive, preventive (based on risk), personalised and participatory”. The impact of this will be seen as greater agility with regards to new market inroads, lower costs, more efficient drug trials and a much-improved service to patients and customers.
Eben Esterhuizen, General Manager, OnShelf Pharma
Note to the Editor:
OnShelf Pharma’s foundation is built by an FMCG specialist and has a culture of tenacity with a smart solution oriented approach. The business has achieved phenomenal growth to become the preferred healthcare sales agency in the healthcare sector.