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Most companies can see benefits from upgrading their data approach:
But although data is one of the hot topics these days, rarely do we see it used in the real world. Time to change it!
I will show you how our clients use data to streamline their own processes and bring products faster to new markets. As I’ve been working extensively with pharmaceutical and life sciences companies, I will focus on this industry here.
To develop a single medication, we need to create up to 20,000 different compounds.
It can take around 12 years, and cost an average of over $1 billion.
Even a 10% reduction in these numbers can have a huge impact on the development process – and typically, you would already have all the data necessary to do it.
The R&D process starts in a laboratory, with many different chemical substances. Once properly researched and tested, the medication can be transferred to the production phase.
The substance should also be reviewed against existing production compounds, as part of quality and safety checks. The final aspect is the back office work, including financing, marketing, sales, HR, etc.
The entire process is very time-consuming and cost-prohibitive. By putting data to work, you can bring new medications to market faster, at less expense.
You can speed up the R&D process by analyzing the results of conducted tests at scale. This way you gain a deeper insight and detailed knowledge of molecular solubility, useful for creating new compounds.
Additionally, you can cross-analyze results from scientific papers. You can review clinical trials for information about the safety or effectiveness of a substance in a specific group of patients.
You can also assess the market potential of medicine by gauging future usage among different population groups. These insights can be gained by analyzing epidemiology and reviewing clinical guidelines and papers.
A central repository of compound data and clinical outcomes, when used together with cloud-based analysis, can reduce the time needed for analysis to minutes instead of days or weeks.
By centralizing a data source, you can also take advantage of machine learning models that predict certain trends in order to optimize production. This can give you insights into aspects such as molecular solubility, biological effects, and toxicity of a new substance.
Big data analytics can help you optimize your supply chain too.
Consider manufacturing – it’s not without its challenges, with multiple production lines, devices, etc. Any failure or mistake can be costly and even lead to some products being withdrawn from sale.
That’s why data needs to be ready to analyze if something goes wrong, so that you can react fast to any problems.
Additionally, with predictive modeling, you can get information about impending failure in time to perform maintenance with minimal downtime. Device information collected in real-time is especially useful for anticipating any equipment problems.
Finally, you can use demand forecasting to determine how much of a given product your organization needs to produce. Based on historical sales and external factors, such as seasonality, you can set up predictive models to forecast future demand.
According to Deloitte, 80% of organizations empowering all employees with data exceed their business goals. At the same time, around 70% of executives don’t know how to make the most of their analytical tools.
This means that even though most businesses now know how important data is, a majority of them don’t know what to do with it.
Data is one of the most powerful assets a company can use. Soon, we will see a massive change in the way enterprises consume and process information. To stay ahead of the competition, companies should start their data journey now, if they haven’t already.
How do you know your data is not working hard enough?
Maybe you have a centralized data source but still spend lots of time on confusing spreadsheets, or perhaps your production or sales estimates are done by hand and are not entirely accurate. If that’s the case, you could benefit from developing a data strategy.
Three steps to your data roadmap:
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