Intelligently Transform Your Data into Innovation for Results

If you’re like many Pharma companies, you’re drowning in customer data. You know their buying habits, you’ve segmented based on past behavior, and you may even know the outcome of your medicines. The problem isn’t a lack of information. Rather, it’s knowing what to do with that information in order to get excellent results. In other words, you need context for all that data, and that is where Artificial Intelligence (AI) shines. AI can sift through trillions of data points and identify opportunities to provide improved service to your customers and, ultimately, enhance revenue.


Use AI Strategically
However, it’s not the AI itself that’s the solution, but how you incorporate it. Like most technology, it’s a sophisticated tool. It’s one thing to capture the data, yet using it for a competitive advantage is another matter. The key to using this successfully is not simply data mining but ensuring you are being strategic. One must always start with an objective or a challenge to solve. We see so many companies just wanting to combine data to “see what’s there”. You can do that, but it is far more intelligent to strategically analyze your challenges and objectives first, figure out what data would be best to capture to achieve these, and then capture and create the right algorithms to solve the challenges. You will always find things you had no idea you were looking for as that is the nature of the game, but you should always start with a plan. You don’t get the information you need simply by uploading your data and pressing a button. Rather, you need to have a strategy, find the right data, clean the data, and then choose AI techniques to apply that are appropriate for that data combination. It helps if your AI company understands the Pharma industry because then they can guide you. Over the past few years, I’ve noticed several large Pharma companies engage with well known big data AI companies. While these AI companies are well-versed in Artificial Intelligence, they do not specialize in Pharma. After a year or so, the project fails, the Pharma company turns to Eularis, and then we get a successful project out of it.

What is the lesson here?
There’s a similarity in each of these failures, and one thing we’ve learned is that AI companies who don’t have a Pharma background are missing key insights to guide their questions and strategies so that you get actionable insights. Strategy is key. Here are four considerations to ponder when bringing AI successfully into your mix:

1. Ask Intelligent Strategic Questions
It’s certainly possible to simply integrate the data and get information. However, that doesn’t provide you with the information you need to have a truly competitive advantage. Rather, you want to use your industry knowledge and strategic thinking about the Pharma world to ask the right questions in order to obtain the data you need to solve problems. Data scientists are not strategists. They are experts in data science, which means they can deliver and analyze data but are not necessarily thinking about it from a strategic point of view in order to ask the right questions. You’ll get more value from the data if it’s the right data to answer your strategic question. When you think about what challenge you need to solve, or what outcome you need to achieve, you’ll ask better questions at the outset, use better data for that outcome and, thereby, get better information to meet your needs. You will always need a strategic team who can apply Pharma expertise to all aspects of the business and use that knowledge to define the critical challenges you need to solve and objectives you want to meet, as well as optimal questions to ask of the data. Many AI companies are VC funded and may have one or two ex-Pharma sales people in their employ, but do not have the real in-depth industry experience necessary to think through the strategic issues effectively. If the company you are working with is like this, you need to lead them in your strategic thinking and not assume that the AI itself is the answer.

2. The Right Data
Surprisingly, even large AI companies often do not think through the data they need properly. I saw a recent case of this in which data from a US hospital was utilized for a Japanese client. What many companies do not understand is how the Pharma industry works. Different therapy areas are dissimilar, with different drivers. You cannot possibly use the same data to solve all issues in diverse markets and therapy areas. It just won’t work, as many are finding. You need to think strategically about the data – what data do you need that is relevant and accurate to solve the issue you are struggling with? For example, you can find platforms pre-filled with data and you can add limited versions of your data into them; however, the results are not satisfactory as the data is pre-filled with data from one market, and some companies sell this to all markets. If they are selling the same things to all companies, the strategic thinking has not been done for specific companies’ issues nor created to solve their specific problems. It comes back to a company not understanding the industry thoroughly enough to think strategically about the specific issues.

3. Innovative Thinking
Assuming you’ve asked problem-solving questions, and have good insights, it’s time to apply the information. This is where having a digital native, fully immersed and up-to-date in all applications outside of Pharma, becomes invaluable. By having a combined team that includes deep Pharma experience and strategic expertise, with data scientists and digital innovators, teams can get the best results out of their data.

4. Believing in Magic
AI can seem like magic. Yes, all data can be ingested and transformed. Yes, algorithms can be written for almost anything if the data is available. What most do not explain is the amount of data cleaning and processing required each time to ingest data properly for specific algorithms. It is not a matter of click and upload. A study by Xplenty revealed that data scientists spent 50%-90% of their time cleaning up raw data and preparing it to input into platforms. They state that this explains why only 28% of companies think they are generating strategic value from their data. However, I think the reason many are not generating strategic value is that they are not thinking strategically with it in the first place but expecting the data to do all the work for them, and not actually doing the strategic thinking upfront to begin with.