People often want to look to hard data to solve the most difficult issues in society. That seems preferable to taking best guesses at predicting the future.
With the legalization of recreational cannabis this past October, the world is looking to Canada for best practices and lessons learned, with regard to recreational and to the country’s longer experience to date with medical cannabis patients.
As the industry continues to evolve, big data is ever more critical in the process to best understand every step of the journey.
As the cannabis industry matures, it is likely more of a range in the data sets will become available. The cannabis industry has no shortage of anecdotal data, but patient-reported outcome data only matches real-world criteria with patient choices.
That said, today’s medical cannabis data lacks standardization. “If cannabis wants to play in the same sandbox as other medications, it really needs to be treated the same way,” Prad Sekar, CEO CB2 Insights, noted during the Cantech Investment Conference in Toronto on Jan. 29 to 30.
What does this mean for the world of data collection? Working with health professionals is critical, although with many being third-party physicians—those who work at cannabis clinics or services like video chats, for example—standardizing data sets can be a challenge.
Creating a standardized data set is the goal of many of today’s larger medical cannabis companies, as is meeting data integrity and data reliability. The key here would be for the major companies to unify their clinics and begin using stage one to stage four—studies assess the safety of a drug or device; studies test the efficacy of a drug or device; studies involve randomized and blind testing; and studies are conducted after a drug or device has been approved for consumer sale—as benchmarks for success.
Where do the opportunities lie?
Patients need a review platform to help them understand and learn from the empirical experiences of others
Data collection does not end when the cannabis is prescribed. Lift & Co. seems to take the view that patients still need a review platform to help them understand and learn from the empirical experiences of others.
This is where the company claims a large market opportunity exists, as evidenced by review platforms the company has launched over the past year. “The stigmas that led them to investigate and use cannabis, is just as important as their lifestyle data, and just as important as the medical strain data,” says Lise Dellazizzo, vice-president of data strategy at Lift & Co. “The conversations we have most are around tracking data along the patient journey. Its all the ancillary data around the purchase and user behaviour patterns that is the golden nugget of 2019,” Dellazizzo told attendees.
What are the key pillars of data?
For data to stay relevant, high volume, high velocity and high variety is needed. While in years past many industries had small data sets based on transactions, that is not the case today. The shift is now moving towards what type of ancillary data can be pieced together to truly understand who a company’s end-customer is.
In the cannabis context, for example, how much will strain data actually be relevant, if it appears as though the preferred forms of cannabis are edging towards topicals, oils and edibles.
Are trends similar on a global basis?
Although it seems like an ever-shifting battlefield, there are still some traditional marketing tactics that can apply to the cannabis industry. However, the greatest barriers to any market is the adoption of the clinical community because that is the bottleneck for people before cannabis can be understood by the wider community.
“Patients coming to market globally are small and slim compared to Canada and U.S.,” says Shawn Moniz, CEO of Cannvas MedTech Inc.
Will AI play a bigger role in cannabis?
Sooner then later, artificial intelligence (A.I.) may enter the cannabis picture. The challenge in A.I is that people don’t really know the right questions to ask yet. Data is only useful when it begins with the right questions. Looking for patterns through machine learning and observing what people are learning will be important. What are consumers’ likes, dislikes and lifestyle choices. This will give birth to a world of predictive cannabis analytics.