Data Analytics in the Cannabis Testing Realm

Big Data Meets Big Cannabis

Data Analytics in the Cannabis Testing Realm

Data Analytics in the Cannabis Testing Realm

At first glance cannabis-testing labs narrowly and exclusively provide a service to cannabis growers, producers and dispensaries through supplying important potency values and quality assurance data on products prior to patients’ access, purchase and subsequent use. Beneath this primary quality assurance focus of cannabis testing labs, which combines customer service with topnotch certified analytics, is the unapparent value to be gained from data analytics.

Cannabis testing labs are the unique possessors of vast amounts of data on the chemoprofiles for hundreds of individually analyzed cannabis samples, many of which are for the same strain collected from the same grower over time but also from different growers using different cultivation approaches. At DigiPath Labs, those chemoprofiles includes 11 (soon to be 17) individual cannabinoids and 22 terpenes. Furthermore, DigiPath Labs in collaboration with Medicinal GenomicsTM is now able to add strain genotyping data to link with chemoprofiles to eliminate the sometimes arbitrary nature of strain naming. Interpreting chemoprofile data for a particular strain over time for a grower is valuable information; further analyses of strain chemoprofiles by varying cultivation techniques is also serviceable intelligence. Beyond potency, through data analytics are interesting findings with regards to the microbiomes supported by individual cannabis strains, including both innocuous microbes as well as pathogenic to the plant.

At DigiPath Labs we are shaping our performance through analytics and improving our service to our clients by building our brand around data, just as top-performing companies in other industries build on their business intelligence by deploying statistical data analytics to identify advanced utility from the data they collect, eliminating noise and taking a focused approach to analyzing useful data to reach practical intelligence. This business approach requires a strategic plan for collecting, organizing, storing and identifying value for both de-identified data and client-specific data. Data solutions will ultimately require a collaborative approach where real-time data analysis becomes a storyboard where data insights, observations and discoveries are shared. An expanded communal approach to data mining requires strict governance on who can access data, what can be done with it and checking the integrity of updates.

The opportunity to draw insights from cannabis testing data is here. Achieving expertise in data analytics requires capability and organization. The competition to be an effective top-performer in data analytics is real. The story on what it will exactly look like and who will control it is just beginning, but the utility, marketability and value of such data analyses is evident.

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