Institutions need information in order to make decisions. A big part of the information they need to measure and predict outcomes is in the data, but real information is the convergence of that data with sound analysis. Integrity of the data input is critical but so is sound analysis of the output. Government, academic and corporate institutions all have different beneficial interests in this process of input and output.
Government institutions will have large amounts of data input from the sales tracking used to regulate and collect taxes and a huge need for interpretation and analysis of that data. Each state has a different system in its “seed to sale” tracking which need to be standardized before analysis can occur.
Academic institutions have no real input and the only output they will use is for research. They will give much more than they take in this scenario. They have the least stake in any beneficial outcome other than intrinsic and intellectual. This gives them a very neutral stake in the process and makes them uniquely qualified to manage the data aggregation, integrity and availability.
Corporate Institutions via POS and ERP software systems and middle ware providers will have significant input into economic modeling, especially for the retail sector, but most corporations will just want to get their hands on the reports and outcomes. Gartner and Forrester are not exactly covering the marijuana industry yet. All the analysis that needs to be done by financial institutions, investment banks, agriculture, retail and real estate can be measured out of this data.
Roles and Responsibilities
The data gathering side is a huge undertaking that will not come at a small expense and creating multiple data repositories is, frankly, insane. The data needs to be gathered and scrubbed, then merged. It is a huge deal. Not only do you have multiple states using multiple seed-to-sale tracking systems, you also have a proliferation of POS and ERP systems entering the space. Every system calls their fields something else even though they have the same data. You have to map hundreds if not thousands of fields to one another.
Once the data is scrubbed and hopefully automated it needs to become anonymous. Not just to protect the underlying sources but also to eliminate any bias in the analysis. Finally, the data needs to be stored and secured and served up for people to access.
All that complete, we now have responsible analysis of the data. So far the only two neutral studies I have seen in economics are the Harvard and MPG studies I mentioned in a previous article. They just report and predict outcomes, but they do not advocate for or against. I would expect with proper data many of the institutions will be able to assign an analyst to do studies and data should be made available to them, likewise some analysts should come from the other stakeholders as well.
Clearly the neutrality of academic institutions, the significant input and need for output of the government and corporate institutions all need to work together to make this happen.
In the meantime, while all of this falls into place and dominant players emerge, institutions will be slow to adapt and opportunities will continue to exist for non-institutional players; however, navigating the minefield on limited information should be met with extreme caution.