Good Data In, Good Data Out

This may come as a bit of a shock for some, but more data is not always a good thing! More good data is a great thing, but more bad data is not. Let's start by defining good data versus bad data.

Before you can collect good data, you must define what good data looks like to your company. Good data has some common characteristics; the data should be accurate, available, relevant, reliable, and valid. Defining terms around what good data is allows your company to record, analyze, and communicate data in a more cohesive and efficient manner. This improved collaboration ability saves you time and money, whether you are managing processes with a cannabis SOP template or calculating the cost of good quality.

Bad data is everywhere. In 2016, IBM estimated that poor data quality costs the US economy around $3.1 trillion annually. It is safe to define bad data as the opposite of good data; that is data that contains entry errors, is inaccessible, stale, and untrustworthy. Can data start good and become bad? Absolutely. Can bad data become good? Yes, but it is less likely to happen the longer a process goes unchecked and bad data collection continues to occur. Issues like nutrient burn in cannabis cultivation can be traced back to inaccurate data, highlighting the importance of maintaining data integrity.

Now that we have an idea of what is good data and what is bad data, imagine a scenario where a company is using a paper-based quality system. The cultivation team consistently submits work records with missing fields. The recorded data is illegible, and these forms make up the batch record for a single batch over a 9-week flowering cycle. The quality team must retroactively compile this data as accurately as possible, while also documenting a deviation to investigate the data collection process. This situation results in bad data, which then costs another team, possibly at a higher labor cost, to chase down data that is likely inaccurate days or weeks later. When comparing batches to find opportunities for efficiencies or innovations, decisions are now based on bad data, potentially leading to issues with the accuracy of reports, such as those needed for a dispensary SOP template or a cultivation SOP template.

Let’s look at a scenario where a paperless solution is in place, and good data is being collected. A notification is received for a scheduled transplant. The activity record is created at the start, capturing the date, involved personnel, room location, and expected plant count. The system notifies that a team member does not have an up-to-date training record for the active revision of the transplant SOP document. The team member can update their knowledge while the rest of the team proceeds. Each plant is scanned through the system, confirming inventory in real-time. The work activity is completed and signed off, with the record immediately available for review. By making this record in real-time, the system has time to validate the data, allowing the company to leverage this good data to make informed decisions. Implementing the best seed to sale software can ensure that these processes run smoothly and that the data remains reliable.

Getting the most out of your data requires you to have good data. Having good data enables your company to make informed decisions, challenge assumptions, provide insights, expose levers that drive performance metrics, highlight the effectiveness of continuous improvement efforts, cultivate a culture of quality around day-to-day processes, and ultimately provide visibility to those that need it the most.