TLDR: While collecting data is important, the form of that data is equally, if not more, important. The acquisition of Geotic by Goldspot provides major value in the realm of a consistent data interface and formatting and facilitates the launch of Goldspot’s R&D projects that it has developed. Don’t sleep on the Geotic deal!
Goldspot announced the acquisition of two companies on the same day back in the third quarter. While its purchase of CEO.CA got the bulk of the attention (understandably so), Goldspot’s acquisition of Geotic remained under the radar. Geotic was purchased in a cash + stock deal for $2.5mil CDN, which appears to be a very respectable price for a company with fully-developed software products and by all indications is currently cash flow positive.
When the Geotic deal has been talked about publicly, it is mostly mentioned as an avenue for Goldspot to further develop its SaaS business model. While it certainly will assist in doing that, there is also another less talked about benefit to the addition of Geotic that has been overlooked - data homogenization. (**I provide a simpler personal example of the importance of data homogenization I encountered in my work at the bottom for those that want to better understand the concept**)
While the only place most people encounter the word “homogenize” is on a milk carton, homogenization of data is of tremendous importance to any company that has a service model based on the delivery of data. In Goldspot’s case, the data delivery comes in the form of prioritized drill targets, resource estimation models, geophysical surveys, geochemical reports, drill results and so on.
While all of the data collected can be used for specific acute purposes, one of the overall values of Goldspot is its unique capacity to integrate and stack these various data flows together to provide increased accuracy and higher quality overall outcomes. While there is value in using multiple processes in a similar phase of the process (for example making analysis based on geological structure, geophysical surveys, and geochemical report in the targeting stage), there is an additional benefit when data collected and analyzed at one stage of the exploration process can be fed in and analyzed automatically by another step in the discovery/estimation process.
This is one place where Geotic excels. Its software product line (GeoticField, GeoticLog, GeoticGraph, GeoticCAD, and GeoticMine) provides an interface covering the entirety of the exploration/development process from the very first field samples to the engineered mining plan. By having a full suite of software, this means data collected in the early phases is already pre-formatted in an easily usable form for a later step in the process. On top of that, when the data is collected and stored, it is done with specific consideration of its intended final use.
This is of particular importance to Goldspot’s long-term model because of the 40+ R&D products that it is developing, including Litholens, GeoFez, MinusOne, SomSpot and so forth. All of these products are trying to collect and process geotechnical data in some unique way. But what every single one of these products need is an effective interface for its data and results to be utilized by the end-user. Because the Geotic software suite covers the full span of the exploration process, the software provides an interface for all of the 40+ R&D products that Goldspot is developing. Even the most powerful products and tools are only as good as its usable interface. So, if you are excited about all the new products that Goldspot has been developing these last several years (like Litholens), then the Geotic acquisition should be of particular interest to you because it is one of the major keys to unlocking the full capacity of these R&D projects.
The final reason that the Geotic acquisition and improved data homogenization should be exciting to you is because I believe it begins to build one of Goldspot’s strongest moats. Once data is already integrated into the Geotic suite, there are no additional steps needed to import and export it for further steps in the exploration process. There is an incredible amount of time and resource savings that is derived from having all the data that you need without any need to do a data import or performing any modification to the data to make it usable by the subsequent tool. Once a client has started utilizing Geotic at the field survey stage or drill targeting stage, the efficiency gains from remaining with Goldspot and Geotic is substantial. This built-in incentive to continue working with Goldspot begins to create a well-formed moat for the company that distinguishes Goldspot from all of its potential competitors. Goldspot currently leads the pack in the AI/Machine Learning aspect of mining exploration due primarily from being first to market and its high quality personnel. Unfortunately, those advantages don’t always form a lasting moat because technology can quickly catch up and personnel can jump ship. But having clients tied into a software suite that integrates all their data and exploration work together forms a far heftier and lasting moat for Goldspot.
So while the Geotic acquisition may not have garnered as much attention as the CEO.CA acquisition, those of us that follow Goldspot closely should not sleep on this transaction. All indications are that it will form a key and essential function to the long-term growth of Goldspot into the multi-billion dollar player in junior mining exploration that we all envision.
Non-Mining Example of Data Homogenization and Why It Is So Valuable
**If you are already familiar with data homogenization, this will probably be too basic, but if not, I hope this example helps better explain what data homogenization looks like using things that most people are familiar with.**
A few years ago, I was working for a company with well over 1,000 employees that also had a substantial amount of employee turnover. Because of the constant churn of employees, we noticed that there was tremendous time and money wasted constantly inputting employee data into various systems. For example, we had recruiting tools where applicants would first input basic personal and contact information. Then we had an interview process that usually involved having applicants also fill out a formal application. Information from the paper application was then hand-entered by a Human Resources employee into the onboarding system (I-9’s, W-4s, etc.). Our safety department would also hand-enter in employee data regarding employee training and certifications. Then the finance department would hand-enter all the employee data into the payroll system. Then we also had to hand-enter data to be sent to our 401k provider, as well as to each of our employee benefit providers (health insurance, dental, AD&D insurance, etc.) each year. When we looked at all of these various processes, it was crazy how much of the data being hand-entered at each stage was just a repeat of information that was already collected at an earlier stage - well in excess of $200,000 was wasted on repeat data entry each year. Instead of hand-entering all the data at each step, we looked at what it would take to pull information collected from prior processes and auto-populate each across all of these diverse systems and processes? That way, when an employee goes to fill out a form (for example signing up for health benefits), before they begin the form, all the blanks for name, address, DOB, SSN are pulled from their job application. Instead of having to fill in their spouses name and telephone numbers, the information that was previously provided on the employee emergency contact sheet could be used. And so on and so on. By using this approach, specific information would only need to be entered once and then every other time it needed to be used, the system would automatically refer to the prior data entry and pull the data from there. If there was an error or change in employee data (say a telephone number change), it only needed to be changed in one place and every other document would automatically be updated.
This was all resolved by creating a master database in which each employee had an individually assigned database record that was utilized by each system throughout the entire lifecycle of their employment with the company (from first application to conclusion of employment). But that is not the data homogenization problem, that is just a database/data storage issue. Data homogenization involves looking at every single process where a piece of collected data may be utilized and how it will be utilitized. For example, an employee phone number. This information was being collected by the very first recruiting system, and honestly, for that function it didn’t matter what format it was in - it could be “xxx-xxx-xxxx” or “xxx.xxx.xxxx” or “(xxx)xxx-xxxx” or “xxxxxxxxxx”. No matter what form it was in, the recruiting team could figure it out and contact the applicant. But what if the employee health insurance company had a system that required the phone number to be imputed only in the form “(xxx)xxx-xxxx”? If you are aware of that, it makes it crucial that when that information is first collected (in the recruiting system), that it is done in a format that it can be functionally used by all subsequent processes without any need for human intervention. Remember, the key is reduction of human time and resources in accomplishing tasks. As we rolled out this system, it became very obvious that simply collecting “employee data” was not enough. It does no good to have data if it is in a format that either can’t be used or takes up unnecessary resources to access it.
Even when looking at something as simple as collecting employee data (names, addresses, telephone numbers, dates of birth, SSNs, family members, etc.), data homogenization was a huge component to making the integrated processes all function seamlessly. With that in mind, now think about complex geological data from numerous sources (geophysical surveys, satellite survey maps, field survey samples, drill core test data, geochemical report associated with various field and drill core samples, and so on and so on). Not only does it involve an incredibly high degree of complexity at each component, but each of the components are each individually unique and differentiated. The Geotic acquisition is big for Goldspot because it shows that the company will be engaging with promising mining exploration companies throughout the entire exploration/discovery process and guiding them throughout with needed tools that work together effectively. That level of involvement provides unique value to Goldspot’s clients as well as Goldspot’s shareholders.
