Part 1 – Introduction, Market opportunity, and some current risks & opportunities
Part 2 – Coming next, financial model considerations, how-to get started, managing risks
Introduction – Drone technology is changing our future. We can now digitize the physical world better, faster, and cheaper than ever before. Data that was historically captured by satellite or manned aircraft for $thousands and $hundreds, now costs just $pennies. Today we can collect information from places that were previously inaccessible or too dangerous for people to access. And that has opened a market that will help us solve many problems facing humanity today including food production, climate change, security, jobs, and more.
But much like the introduction of other transformative technologies, many businesses are going to fail along the way, and the Drone as a Service, (DAAS), industry is no different. In this article I will attempt to highlight some of the opportunities and risks of doing business in the drone services market.
How does Drone as a Service work? The DAAS business model can be split into three convenient segments:
Data Acquisition – The first step is for an operator to travel to the target location, set up his equipment, fly the mission, and collect the required data. Set up time and mission planning can take minutes or hours depending on the complexity of the location. Typically flights last 15-100 minutes and can cover small plots up to hundreds of acres. Weather, potential airspace restrictions, and privacy issues need to be taken into account and sometimes a second flight is required.
Post Processing and Analysis – Until you actually perform this step, many don’t realize the size and complexity of this task. Each flight often collects gigabytes of data and it all requires some post processing. Even a simple case like creating a short video and a few still pictures for a real estate sale requires editing down 10-15 minutes of video into a 1-3 minute highlight with lead-ins and post video closes and a little Photoshop work to bring out the best quality of the pictures. On the other end of the scale, it’s easy to collect 5+ gigabytes of data in a single flight over a 500 acre farm that requires hours of processing that cannot practically be done on a PC. While there is a growing base of analysis software coming to market, we have very little history and almost no hard data on ROI today. Who is to say that the wonderful NDVI analysis helped increase the farmer’s yield by 10% or was it because it rained at the right time? Proving hard cost savings and higher sales is going to take a few more years to validate.
Interpretation and Consultation – This is where the key value proposition resides. There will always be a place for subject matter experts to provide value-added services to their clientele. In the DAAS case this is particularly true right now. Most of us are trying to interpret information captured from above and correlate it to measurements taken on the ground. Building a strong business relationship with a customer can lead to a long term collaboration. With today’s virtual communications and data sharing capabilities, the productivity to perform this service can be maximized. That said, every effort should be continually taken to automate analyses because labor is not easily scalable. From our recent field research, providing quick turn around on missions is of key importance to end customers.
What data is being collected? – By far and away the biggest use of drones is to capture optical data from high resolution still and video cameras. Today we can fly 1,000 acre crop in a single flight and see the leaves on every corn plant. However as sensors become more miniaturized, there is growing use of thermal imaging, Lidar, chemical identification, and other applications. For example, today we can fly over a herd of cattle, count how many are there, identify which ones have an elevated temperature, and determine the quality of the grazing pasture. In addition, data is being collected from farm machinery, static monitors, and other sensors and integrated with drone captured data. In the future, combinations of data from multiple sensors will add a whole new dimension to our analysis capabilities.
How big is the Market? – A number of studies indicate that the commercial drone market will grow at double digit rates for the next ten years and total anywhere from $5B to $50B+ in the next decade. While none of these estimates are very solid, we all believe that each of the market verticals mentioned above represents a multi-$B opportunity.
What are the risks? The truth is starting any business is difficult and most fail. You need to have special skills to be successful and particular expertise when working in a rapidly changing technology centric market. If you wanted to start an auto sales business, there is quite a bit of history and best practices from which to draw, but the drone business as a whole and the DAAS model in particular, have never been done before. Even the most obvious example of drone assisted crop scouting still needs to be proven to make money for the Agronomist. Here is an article I wrote on this topic a while ago:
Here are a few of the risks that I see on the horizon in general and at the end will comment on DAAS risks as I see them today.
peak and begun to do the earnest block-and-tackling value creating development that will be required to build a sustainable business. The big question that still needs to be answered is: “What is the ROI? How much $$ can I save? How does it improve my customer service?, etc. Unfortunately, most businesses have very little operating history and only a few data points to prove end user value. So the companies that are focused on applications that yield quantifiable ROI will ultimately be the survivors.
Regulatory and related risks? – Unlike many other emerging technologies, the drone industry has to operate in a highly regulated environment. The overriding concerns are ensuring safety and privacy. Many countries like Canada, France, Australia, and England have established rules for commercial use of drones. Most require some sort of certification of the operator, offer common sense restrictions about flying in congested areas, near airports, etc., require liability insurance, and address safety and privacy issues. Others, including the US have yet to publish final rules.
Right now key restrictions under debate include flying beyond visual sight of the drone, collision avoidance technologies, night flights, and flying over populated areas such as events and sport stadiums. Until either the technology improves or the regulations are changed, it will preclude widespread use in infrastructure inspections, search and rescue, and other applications. Anyone contemplating doing business in these areas will need to be ready to quickly change their business model as both technology and regulations evolve.
What are the disruptive DAAS risks on the horizon? I see two primary drivers at this moment (one positive and the other negative), the very low cost of collecting data for fun and business, and the relative difficulty in using the technology. So what is going to change the DAAS business in the near future?
Labor is expensive and not easily scalable – Each of the three segments of the DAAS model requires trained labor. Whenever labor is involved quality and timeliness become important factors that have to be managed. Further, using contract labor such as an Uber model, only complicates the management challenge. And finally ‘prosumer competition’ (ie hobbyists monetizing their sport), will need to be considered. Businesses should look to integrate the DAAS model into existing processes wherever possible; for example, because a crop scout is going to be onsite anyway, have him/her fly the drone. The ‘interpretation and consultation’ segment is important and investments in people should be focused there.
Continued drone technology development will reduce the need for third party data acquisition – Right now the DAAS model makes sense to include data acquisition for two reasons, capital cost of the equipment and difficulty in drone operation. Both of these reasons will go away, SOON. Drone prices are going to follow a classic better, faster, cheaper curve that will make equipment acquisition much cheaper. And soon after, sensors will follow the same trajectory. Drones should be considered commodity “data acquisition devices”.
Our company has been rolling out an “Easy Button” initiative for more than a year in anticipation of reaching a more general business user. It’s now relatively easy to plan and fly a mission, and very soon we will be offering a no-brainer auto-landing capability. 3D Robotics’ new drone, the Solo, has made drone operation and video capture much easier to name just a couple. In the near future, an average user will be able to easily perform the “data acquisition” component of the DAAS model and eliminate the third party operator who has to travel to the destination site and be limited by weather and other conditions.
Post Processing and Analysis will become far more sophisticated – This component of the DAAS model will also undergo significant change. We have only begun to develop applications that let us create actionable information for end users. Look for thousands and thousands of offerings to come to market in the upcoming two to three years. Developing proprietary algorithms will become a key component to the success of the DAAS business model.
Interpretation and consultation will become the cornerstone – Here is where the DAAS model continues to make most sense. Building relationships with your customers, acting in a paid advisory role as a subject matter expert will continue to be valuable.
In summary, DAAS represents a new and exciting business opportunity. For the short term, drones should be relegated to ‘data acquisition devices’ and the emphasis should be on “it’s all about the data”. Companies that are focused on applications with a quantifiable ROI will create good value propositions. The winners will be those companies that develop proprietary algorithms which solve real business problems, minimize capital and labor costs, and are nimble enough to react quickly to tech developments and regulatory changes.
