A modern data company
Data is the new oil or the new gold, depending on whom you talk to. The amount of data is growing faster than anyone could anticipate, and it will continue to do so in the coming days, months, and years.
Enabling technologies such as IoT, the cloud, and 5G, to name a few, are taking off. We could not imagine using our phones to watch a full-length feature film a little over a decade ago. Now our children are binge-watching entire episodes on Netflix while in the car over 4G, on the way to our holiday destination.
Data is everywhere, and information is stored and kept for future analysis. Multi-billion-dollar businesses have been built leveraging data to have value on its own. Data can be stored and sold repeatedly—data in, data out.
Of course, many of those businesses have invested tremendously over the past decade in building and deploying infrastructure to collect, store, and transmit that data.
xyzt.ai is a software platform. We are not (directly) in the data business. But many of our partners in our ecosystem are. With this whitepaper, we collect the findings and lessons learned from working with those companies in the hope that we can give you a few tips and tricks on how to become a mature data company.
A few remarks first. Firstly, we could have made this report consist of not just five tips, but a dozen, easily. We chose these five; however, this does not mean there are no others or more essential tips.
Secondly, we are biased towards “x,y,z,t data” – location data with a spatial and temporal component. But the five steps we outline here apply to any big data company building a business around collecting and selling data.
In our view, a modern data company should focus on the following five topics to become a mature data company:
- First and foremost, listen to your customers
- Invest from day one in building an ecosystem
- Diversify your data offering
- Scale your business by creating a platform, Data-as-a-Service (DaaS)
- Add analytics and insights to your business model
Five steps to becoming a mature data company
First and foremost, listen to your customers
This is true for any company, but especially for data companies. What do your customers need? What are the struggles they have with your data? How can you help them out? What other data sources are they looking for? What other data sources are they buying elsewhere? Could you offer derived data products to help your customers accelerate their solutions?
Learn from your customers and adapt based on their feedback.
Invest from day one in building an ecosystem
Many markets and verticals on the journey of digitalization and innovation are slow to adapt. Take the maritime industry as an example. One of the oldest industries in the world. It is slow to change. Don’t try to turn the ship by yourself. You won’t be able to do so. It would help if you had an ecosystem around you to move things forward.
As a data company, this is extremely important; most likely, the end-consumer of your data is not your direct customer. Instead, it’s a car driver looking for an open parking spot (your customer would be the platform that notifies drivers of available parking spots based on connected car data). Or it could be a financial analyst working with a business intelligence (BI) tool ingesting maritime AIS data to analyze dry bulk trading trends.
Your data is the oil, but engines are necessary to consume the oil. Those engines will be sold to and used by the end-users. You need to invest in an ecosystem, build partnerships, implement a company culture around leveraging partnerships.
This also means that; don’t be difficult to share sample data sets, set up joint demonstrations, or refer each other to your customers or prospects. Make it as easy as possible for those engine-makers to leverage your data as they are your multipliers.
Finally, look at your competitors; what are their data offerings and coverage? Maybe they should be your partners. Perhaps you can offer your data to them, and they can offer their data to you. Win-win. We see many competitors complement each other and become partners in the data ecosystem.
Diversify your data offering
You can rank data companies by the number of different types of data they offer.
Why should you diversify? Well, a problem is never solved by exploiting a single data source.
Listen to your customers, their problems, and what type of data they need. Leverage your existing infrastructure, processes, sales teams, and partnerships to extend your data offering. Hire experts on the topic. For example, when developing your offering with weather data, hire expert sales who have sold weather data before. Hire somebody knowledgeable about the use cases and problems that can be solved by combining weather data with your other data sources.
Make sure your data “talks to each other.” That different data sets are compatible. You have the partners in your ecosystem with tools that can work with not just one data type but all data types. Make it as easy as possible for the buyers of your data sets to leverage them.
Some examples of industry verticals where multiple data sources are needed and used to solve problems:
- Maritime data
- Satellite AIS data
- Terrestrial AIS data
- Weather data
- Port polygon data
- Vessel information data
- Connected car data
- Floating car data
- Events data
- Parking data
- Road friction data
Make your offer complete by reaching global coverage. Prepare to scale globally if not already done. Find your data providers, build partnerships, and make a difference to stand out from your competition.
Scale your business by building a data platform, i.e., Data-as-a-Service
Your data should be easier to buy than to use. Build a platform with a marketplace and APIs to automate and scale your data delivery. Even if you are not exposing this platform externally at first, make sure internally that you can use it and answer a data request in seconds.
Make it as easy as possible to deliver the data and consume it. Make the process as fast as possible. In our experience, we have seen companies take many months to provide a data set. Self-service or same-day delivery should be your target.
Adopt cloud technologies. Expose APIs to connect and download the data. Provide data samples. Work with your ecosystem partners to provide data integration examples in GIS, BI, or any other tool with your samples, so that your customers know how to digest the data and extract insights.
Think in terms of Data-as-a-Service.
Add analytics and insights to your business model (DIAAS)
Not all users are data scientists, know python, or can crunch data. Add easy-to-use analytical tools to your offering. Either through your platform or your ecosystem. The value of your data lies in the insights that can be extracted.
If there are no tools to consume your data, what value remains in your data?
This is where xyzt.ai steps into the picture. We are helping the data partners in our ecosystem add analytics and insights to their business model. By enabling them to leverage the capabilities of the xyzt.ai platform. By allowing them to embed our visual analytics platform.