Three Challenges of Building a Proprietary Dataset

The current state of the software industry is a quest towards autonomy. What that means is that we are currently at a stage where systems allow humans to navigate the world which means that the humans are still in control of the steering wheel and tech is offering data & options with the final decision in the hands of humanity. Like self-driving cars, the software will also reach a point (via AI) where it will be equally self-driving. The world will shift from a situation where machines are assisting humans to the world where humans are assisting machines. All the above is backed by a Proprietary Dataset.

When applied to C2, currently it operates as a workflow-driven solution that encourages sales reps to input their activities and in turn allow their supervisors to view said activities and manage better. The AI version of C2 would input the activity automatically. The system, like AI startups, would be able to predict which leads are worth closing. It would draft correspondence for leads that are not only qualified but also most likely to close. It would then know, based on experience (trial and error) which channels of follow-up to use (email, chat, social) in order to generate the desired result. When the closing meeting requires a human face at the dinner table, the human takes over.

This can only happen with a qualitative proprietary dataset. One that is unique, has a scale of data and weighs heavily on the data network. In order to build a proprietary dataset, digital entrepreneurs will need to.

1. Value

Without a significant investment, the startup must be interested in bringing tremendous value to the end user. With time, the value must be incremental in its offering. Think of the evolution of LinkedIn or Facebook, the expansion of goals and how that translated into the user experience.

2. Network

Every new customer or user will get more value from the first day of using your service than his or her predecessor. This is because of the N+1 concept and the past user's datasets have built on the refinement of your service over time. With more users, a dataset becomes more robust and has the power to deliver.

3. Test

During Alpha and Beta phase, get testers on board for free to figure out what part of your hypothesis lives up from a value, navigation, journey, and growth standpoint.

Follow the above and build a world class proprietary dataset.