Motivation & Use Cases
Last updated
Last updated
Nobody wants to trust their data with somebody else. But everyone wants to make the most of the data available.
Most data vendors disclose all the data to the buyer upon sale. This means certain types of data simply cannot be monetized because of industry secrets and privacy-protection laws.
Data needs to be stored somewhere. It can be encrypted, in different ways, allowing different use cases, each coming with its own pros and cons. But data in active use usually needs to be decrypted or encrypted, which is still a technology in its infancy and currently incurs a 10x storage overhead and a whopping 1000x compute overhead.
Ocean Protocol is the main inspiration and competitor for Fishnet. We think it tries to do too much with different types of data at the same time. Fishnet focuses on the following aspects while processing exclusively time series data:
Scalability: Processing data should be as fast as possible.
Security: Data leakage must be kept to a minimum.
Decentralization: No central authority can crack Fishnet's security.
Adaptable: All of the above aspects can be tweaked to suit the network's needs, forks are easily created and can be integrated with one another.
By locking in on these aspects, we expect to deliver exceptional service to our users, no matter who they are. Fishnet is to be the platform of choice to build businesses based on time series data and enable collaborations across industries.
Even though time series data is a very specific subset of tabular data, it has applications in many industries:
Finance: Financial institutions, such as banks and investment firms, rely heavily on time series data and forecasting to make investment decisions, analyze market trends, and manage risk.
Energy: Companies in the energy sector, including oil and gas, renewable energy, and utilities, rely on historic time series data to predict demand for energy and optimize production and distribution.
Retail: Retail companies use time series data to forecast demand for products, optimize inventory levels, and develop pricing strategies.
Transportation: Transportation companies, including airlines, shipping companies, and trucking companies, rely on time series data to forecast demand, optimize routes, and manage fleet capacity.
Healthcare: Healthcare organizations use time series data to forecast demand for healthcare services, manage staffing levels, and monitor patient outcomes.
Agriculture: The agriculture sector uses time series data to forecast crop yields, manage crop rotations, and optimize the use of resources like water and fertilizer.
Real estate: Real estate companies use time series data to forecast property values and rental rates, as well as to analyze market trends and inform investment decisions.
Government: Governments use time series data to forecast tax revenues, manage budgets, and monitor economic indicators.
All these industries have their own security standards and are often heavily regulated in terms of the data that can be shared with other entities. What makes Fishnet special is that all these different data sources can now begin to collaborate with each other, running counter-party-approved computations on joint datasets.
This is only possible as we are dealing with time series data distributed by time windows on an intermediary network of computation nodes, all informed by a public & cryptographically-secured permissions management system.