Forecasting is one of those fields that isn’t given much thought from day to day, however, its role in business analytics is hard to understate. In short, forecasting is the act of taking data from past trends, sales, and other economic indicators, and using them to make an informed prediction on future events in the specified industry.
And perhaps not unsurprisingly, this forecasting technique has been broadly applied to any such industry that it might be seen as useful, whether that is the case or not. Today, it can be seen utilized in fields as far-ranging as sporting events, stocks, and even public elections.
Current Problems in Forecasting
The biggest problem facing anyone using existing forecasting methods is, of course, accuracy. This is because forecasting is not an exact science by any means. A lack of accuracy can stem from any number of other issues involved in the methods being used.
Perhaps the most significant factor that can hinder progress on forecasting models is a lack of data from which to draw conclusions. If the data you are working with is incorrect, unrelated, or simply has large gaps of missing information, prediction models will be unable to reveal much helpful for any industry.
Another limiting factor that can affect any niche industry using forecasting is a lack of proper tools for analyzing all the data that come in. For example, many businesses will employ sales teams that are the face of the company to any outside customers. As such, these salespeople will be able to provide the greatest firsthand information on the customers, their needs and worries.
But, the question then becomes, how do you quantify this? How accurately can your sales team record these items and how then does an algorithm analyze them?
Addressing Forecasting Problems with Blockchain
Of course, blockchain is not the magical cure-all for all ills in the forecasting space, but many are proposing uses for the technology to facilitate better and more effective models. Chief among these is the massive upheaval that blockchain has the potential to cause in the collection of data. And, being that data, or lack thereof, is perhaps the biggest limitation in forecasting, this could indeed be a game-changer.
So what are these massive upheavals? The core idea of blockchain is that it is made of distributed nodes rather than one central server. Not only does this provide security and greater combined computing power, but it also allows for data to be entered by these different nodes. This means that data collection for predictive models could be done by a vast array of members in any given blockchain, and subsequently, could then be distributed and shared among all in the network for use in their own forecasting endeavors.
Companies Using Blockchain in the Forecasting Space
One company using blockchain to aid in forecasting is Augur, which also happens to be one of the longest-running projects based on Ethereum. Augur is, in essence, a gambling platform in which people bet money on the outcome of events. However, where Augur differentiates itself is that their predictive models’ data come from all the members or nodes of the blockchain voting on the outcome. By doing so, they are providing their own insight en masse rather than relying on a centralized server or expert, which might be fallible or manipulated.
They market this strategy as being able to “Google the future”.
As blockchain technology continues to find successful use cases in specialized and niche industries, we at TraDove seek to unite these industries together in the B2B sphere. TraDove is developing its own proprietary B2B Blockchain Payment Network which utilizes a currency-pegged token to facilitate fair, secure and transparent B2B transactions around the world.