Making Data Analytics Work on a Blockchain
By: Ali Aswegui
Tags: Machine Learning, Artificial Intelligence, Blockchain, Technology, Bitcoin

“Artificial Intelligence”

Artificial intelligence with predictive learning capabilities employing black box algorithms have been around for decades being used by different institutions including the government and their military.

Applications of such are varied from medical to education to national security. However, do we know how it really works on a blockchain providing behavioral data analytics?

“User Interfacing provided by DATAVLT”

DATAVLT was one of the first institutions to apply A.I. with machine learning capabilities on a blockchain with the aim of providing a low cost and efficient data analytics tool. They have provided on their whitepaper an overall data processing framework. The task; let us digest each component in simple terms. The discussion will be in this order: external and internal data, algorithm black box, artificial intelligence, predictive learning or machine learning capabilities, and the DATAVLT blockchain.

“Business data”

1st step: What type of data will be fed in to the network?

The data can be external and internal. External data includes information from the global web including that of similar entities, industries, Google analytics, any other third party sources, and data from communication involving consumption of goods and services like social media accounts of the organization using the platform. Internal data includes the organization’s private data held by different departments especially that of the IT and planning departments to facilitate better productivity and encourage innovation among the employees to serve their customers better.

“Black Box”

2nd step: What is the algorithm of the black box?

When all data needed is gathered, it’s time to feed these to the algorithm black box. This data is then treated as inputs to the box (this box can be an algorithm, a transistor, or even the human brain). It will act as observer and selects all data related to the specifications or the industry where the user of the tool belongs. Afterwards, the selected data will be considered as outputs where they have observable elements or similarities. That brings us to the next phase.

THE BLACK BOX PROBLEM SOLVED with DATAVLT

Despite many uses of such, some will contend that it can have biases to the output data processed which may cause inaccuracy of analysis. Furthermore, creators will not be able to know some black box algorithms processes. However, this can be fixed if an organization will devote their time with it. DATAVLT knows the existence of this problem and has been very rigorous since their inception to bring the best Black Box. The reason why most fail is the quick deployment of such systems without proper testing. But, DATAVLT, on their road map we can see clearly their commitment to deliver the best tool since they will be conducting numerous beta testing with partners before official launching.

“Future Learning”

3rd step: The function of Artificial Intelligence

DATAVLT stated that AI will sort, unclutter, and reject duplicitous and dubious data. This means that the A.I. will have to trim down the insurmountable data outputs to have obtained what are necessary for analysis and to reject duplicated data resulting from departments keeping separate accounts that have the same similarities.

“DATAVLT Blockchain”

4th step: The DATAVLT blockchain

Once the data is ready for processing and in depth analysis, it should be secured and remain unaltered to prevent inaccuracy of reports. This is done through blockchain since it has the characteristic of immutability and tamper proof where no one will be allowed to make deliberate and unintentional fraud within the network.

5th step: The machine learning capabilities (Predictive learning)

The data will now be ready to be processed into information which can be communicated to the management team. Predictive learning capabilities allows the tracking of significant patterns of historical financial information, trends of customer behaviors, manager performances, variances useful in management by exception, and others.