Big data handle a huge amount of data sets on a day to day basis and process and analyze it. Blockchain takes transaction processing and the smart contracts and distributes them out who runs the blockchain technology for authorizing transactions. So, both dealing with their core is very much the same obviously, their business function is different but if they work together it is considered as the best merge.
Big Data services in some industries can be very sensitive and need to be secure as well as privately held, it is used in fields like Banking, Defense, Healthcare and so on. The main concern for the owners or stakeholders for Big Data is the security of their data. Though there are many technologies available for the security of Big Data perhaps there are some serious flaws of such techniques and data is vulnerable.
Artificial Intelligence creates intelligent machines that work and react like humans. If we combine Blockchain and AI it will be more secure and highly efficient. One way to combine the two is by using a distributed ledger, distributed ledger stores large amounts of encrypted data and artificial intelligence can manage it effectively. Surely, for securely storing personal data in the Blockchain and sell access to it. As a result, data, model, ledger and AI marketplaces arise.
People don’t necessarily think of the banking sector as an exceptionally high-tech industry, but some brands are changing perceptions using Big Data, AI and Blockchain. One bank engineered a virtual assistant that uses predictive analytics and natural language processing to help customers view banking transaction histories or information about upcoming bills.
Also, that virtual assistant gets smarter with every transaction and here comes the role of AI using supervised, unsupervised or reinforcement learning. Representatives for Bank say the assistant will eventually study people’s banking habits and provide relevant financial advice and based upon those habits and advice, the assistant could use Big Data for storing that large amount of data in Hadoop and processing and analyzing upon that data. Big data aids in the fight against banking fraud and AI in the predictive machine learning model detected the equivalent around $100,000 in fraudulent transactions in the first week of use and was more close and accurate in upcoming weeks.
So, here comes the role of Blockchain, for storing these transactions in a block and attaching those blocks to make a chain of blocks, Blockchain is used. So, no possibility of fraud is there and if some group of people tries to tamper data simultaneously, then also it can be detected by a change in hash value from the real hash value.
Another Example of merging these three fields is in the transportation industry. People need to reach their destinations on time, and big data and analytics help public transportation providers increase the likelihood of successful journeys in a minimum interval of time. The transportation industry can use statistics to map customer journeys, provide people with personalized details and manage unexpected circumstances. Representatives can tell how many people are on a given bus or minimize the distances travelers must walk to board buses and can tell if there is another bus on the same route or not.
Not only on-road but analytics assist people in the rail industry, too. On-board sensors give details about trains’ braking mechanisms mileage and more important information. Datasets from over 100 trains could produce up to 200 billion data points annually.
The people examining the information attempt to find meaningful patterns that guide them in improving operations by using Artificial Intelligence. They might discover chains of events that lead to equipment failure and take trains temporarily out of service in case of emergency.
Blockchain helps in storing these sets of data in blocks and verifying that no data is being changed or tampered and can also use consensuses if required for verification purposes. The transportation sector is also one of the best industries for people seeking data science careers and for researchers, it is a huge amount of profitable data for analysis purposes, which can lead to the growth of more money-oriented businesses in industries like these.
Here are the benefits if these fields are merged and utilized together:
Empowerment: A blockchain-based system empowers the data source providers to monetize their data and better capitalize demand, allowing data source providers to access the large global marketplace.
Transparency: A blockchain approach provides data providers with full transparency, traceability, simplicity, overcoming many of the hurdles data providers currently face in the existing marketplace. Anyone, who has operated in the big data space knows that duplicate data, false data, and sourcing are unfortunate industry truths. However, a blockchain-based approach provides complete transparency, allowing the buyers to see where those data has been and where it came from before purchasing.
Confidence: A more transparent grading system for data will improve confidence-building between the end-user and data sources. Currently, most data purchases are practically blind transactions, whereas buyers won’t know what kind of data they’re receiving until they buy them or use them because no vendor would ever reveal the data before money changing hands.
Smart Indexing: “Smart Indexing” engines are now utilizing analysis by using predictive analytics (a type of AI using data analysis and machine learning) for Confidence Scoring to provide continual real-time accurate data.