Why Big Data Needs Blockchain Technology
Blockchain technology presents a new paradigm shift in how data is stored, accessed and verified. In the coming years, the big data industry and blockchain technology will begin to form a pivotal relationship that will transform how companies manage their complex and ever increasing databases.
To better understand what is blockchain and why there is so much synergy between blockchain technology and big data, let’s start by defining each topic
What Is Blockchain Technology?
A blockchain is a decentralized public ledger (or record book) that is shared across a distributed network of computers.
This ledger keeps track of assets owned and transactions made on the network. The main value proposition of blockchain technology is that it provides a secure and trusted database that enables parties to transact with one another without the need for an intermediary (like a bank or insurance company).
The public ledger is open and immutable, meaning that everyone has access to the same information on the database, and it cannot be altered once inputted. Miners (autonomous individuals or entities that operate computer nodes) secure the blockchain network by validating transactions using consensus mechanisms like Proof-of-Work (PoW) or Proof-of-Stake (PoS). The most common consensus protocol, PoW, requires miners to validate transactions by solving a series of complex computation problems through the use of a powerful computer or mining rig.
Miners compete to validate these transactions, and the first to solve the computation problem wins a prize in the form of whatever cryptocurrency is native to that blockchain.
The benefits to companies are enormous. They can reduce the cost of compliance, security, upkeep and transaction processing all through the blockchain. For this reason, it makes sense that 84 percent of companies (including Amazon, Microsoft, IBM, Deloitte and JP Morgan) are dabbling in the technology, according to a PwC survey.
What Is Big Data?
According to Wikipedia, Big Data is simply defined as:
“Data sets that are too large or complex for traditional data-processing application software to adequately deal with. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.”
Big Data has six characteristics (called the “6 V’s” of big data) – Volume, variety, velocity, and veracity.
This is the quantity of generated and stored data. The size of the data determines how valuable it is and how much insight can be gained. Big data is usually considered to be on the scale of terabytes or even petabytes. This data is often very dense and unstructured.
Variety refers to the different types of data available. Compared to small data, Big data includes text, images, audio, video and multiple other types that often make it very challenging for analysts to decipher. This creates the need for additional pre-processing to better organize all of the different data types.
This is the speed in which the data is generated and processed to meet the needs of the entity using it. Velocity can also be assessed by the frequency in which data is generated or the frequency in which it is handled, recorded and published.
Big data is often viewed in real time or near real time, depending on the needs of the particular industry using the data.
Veracity refers to how “true” the data is. It’s a characteristic for defining the quality and accuracy of data. High volume data with lots of variety only makes it more difficult to attain high veracity. The consequences of low veracity are incorrect business analytics, which can lead to poor decision making. The best solution for maintaining high veracity is to track data sets to their source and correct any mismatches or errors discovered along the way.
Value refers to the amount of money that can be generated from the data. In most cases, more data is equal to more money. However, in order for that data to be valued accurately, it needs to be processed and organized in such a way that a seller can decipher it for their particular use case. For example, if a medical research company is in need of blood type data for a certain demographic, they must be able to purchase data that they can easily decipher to find exactly what they’re looking for. Any additional work required to categorize and sift through irrelevant data could impact the monetary value of that data.
This refers to the number of inconsistencies found in the data. These inconsistencies can be discovered by using outlier detection techniques. The lower the variability, the more high quality the data. An increase in data variety can also lead to high variability.
Big Data Use Cases
The use cases for big data vary depending on the industry one is focusing on and what problems are being solved. Generally, big data is used to improve customer experience. The more an enterprise can learn about its customers, the better it can serve them, present relevant data to a users profile, and learn more about purchasing behavior as prices change.
Big data is also fed into AI and machine learning models to improve the functionality of the software, enabling systems to execute operations more precisely based on inputs of higher quality data from customers.
How Blockchain Solves Big Data’s Biggest Challenges
What makes blockchain technology an ideal fit for big data is the fact that almost all of its value propositions solve a critical challenge that exists in the big data industry.
Information stored on a blockchain is distributed across a network of nodes. This limits the possibility that the data can be stolen or manipulated. Decentralization ensures that the integrity of the data can be maintained and that variability can be reduced.
Decentralized databases are also highly secure because there is no single point of failure. Once a piece of data is inputted on the blockchain, it becomes immutable and cannot be tampered with. Nodes are incentivized to keep the blockchain network secure by validating data transactions. This incentive mechanism ensures that multiple parties are working to prevent the system from being compromised by a single individual or entity.
The transparency of the blockchain ensures that companies are able to trace the source of a piece of data back to its origin, something that is critical to ensure high veracity in a data set.
Blockchain technology has few limits on the amount or types of data it can store, which makes it an ideal solution for companies with a large variety of big data.
Furthermore, smart contracts, a feature of the technology that enables transactions to occur under a set of codified rules, can enable terabytes and petabytes worth of data to be processed with minimal variability and maximum velocity and veracity. These features ultimately lead to higher valued data.
The Next Step in the Evolution of Big Data
It is becoming increasingly clear that blockchain technology is the ideal solution for many of the challenges facing the big data industry.
Today we are already seeing examples of big data and blockchain projects such as Storj (an open source, decentralized file storage solution) and Omnilytics (a platform that combines blockchain with big data analytics, using artificial intelligence and machine learning to vastly improve data process speed and quality).
Blockchain technology represents the next step in the evolution of data storage, processing, and quality control. As awareness of the technology continues to grow, observers can expect more companies to adopt decentralized databases, ultimately unlocking new layers of value for themselves and the entire big data industry.