Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
Anyone who has ever worked with data analytics understands how it's vital to have quality data
Data Science is all about the analysis of data to extract useful insights. As a quickly evolving and exciting discipline, it’s powerful enough on its own. When paired with Blockchain, Data Science is turned into something far more structured and concrete, so it becomes even more useful.
Blockchain and Data Science are together changing the way many industries are operating. This is especially true in different industries that heavily rely on data, such as Finance. Performing predictive analysis when trading crypto markets, for example (or any other financial market for that matter) is made far more accurate and precise with these 2 powerful technologies working together.
Why is data analytics so important in crypto space?
To make better decisions, it’s important to take advantage of an ever-increasingly large amount of data, whilst also ensuring that the integrity of that data is as high as it can be. Bad data leads to bad analyses, no matter how sophisticated the analysis techniques themselves are.
Thus, it is important to look for new technologies that can improve the way data is collected, stored, secured and use.
5 Blockchain Use Cases in Big Data
There are at least five specific ways blockchain data can help data scientists in general.
- Ensuring Trust (Data Integrity)
Data recorded on the blockchain are trustworthy because they must have gone through a verification process that ensures its quality. It also provides for transparency, since activities and transactions that take place on the blockchain network can be traced.
Most times, data integrity is ensured when details of the origin and interactions concerning a data block are stored on the blockchain and automatically verified (or validated) before it can be acted upon.
- Preventing Malicious Activities
Because blockchain uses consensus algorithms to verify transactions, it is impossible for a single unit to pose a threat to the data network. A node (or unit) that begins to act abnormally can easily be identified and expunged from the network.
Because the network is so distributed, it makes it almost impossible for a single party to generate enough computational power to alter the validation criteria and allow unwanted data in the system. To alter the blockchain rules, a majority of nodes must be pooled together to create a consensus. This will not be possible for a single bad actor to achieve.
- Making Predictions (Predictive Analysis)
Blockchain data, just like other types of data, can be analyzed to reveal valuable insights into the behaviors, trends and as such can be used to predict future outcomes. What is more, blockchain provides structured data gathered from individuals or individual devices.
In predictive analysis, data scientists’ base on large sets of data to determine with good accuracy the outcome of social events like customer preferences, customer lifetime value, dynamic prices, and churn rates as it relates to businesses. This is, however, not limited to business insights as almost any event can be predicted with the right data analysis whether it is social sentiments or investment markers.
And due to the distributed nature of blockchain and the huge computational power available through it, data scientists even in smaller organizations can undertake extensive predictive analysis tasks. These data scientists can use the computational power of several thousand computers connected to a blockchain network as a cloud-based service to analyze social outcomes on a scale that would not have been otherwise possible.
- Real-Time Data Analysis
As has been exhibited in financial and payment systems, blockchain makes for real-time cross-border transactions. Several banks and fintech innovators are now exploring blockchain because it affords fast — actually, real-time — settlement of huge sums irrespective of geographic barriers.
In the same manner, organizations that require real-time analysis of data in large scale can call on a blockchain-enabled system to achieve. With blockchain, banks and other organizations can observe changes in data in real time making it possible to make quick decisions — whether it is to block a suspicious transaction or track abnormal activities.
- Manage Data Sharing
In this regard, data gotten from data studies can be stored in a blockchain network. This way, project teams do not repeat data analysis already carried out by other teams or wrongfully reuse data that’s already been used. Also, a blockchain platform can help data scientists monetize their work, probably by trading analysis outcomes stored on the platform.
The Covalent API holds terabytes of data, and regardless of industry best practices, is a candidate for a single point of failure. The decentralized network launch addresses this challenge and will help scale with the onboarding of applications using the API.
Covalent provides the richest and most robust data infrastructure for the entire blockchain ecosystem through a single, unified API.
Covalent leverages big-data technologies to create meaning from hundreds of billions of data points, delivering actionable insights to investors and allowing developers to allocate resources to higher-utility goals within their organization. Instead of pain-stakingly sourcing data from a small handful of chains, Covalent aggregates information from across dozens of sources including nodes, chains, and data feed. The Covalent API then sources end-users with individualized data by wallet, including current and historical investment performance across all types of digital assets. Most importantly, Covalent returns this data in a rapid and consistent manner, incorporating all relevant data within one API interface.
To learn more about Covalent, visit covalenthq.com