What's happening in the world of Bitcoin

 

Blockchain and Big Data are among emerging technologies that have the potential to transform many industries, dramatically changing the way businesses and organizations are used.

One would think that these technologies are mutually exclusive - each one creates a unique way and is applied independently. But it will disappear.

 

Blockchain, like data science, is slowly changing the operations of many companies. And while data science focuses on mining data for proper management, blockchain ensures data trust by supporting accounting records.

 

The question is, is there a place where these two ideas come together? What will happen if these two technologies are applied at the same time?

 

Simply put, how will blockchain disrupt data science?

To answer these questions, it will help to better understand blockchain and data science separately.

 

What is Blockchain? Blockchain is essentially a transparent ledger that records business transactions in an unmanageable way.

The technology became popular because of the interest in bitcoin and cryptocurrency in general, but it has found that it is important to record not only cryptocurrency transactions but everything that has value.

Knowing the power of this emerging technology, developers and enthusiasts are working on the functionality when it is used for blockchain.

 

Demand is high for blockchain developers 

Demand for blockchain developers has increased in recent years, as have jobs working on various blockchain applications. Reports from independent platforms like UpWork have identified blockchain skills as the most in-demand skills. In the same way, experts in other areas such as legal studies will have a great advantage if they have blockchain skills - or at least an understanding of the technology.

What is Data Science?

Data science seeks to extract knowledge and in-depth insights from structured and unstructured data.

This area includes statistics, data analysis, machine learning, and other advanced methods used to understand and analyze real-world processes using data.

 

Data is often described as the new oil in economic terms, so that large companies, including the famous GAFA (Google, Amazon, Facebook, and Apple), control a lot of data.

Some common data science applications are found in internet engine protocols, digital media, and streaming services.

Data collection, the main part of data science, has proven to be necessary for the health industry to follow the treatment of patients in a fast and efficient manner; and travel, games to improve customer experience; power management and many other departments.

Strong demand for data scientists 

There is also a lack of satisfaction for data scientists who can provide more insight into the data and help solve other problems. This is even more evident when you consider big data, an advanced part of data science that deals with large amounts of data that cannot be handled by traditional data processing methods.

The relationship between Blockchain and Data Science 

Unlike areas such as fintech, healthcare, and supply chain where blockchain is currently well-known, the technology has not yet been explored in terms of data science. For some, the relationship between those ideas is unclear or even non-existent.

To begin with, blockchain and data science interact with data - data science analyzes data to gain actionable insights, while blockchain records and supports data. Both use algorithms designed to control interactions with different data segments. One common theme that you will soon discover is this, "data science for prediction; blockchain for integrity." 

The impact of blockchain and data 

Data science, like any technological advancement, has its challenges and limitations which, when solved, will unleash its full potential. Some of the key data science challenges include inaccessible data, privacy issues, and dirty data. Handling dirty data (or incorrect information) is an area where blockchain technology can positively impact the field of data science to an extent.

According to a 2017 survey of 16,000 data professionals, dirty data such as duplicate or incorrect data has been identified as a major challenge in data science. Using arbitrary contracts and algorithms and cryptography, the blockchain supports the data, making it almost impossible to control it due to the huge computing power that will be wanted.

Despite its decentralized nature, blockchain technology ensures the security and privacy of data. Most data is stored on central servers that are often targeted by cyber attackers; numerous reports of hacking and security breaches indicate the extent of the threat.

On the other hand, Blockchain returns control of data to the creators of the data, making it more difficult for cybercriminals to access and manipulate data on a large scale. How Blockchain Can Help Big Data?

So much so, says Maria Weinberger of Janexter, blockchain is a habit. This follows the understanding that blockchain focuses on supporting data while data science or big data involves making predictions from big data.

Blockchain has brought a new way of managing and managing data - no longer from a central point where all data must be collected together, but in a decentralized way where data can be analyzed directly at the edge of any device.

Blockchain is integrated with other advanced technologies, such as cloud solutions, artificial intelligence (AI), and the Internet of Things (IoT).

Also, the data obtained through blockchain technology is processed and complete, plus it cannot be changed as we said before. Another important area where data from the blockchain becomes a big data increase is the integrity of the data since the blockchain tracks the origin of the data through its connection chain.

5 Use cases for Blockchain and big data 

There are at least five specific ways that blockchain data can help data scientists in general.

Ensure reliability (data stability) 

The records recorded in the blockchain should be trusted because it must undergo a process of verification that makes it safe.

It also provides transparency, since it is possible to trace the activities and transactions that take place on the blockchain network. Last year, Lenovo introduced this case of blockchain technology to identify documents and types of fraud. PC giants have used blockchain technology to support physical documents with digital signatures.

The digital signature is processed by a computer and the authenticity of the document is proven by a blockchain record.

Often, data integrity is achieved when the details of the origin and relationship about a block of data are stored in the blockchain and automatically verified (or confirmed) before any action is taken.

Prevent bad behavior 

Since blockchain uses a consensus algorithm to verify transactions, it is impossible for a single unit to become a threat to the data network.

A node (or group) that starts doing something wrong can be detected and removed from the network. Because the network is highly distributed, it is almost impossible for one group to create enough computing power to change the approval criteria and allow unwanted data into the system.

To change the rules of the blockchain, many nodes must be brought together to create a consensus. It is impossible for just one actor to achieve this.

Make predictions (ie predictions) 

Blockchain data can be analyzed, like other types of data, to reveal valuable insights into behavior and trends, and as such can be used to predict future events. In addition, blockchain provides structured data collected from individuals or any device.

In predictive analytics, data scientists rely on large amounts of data to accurately determine the outcomes of social activities such as customer preferences, customer lifetime value, firm pricing and rates.

Remove the name for the company. However, this is not limited to marketing information, because almost any event can be predicted with accurate data analysis, whether it is human emotion or investment signals.

And because of the distributed nature of the blockchain and the large computing power available, data scientists even in small companies can perform large-scale research projects.

These data scientists can use the computing power of thousands of computers connected to the blockchain network as a cloud-based service to analyze social outcomes at a level that would not be possible otherwise. Real-time data analysis 

 

 

As shown in the financial and payment systems, blockchain enables transactions across time limits. Many banks and fintech developers are now exploring blockchain because it enables fast - in fact, real-time - settlement of large sums of money, regardless of regional barriers.

Similarly, companies that need real-time data analysis at scale can use blockchain-enabled systems to achieve this. With blockchain, banks and other companies can see data changes in real-time, allowing for quick decisions, whether it is blocking suspicious transactions or investigating negative events.

Manage data distribution 

In this regard, data from data studies can be stored in the blockchain network. In this way, the working group does not repeat the data analysis that has been done by other groups or use the data that has been used in the wrong way.

In addition, blockchain platforms can help data scientists monetize their work, perhaps by trading research results stored on the platform.

 

 

Conclusion 

Blockchain, as it has been said, is in its infancy, although that may not seem so because of the technological improvements achieved in a short period of time. One would expect that as technology grows and there are more innovations in its environment, other uses and research will be discovered - data science is one area that will benefit from it.

 

That being said, a few challenges have been raised regarding its impact on data science, especially on big data that requires dealing with large amounts of data. One of the concerns is that applying blockchain to this would be too expensive to pursue. 

 

 

In fact, storing data on the blockchain is very expensive compared to traditional methods. Data processing constraints are small compared to the large amount of data collected per minute for big data and other data analysis projects.

 

How blockchain expands to address these concerns and disrupt data science opportunities will be of particular interest because, as we have seen, the technology has great potential to change the way we manage and use data. 

 

 

The amount of Bitcoin held by mining companies has dropped to levels not seen since February 2010 (Source: IntoTheBlock) 

Bitcoin reserves exceeded two million BTC - the first exceeded on February 19, 2010 - only 46 days since the beginning of 2022. This shows the effect of miners selling their Bitcoins during the year, sometimes selling more in a month than they mine, to offset the dwindling profits as the market - suffers.

IntoTheBlock uses machine learning algorithms to identify the wallet addresses of miners and track their holdings, including wallets linked to miners or mining pools that accumulate BTC but do not do their job. The total amount of BTC held in these wallets makes up the mining pool metric of the research company.

 

The fact that the reserves are below the 2 million BTC mark whenever they have this year highlights how difficult things are for the company. The number of Bitcoin miners initially fell below 2 million in July last year following news of mining in China, but the number has since rebounded. 

The pain of this year has been long. 

Companies that borrowed millions for their mining equipment, such as CleanSpark and Argo, have seen losses month after month. 

Just last month, Compute North filed for bankruptcy, Iris Energy sold $100 million in stock to raise funds, Compass Mining closed operations in Georgia, and one of the largest Bitcoin mining pools, Pooling, froze withdrawals. 

The last period of low miners was a different time for Bitcoin. In 2010, cryptocurrency was released as open-source software a year ago, the month after creator Satoshi Nakamoto published a white paper explaining how peer-to-peer e-money works. 

Bitcoin was sold for US dollars in 2009 at the New Liberty Standard Exchange when $5.21 could buy 5,050 BTC. At today's price, that amount of BTC would be worth almost $97 million. 

On the day that the first miner passed two million in February 2010, for example, miners held one out of every five bitcoins produced. The amount of Bitcoin miners distributed has dropped below 10%.