New technologies are emerging quicker than one can think. Emerging technologies like big data and blockchain are becoming conventional. Such technologies are changing the way organizations do their business. It is assumed that these two technologies are stand-alone and are being employed independently.
However, while data science deals with finding insights from raw and unstructured data, blockchain technology is a shared ledger that records and tracks transactions and assets in the business network.
As these technologies grow, the complexity and amounts of data stored too are proportionately increasing. Analyzing this data has revolutionized the two technologies by tapping their potential by bringing them together – there comes blockchain analytics.
The past decade has seen the rise of blockchain developer and technologies. According to a report, the global blockchain market size was valued at $2.89 billion in 2019 and is projected to reach $137.29 billion by 2027, growing at a CAGR of 62.7% from 2020 to 2027.
Integration of the two technologies, blockchain and data science is bound to provide a boost to its market value.
What is Blockchain Analytics?
The global cryptocurrency market size was valued at $1.49 billion in 2020 and is projected to reach $4.94 billion by 2030, growing at a CAGR of 12.8% from 2021 to 2030.
As defined in Wikipedia, “Blockchain analysis is the process of inspecting, identifying, clustering, modeling and visually representing data on a cryptographic distributed-ledger known as a blockchain. The goal of blockchain analysis is discovering useful information about the different actors transacting in cryptocurrency.”
To put it in simple terms, these are the blocks of data that are chained together in chronological order. Blockchain data analysis is a process that involves understanding, classifying, and monitoring blockchain transaction data that allows users to get valuable insights and helps in better risk assessment. With all these analytical possibilities, blockchain analytics becomes the most promising application of data science.
Such transparency of data enables the regulatory authorities and law enforcement to have full visibility of unauthorized transactions and track the movement that helps them to identify the criminals. Similarly, individuals get better visibility of the trends and investments that empowers them to make more informed decisions.
To understand how it works let us first understand the blockchain and data analytics separately.
What is Blockchain?
Cryptocurrency Bitcoin was the first Blockchain application. Based on its tremendous success, thousands of such blockchain-based cryptocurrencies were created that are known as alt-coins. This technology is compared to the invention of double-entry accounting. To be doubly sure is bound to bring revolution in the business world.
Blockchain is basically the distributed ledger that records economic transactions which anyone can access but cannot be manipulated.
Blockchains can be divided into two types – Private and Public. Private blockchains allow read and write access to designated participants with necessary permissions. On the other hand, a public blockchain can join any node on the internet, and information/transactions can be observed by all the nodes that are connected. Public blockchain does not require any permission to access the transaction information. Cryptocurrency falls under the public blockchain.
What is Data Analytics?
Data analytics is the process of analyzing the raw data to find trends that help in getting the answers to make informed business decisions. It extracts knowledge and insights from structured and unstructured data with the help of machine learning and other advanced methods to use and analyze the data.
The organizations are running their growth machinery on the fuel of data. This data is mined, structured, and cognitively analyzed in various business applications. For example, in the healthcare industry, data science is helpful to track patient treatment and equipment flow, in the travel industry, it is used for improving customer service and experience, and many more.
Relation between Blockchain and Data Science
This area has not been researched much. However, the common link between the two is that both technologies have data at the center. While blockchain records and validates data, data science delivers meaningful insights for problem-solving and decision-making.
Both the technologies apply algorithms to interact with other data segments. In a nutshell, blockchain is for data integrity and data science for predictions.
How is Blockchain Impacting the Data Science?
As understood above, both technologies have different benefits with data as the center. Bringing them together will add another layer of data that satisfies the following basic requirements:
The specific benefits of each technology, when combined together, can save money by storing and analyzing data using blockchain technology that can store data for long periods of time.
Listed below are some benefits of how blockchain will enhance data science.
Enables Data Traceability
Blockchain enables peer-to-peer relationships. This means that if one account cannot explain the published methodology, this can be viewed by the other peer who could analyze and review the methodology from start to finish to identify how were the results achieved.
Blockchain enables the user to trace data through ledger transparent channels from the point of entry to exit. The user gets to know which data is reliable to use, how to store and update it, where it comes from, and its usage.
Makes Real-Time Analysis Possible
Real-time data analysis is very difficult. However, blockchain technology enables companies to analyze data on a real-time basis in a proficient way that helps in identifying any irregularities at an early stage.
Blockchain also enables two or more people to work on the same kind of information at the same time similar to the spreadsheet feature where the changes can be made and assessed by different users on a real-time basis.
Ensures Data Accuracy
As explained above, blockchain data is stored in both private and public nodes. Before adding the data to other blocks, the data is examined and cross-checked at the entry point itself. This, in itself, is a data verification process.
Makes Data Sharing Smooth and Easy
Smooth and easy flow of data are what make the functioning of the organization smooth. Managing data on paper is not only tedious but is difficult to manage as well.
Blockchain technology is fascinating when it comes to data flow and access. It can easily be viewed, transferred, and accessed in real-time, making it possible for two or more users at the same time.
The smooth and easy data flow without restrictions streamlines the administration process.
Content source: https://appinventiv.com/blog/blockchain-analytics/
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
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What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.
To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.
The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:
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For Big Data Analytics, the challenges faced by businesses are unique and so will be the solution required to help access the full potential of Big Data.
Let’s take a look at the Top Big Data Analytics Challenges faced by Businesses and their Solutions.
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Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.
If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?
Let’s take a look at the most important things you need to know.
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