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Data analysis, a key aspect of data science, has been found relevant in the healthcare industry to track patient treatment and equipment flow; in travel a gaming to improve consumer experience; for energy management as well as many other sectors.

This is even more pronounced when considering big data, an advanced aspect of data science which deals with extremely large amounts of data which cannot be handled by traditional data processing methods. Unlike in areas like Fintech, healthcare and supply chain where blockchain is now very familiar, the technology has not been explored extensively in aspects of data science. To some, the relationship between the concepts are unclear if not non-existent.

For starters, both blockchain and data science deals with data — data science analyses data for actionable insights, while blockchain records and validates data.

7 Big Data Examples: Applications of Big Data in Real Life

Both make use of algorithms created to govern interactions with various data segments. Data science, just like any technological advancement has its own challenges and limitations which when addressed will unleash its full capabilities. Some major challenges to data science include inaccessible data, privacy issues, and dirty data. The control of dirty data or erroneous information is one area that blockchain technology can positively impact the data science field in no small measure. According to survey of 16, data professionals, the inclusion of dirty data like duplicate or incorrect data was identified as the biggest challenge to data science.

Through decentralized consensus algorithm and cryptography, blockchain validates data making it almost impossible to be manipulated due to the huge amount of computing power that will be required. Again through its decentralized system, blockchain technology ensures the security and privacy of data.

5 Big Data Use Cases- How Companies Use Big Data

Most data are stored in centralized servers that are often the target of cyber attackers; the several reports of hacks and security breaches goes to show the extent of the threat. Blockchain, on the other hand, restores the control of data to the individuals generating the data making it an uphill task for cybercriminals to access and manipulate data on a large scale.

If big is the quantity, Maria Weinberger of Janexter says, blockchain is the quality. This follows the understanding that blockchain is focused on validating data while data science or big data involves making predictions from large amounts of data. Blockchain has brought a whole new way of managing and operating with data — no longer in a central perspective where all data should be brought together but a decentralized manner where data may be analyzed right off the edges of individual devices.

Blockchain integrates with other advanced technologies, like cloud solutions, Artificial intelligence AI and the Internet of Things IoT. Furthermore, validated data generated via blockchain technology comes structured and complete plus the fact it is immutable like we mentioned earlier. Another important area where blockchain generated data becomes a boost for big data is in data integrity since blockchain ascertains the origin of data though its linked chains.

There are at least five specific ways blockchain data can help data scientists in general. Data recorded on the blockchain are trustworthy because they must have gone through a verification process which ensures its quality. It also provides for transparency, since activities and transactions that take place on the blockchain network can be traced. Last year, Lenovo showcased this use case of blockchain technology to detect fraudulent documents and forms. The PC giants used blockchain technology to validate physical documents which were encoded with digital signatures.

The digital signatures are processed by computers and the authenticity of the document is verified through a blockchain record. 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. Sensors, logs and transactional data can help track critical information from the warehouse to the destination.

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Big Data examples

This book presents different use cases in big data applications and related practical experiences. Many businesses today are increasingly interested in utilizing big data technologies for supporting their business intelligence so that it is becoming more and more important to understand the various practical issues from different practical use cases. This book provides clear proof that big data technologies are playing an ever increasing important and critical role in a new cross-discipline research between computer science and business.

Patrick C. Patrick has worked with Boeing Research and Technology on aviation services-related research with two patents on mobile network dynamic workflow system. Before that, he was a Research Scientist with Commonwealth Scientific and Industrial Research Organization in Australia as well as he worked as a software engineer in industry in North America.