My job in the 2010s was generating value from big data. I was trying to uncover the hidden data treasures in the bank where I worked.
Brief History of Big Data
Big data expresses the volume, variety, and velocity of data. Like today's Web3, big data was a popular term in the 2010s.
In the 90s, companies set up data warehouses where data was organized and stored. Then, they formed analytics teams to access these data warehouses and generate value.
The advantage brought by data warehouses was that they allowed business people like me to tamper with the data. We started with simple reports first. Then we began to investigate the relationships of various variables with each other. In addition to the facts we already know, we also gained important insights that guide business strategies. Finally, prediction models came into play and made essential contributions, especially in marketing.
The methods used to generate value from data have long been given cool names. The term data mining was popular when I started working in analytics. Later, the same job began to be called machine learning. The difference between machine learning and data mining was that it involved more automation. Finally, the most fantastic term, artificial intelligence, came to the fore. Artificial intelligence expressed the coexistence of hand-written codes with machine learning models. Machine learning models have finally solved problems such as translation and speech to text that experts tried to solve with codes written by hand in the past. Therefore, the popularity of machine learning models in the AI discipline has increased since the 2010s.
Extracting Value From Big Data
The complexity of the analysis methods did not increase the value extracted from the data to the same extent. Companies first handled financial data with analytical methods. Blockchains are primarily used to create a new currency. Later, companies began to analyze product and customer information.
One of the areas where big data worked best in banking was marketing. Similarly, tech companies have based their business models heavily on targeted advertising. Marketers use big data to recognize customers and recommend suitable products to them.
Other important uses for big data were algorithmic trading, risk management, credit scoring, and fraud detection. As the volume of data increases, we can make detailed analyses on these issues and better predict the future.
In the second half of the 2010s, blockchains came to the fore, and after the bull market in 2017, more companies paid attention to them.
Creating Value Using Blockchains
The evolution of data analytics over the years can give us an idea of the potential of blockchains, as the rapid development of information technologies fuels both disciplines.
No matter how cool the methods used in analytics are, use cases determine the result. We can put forward a similar view for blockchains. Successful outcomes are obtained when the use case and the method or technology are compatible.
To generate value from big data, developers, financiers, product designers, and marketers had to work collaboratively. Analysts who understood more or less the language of all parties acted as translators.
I think it is essential that software developers and people who understand finance, product development, design, and marketing work together to create value using blockchains. Some of the most brilliant people on our planet become programmers, and these people have a gift for learning fast. However, they do not suffice in all of the subjects mentioned above.
Crypto Use Cases
Blockchains solve the trust problem by reconciling transactions on many servers. Thus, people who do not know each other can collaborate in the digital environment.
Finance has been the first application area of blockchains with a high added value per transaction. If we follow the path of big data, we must remember that product and customer information comes after finance. Digital products such as works of art, collectible cards, and game objects are already on the agenda. The customer dimension played a vital role in the marketing use of big data. In the DEFI world, on the other hand, we lack the tools to recognize users, as things work on an account-by-account basis. Many crypto users also prefer this. On the other hand, if DEFI implementations could identify people, they could give them flexibility regarding loan collateral.
We can list the main sectors of blockchains as currencies, smart contract platforms, stablecoins, content creation and distribution, data management, file storage, exchanges, interoperability, virtual reality, and gaming.
Conclusion
In the future, we can expect that digital infrastructures that involve more than one person, institution, or company will be moved to blockchains.
In an interview I listened to last week, futurist Ray Kurzweil announced that he maintains his prediction that artificial intelligence will pass the Turing test in 2029. Kurzweil made this prediction in his 2005 book Singularity Is Near.
If this prediction turns out to be correct, we may witness that artificial intelligence agents have an important place in the crypto ecosystems of the future. Bots already play an essential role in play-to-earn (play-to-own?) games. On the other hand, trading bots have also started to generate significant volume.
We have been encountering the characters directed by the program in the games for a long time. Artificial intelligence-supported characters will help work in the metaverses of the future.
One of the most critical problems with big data is that it is confined to silos. Blockchains enable data to be made available without compromising security. Even today, it is possible to create machine learning models on big data with the contribution of hundreds of people from different countries.
Big data is controlled by governments, banks, and technology companies. When the information is started to be kept in decentralized structures, the usage areas will expand rapidly, and dozens of new use cases will emerge.
Thank you for reading.
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