Big Data is Kind of a Big Deal: Analyze it to Make Better Business Decisions

Aleksei Antonov Aleksei Antonov
November 27, 2018 Big Data, Cloud & DevOps

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Big data is disrupting every industry it integrates with, altering the corporate landscape by determining market trends, behavioral analysis, and other insights that only improve business decisions. Detecting and understanding large, verified data sets of hidden patterns, as well as unknown correlations, it’s helping startups make better decisions as they seek financial growth and recognition, build effective business strategies, and maintain customer satisfaction. Big data is changing people’s lives for the better, offering a more convenient, yet technical, means to entrepreneurs and investors securing a financial infrastructure within growing companies, while attempting to jump-start brand awareness.

Crypto-based businesses and startups, in particular, can benefit from incorporating big data into their technology and using its insights to create marketing strategies and milk investment predictions. When it comes to cryptocurrency and its volatile market, big data analysis could prove worthy; especially, if crypto and blockchain-based startups applied Wall Street style strategies – most notably, risk management – to its practice when analyzing the current state of the crypto space.

What Kind of Big Data Analysis is Wall Street Utilizing?

Wall Street utilizes big data to improve its risk management methods, while simultaneously increasing profits earned from investments. How? By implementing machine learning, technical analysis, and pattern detection in their research, all which produces keen insight into which markets are increasing in value or dropping down low. The emerging trends within these markets, when monitored by machine learning algorithms, allow Wall Street bankers and investors to gauge where their greatest potential for profits lie. Data-collecting algorithms are the saving grace that Wall Street investors have long awaited for, as they’re now able to make low-risk investments that won’t ultimately cost them assets or collateral. Data points providing insight as to the most valuable trends occurring with the financial sector are in itself a beneficial investment to make.

Should Crypto Companies Adopt Wall Street Big Data Strategies?

Token transactions, digital wallets, and mempools contain priceless patterns and emerging trends that can be utilized to make better decisions when navigating the crypto market and pledging investments. By collecting information that tracks trends in the space, the amount of loss experienced by investing companies would greatly diminish.

The crypto market isn’t currently taking advantage of these hidden, big data sets and they should, for other reasons that don’t have to do with accumulating profits over time. Data points collected within the crypto market are being taken from a designated blockchain that serves as the power platform for trading and investing in tokens that rise and drop in value. Therefore, blockchain-based big data has the ability to indicate the extent of security that investors are provided with when pledging funds. This type of security in itself is another strategy that contributes to risk management methods implemented by investing startups and companies, allowing them to make wise business decisions without the threat of data breaches and hedge hackers that once deterred them from participating in the market.

Big data comes with massive potential. It has the ability to increase profits in young crypto startups aiming to make wise investments and can provide companies with the trend and pattern knowledge needed to achieve large collections of token holders and platform users. Utilizing big data and its tool technology offers those analyzing it with valuable insight as to the crypto market’s current standing, thriving or plummeting, creating a transparency that leads to better business decisions, investment plans, and marketing strategies that increase the overall chance of professional success.

Originally appeared in insideBIGDATA

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