Learn about the responsibilities in the field of data engineering with Part I.
Data engineers keep evolving with the technological advancements and the introduction of various models with that. The data engineering domain is progressing at a rapid pace, powered by disruption in Internet of Things (IOT), Artificial intelligence and machine learning models. Hence, data engineers also need to keep evolving and learning new practices in the field.
Going forward, we will find out about data engineering in the financial markets.
Data Engineering in the Financial Markets
In the financial markets, a data engineer needs to do the regular work of collecting data, cleaning the data which implies taking out errors such as duplicates. The last step is automating the trade with the help of the cleaned data.
Moreover, there are certain other ways in which data engineering helps the financial markets. These are:
- Risk management
- Predictive analytics
- Fraud detection
- Algorithmic trading
Since managing the risk is an extremely important aspect of any financial institution, data engineers play an important role. With the help of clean data sets, the errors in the prediction of trades do not take place. This is important because if the machine learning model gets fed with the erroneous data, it will lead to continued losses for the investor.
With the help of predictive analytics, the investor can foresee the data patterns and can take the right actions for the same in the present. A data engineer helps the firm/individual etc to take the right decisions while investing in the financial market this way. For instance, if the machine learning model is fed with data which has duplicates or irregularities, it will lead to erroneous input. This erroneous input, in turn, will make for false predictions in the trade and hence, less gains.
Going forward, data engineering also helps with fraud detection. As it is extremely important to detect if a hacker has hacked into the system to make the data malicious/unfit for feeding the predictive model, it is a must to get the same checked and cleaned by the data engineer.
In the algorithmic trading domain, data engineers help with the cleaning of the data which is to be fed to the machine learning or deep learning models for predicting the trades. An algorithmic trading system executes the orders with the help of pre-programmed instructions. While doing so, the data is required for historical backtesting which helps to understand if the created strategy would have worked well on the past data or not. While doing so, if the data fed for backtesting is not looked into properly, it can lead to wrong trading decisions in the future.
In the next installment of this series, the author will demonstrate the difference between data scientists and data engineers.
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