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Technology makes our lives easier and is changing the way people work tremendously. The same is applied for managers at investment firms like private equity, as they continuously seek better investment returns for clients.
The Internet, smartphones, satellites, and other electronic devices create tons of data every minute. This data is often collectively referred to as “big data”.
Many investment firms including Private Equity have been trying to leverage Big Data, by incorporating cutting-edge technologies like Machine Learning, including NLP (Natural Language Processing), Computer Vision, to analyze the vast amount of data collected and discover the hidden insights for investment decision making.
As mentioned in the previous article, many private equity firms have been using these technologies to analyze traditional financial data and alternative data in the recent decade.
In this article, let’s examine the key factors on how to make the most effective use of these technologies within an investment process.
#1 – Technology needs to go hand-in-hand with human
Technology is critical to analyze the huge volume of data for real-time or near real-time insights, but human input is still needed to harness the power of technology.
For example, with machine learning technology, not only investment firms can take advantage of ML algorithms to quickly analyze multiple data inputs and generate insights, but also define the connections, and patterns of interactions between the multiple data inputs.
Yet, even though Big Data Tools and machine learning technology can impressively analyze huge volumes of data quickly, the success of the application still relies heavily on human skills and input to build and refine the models.
Many investment firms have built in-house teams of data scientists and machine learning engineers in recent years, through direct employment in the local market or offshore staff augmentation – in order to create and improve the applications of cutting-edge technology in their processes.
The team of data scientists and other engineers will work with business teams to build, calibrate, and train machine learning models to think and learn in the investment domain.
When developing machine learning techniques it is worth noting that investors are already aware of the market. Thus, ensuring that new data and technologies can add value to existing models is an important criterion to meet before project implementation.
Therefore, the necessary skill includes not only the ability to create, utilize, and customize technology solutions but also to ask the right questions to validate hypotheses.
For example, an advanced model that applies only leading-edge technology that perceives only well-documented consumer behavior assumes an inefficient use of resources. In fact, we encourage the use of technology to reveal hidden information and insights that investment firms, like PE, know little about.
#2 – Use the “right” data and leverage data from multi-sources
Investment firms that look to leverage the powerful potential of Big Data need to consider incorporating a wide range of data when implementing their analytics strategy to enhance investment returns. This is because, in the new era of the digital economy, the global financial markets are increasingly becoming more complex, thus, the data generated can also be very noisy and unstructured.
It could be essential to consider as much data as possible to find out which could be the most valuable within the analysis process for investment decision-making.
Many firms have invested significantly in the acquisition of data. As a matter of fact, many firms have tried new broad sets of data for advanced analytics in recent years.
However, data sitting alone in silos will not turn into actionable insights. Advanced analytics is required to turn raw data into insights.
Still, it’s equally important to properly utilize the huge volume of data collected and use the derived information to enhance investment decision-making.
Moreover, the data collected must be tested for its quality and supplemented to build an evaluation and/or forecasting model. When investment firms incorporate a new dataset, it should be cleaned and run through a series of statistical tests to gauge the value of the information it provides.
In the developed nations like the US and Western Europe where technologies are advanced, there are many opportunities to find new useful data thanks to the plethora of digital devices and systems available. Yet, this does not mean that new data is always compatible or additive to existing models. What needs can be done is to try and find out – even if not successful, so that the new ideas can always be applied for subsequent, future projects.
Even though the right kind of data is found, it is somewhat unlikely that one single dataset will generate the needed insights. Fortunately, there are additional data sources that can be leveraged to analyze and derive the needed insights.
For example, in the retail industry, by applying data science and analytics with algorithms exploring the relationship between different data elements, retailers can produce insights into customer behavior, in-store, and online sales performance.
Thus, it’s critical that investment firms using multiple data sources to validate different analytics results, in answering the same investment hypothesis, which can sharply enhance the quality of the evaluation model.
Furthermore, the use of end-consumer data requires a strict process and framework to ensure privacy regulations are fully complied with and protect individual privacy.
Investment firms should have a strong set of compliance rules to adhere to when implementing data analytics. A proper methodology is to use mostly aggregated and anonymized data to prevent accessing personally identifiable information.
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