An introduction to Data Driven Investment Decisions

INREV has recently published a paper introducing data-driven investment, the first of three that will be written on the topic. But, what does data-driven investment mean in practice?

Clearly, data has always played a role in investment decisions – up to a point.  But now advances in digital technology mean that much more data can be leveraged to help investment decisions than in the past, more efficiently and more rapidly.

As the paper explains, a major part of this change comes under the broad heading of Big Data, defined here as the end-product of collecting many data observations, often via artificial intelligence. This has the potential to revolutionise the market data on which investment decisions are based by bringing a far wider range of information to bear than was possible in the past, including datasets not directly connected to real estate – say job-posting data or voting behaviour. 

In addition, accelerated data processing is making it possible to compare much larger numbers of potential investment opportunities at one moment in time. This ability to widen the search pool while also refining the search criteria should help improve investment decision-making and ultimately raise performance, at least compared to less data-driven peers.

After examining the underlying principles, the paper goes on to investigate how organisations can put the idea of data-driven investment into effect, something that will be explored further in the second and third papers in the series.  One key challenge is establishing a structured process for taking investment decisions, a step required to ensure that the data produced, often at considerable cost, is as useful as it can be.  Another is to upskill the organisation’s workforce so that it can embrace data science and all the fruits of AI and Big Data.

In any event, it should be emphasised that data-driven investment does not aim for machines to replace human beings, as humans will always have the final responsibility for decisions.  The quest is rather to find the right blend of human and machine intelligence to optimise those decisions. 

Download the full paper below and stay tuned to INREV news for the next in the series