How to Use Insights From Data for Better Investment Decisions

Due to the modernization of technology, the development of businesses, and their concerns about collecting good-quality data, investment decisions have been much easier to make with a higher success rate. 

Analyzing business data and generating business insights is getting more and more popular among various businesses. Successful investments rely on quality-analyzed data that provides investors with specific investment insights. 

That means if you want to stay on top of investments and stay relevant in the industry, then extracting insights from data is not an option anymore – it is a necessity. 

The difference between data and insights 

The insights-driven companies are effectively growing at an average of more than 30% every year. Before we get into investments and how they rely on data and insights, let’s define what we mean by data and insights. 

Data are the facts, measurements, and statistics collected through observation and examination. This can be numbers, text, images, audio, etc. 

Information is a result of analyzing this data and giving it structure and context. The data gets a more pleasant view and makes more sense to the general audience. Insights are the beneficial knowledge you gain from understanding information. When you utilize information or data and accurately interpret it within its context you acquire insights. 

How to convert data into valuable insights? 

As easy as it might seem to gather lots of data, turning it into a valuable asset (insight) is challenging. However this process is key, 91% of companies say that data-driven decisions are essential to their business. Here are some steps on how you can pull successful insights from data: 

Choose the right questions at the beginning 

Asking the right questions before digging through data ensures you don’t spend time analyzing the wrong information. That way you can avoid insights that have no impact on your business goals or can affect them negatively if they don’t have a relation to the goals.

Express your end goals 

A company must have business goals. These have to be closely aligned with your test objectives. When you write these down it will help you develop a specific, measurable hypothesis. 

Integrate your data sources 

The data sets you have are only a small section and may not always tell the full story. The more data you gather, the closer you get to accurate insights. When you bring all sources together you can hit closer to the bullseye. 

Use context and visuals to comprehend data 

Visuals are common with data; it’s hard to encounter the raw form of data. Even though visuals are quite helpful, context will give you the full story. Data will just jump off the screen with much more meaning behind it. The combination of context and accurate visuals will significantly reduce the chances of making a mistake. 

Segment your data 

Fractioning data into segments can help make a stronger sense of it. Divide web traffic according to categories and it will simplify insight collection. Segmentations can deepen your perception of the target audience. Once you experience the simplicity that segmentation provides, it will quickly become a leading process in your insights strategy. 

Spot the right patterns 

Increase and decrease – are two of the easiest trends to observe on a graph. There are other types of plots like time series and scatter plots with help with understanding data. Never view patterns in isolation from their context. 

Form a winning hypothesis 

After analyzing all the data you have, it’s time to form a hypothesis to test. With a hypothesis, you are finding a solution to a problem that you can confirm with experimentation. A realistic hypothesis can lead to impressive results when testing. 

Be prepared to experiment 

With a realistic hypothesis, you can do and run a test. Until this point the hypothesis – even though born from data – was only an intuition. Experimenting gets you closer to creating a solid fact. According to research from Forbes Insights and Cisco, more than half of directors across North America and Europe rely on analytics to improve the quality of their services and products.

Common investment strategies using data insights 

Investors operate many data-driven (insight) investment strategies, nonetheless here are some of the most common investment methods. 

Risk parity 

Risk parity is an approach used in investment management, in which investors focus on administering risks related to assets. Due to recent advancements in the alternative industry, investors can make insightful decisions based on risk factors that were proven by algorithms. 

Managed futures 

Managed futures is a practice that refers to trends following financial professionals. This strategy is highly systematic and depends on market trends. Since this strategy relies on tracking market trends, it’s considered an alternative investment and diversifies portfolios while managing risks. 

Al investing 

Al investing, in other words, big data investing, is quite a new strategy of investing that relies on making investing decisions based on a measure called alpha. It’s the measure of active return on investment, indicating a thriving investment cycle. Other Al techniques are also used to track social sentiment, business operations, and security management. 

Event-driven investing 

Event-driven investing is a strategy that utilizes new and present alternative data sources and supports insights that border major financial events. Investors who are looking to balance a portfolio with short-term securities should turn to this method. 

Wrapping up 

The payoff for successful insights is clear and quantifiable. Investors utilizing traditional and alternative data sources should remember the different investment strategies, benefits, and shortcomings. 

As a result of the latest advancements and development in modern technology, data-driven decision-making is starting to be engaged across countless businesses.

Huynh Nguyen

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