Alternative Data - A guide for modern asset managers

Traditional financial data, such as stock prices and economic reports, is often not enough to fully capture the complexity of today's markets. Alternative data, which comes from a variety of sources such as social media, news and even weather data, provides additional insights that are critical to making informed investment decisions.

This data can uncover events and trends that traditional analyses miss, giving you a competitive advantage.

In this article, we shed light on how AI-supported data analysis is becoming an indispensable resource for asset managers, especially in times of volatile markets.

What is alternative data?

The term alternative data refers to data that comes from non-traditional sources. It is used to gain additional insights that go beyond the information provided by traditional financial reports and economic indicators. This data can come from a variety of sources, including social media platforms, online transactions, satellite imagery and Sensors in the industry. Alternative data provides a rich and often more up-to-date information base that can be used to predict market trends, analyze consumer behavior and assess risks.

The use of alternative data has increased, particularly in the financial industry, as investors and analysts are always looking for ways to gain information advantages. By using advanced analytics techniques such as machine learning and artificial intelligence, companies are gaining valuable insights from this unstructured data that traditional data sources often cannot provide. This enables them to make more accurate forecasts and better business decisions.

Challenges in the use of alternative data

However, the use of alternative data poses considerable challenges. The sheer volume of data available requires specialized data processing and analysis skills. This is a time-consuming and costly process that often requires the expertise of a specialized team of data scientists. What's more, interpreting this data can be difficult without the right tools, which increases the risk of bad investments.

Advantage of AI-supported data analysis

This is where AI-supported data analysis comes into play. These technologies are able to process large volumes of data in real time and provide valuable insights. They utilize machine learning in programming languages such as Pythonto recognize patterns and correlations in the data that would be difficult for human analysts to identify.

This leads to a considerable reduction in the time required for data analysis and at the same time increases the accuracy of the results.

Provider of alternative data

Alternative Data Providers are companies or platforms that offer access to various types of alternative data. These providers collect, process and sell data obtained from sources such as

• Social media
• E-commerce
• Weather reports
• Satellite images
• other unconventional data sources

originate. Some of the best-known providers include companies such as Quandl, Eagle Alpha and Thinknum. These providers play a crucial role by transforming raw data into processable information and providing access to data sources that might be difficult for individual companies to access. They also offer analytical tools and services to help companies interpret this data and integrate it into their business strategies.

The intelligent platform makes it possible to extract, process and analyze data from various sources, increasing the efficiency and accuracy of data usage.

What are Alternative Data Sources?

Alternative data sources are diverse and unconventional sources for obtaining alternative data. These include social media, where posts and interactions can be analyzed to perform sentiment analysis and identify trends. E-commerce data and online transactions provide insights into purchasing behavior and demand for products. Satellite imagery can be used to monitor agricultural yields, traffic flows and changes in infrastructure.

In addition to these examples, there are many other sources, such as sensor data from the Internet of Things (IoT), which can provide environmental conditions or machine statuses in real time. Web scraping, where information is extracted from public websites, is also a widely used method. These diverse data sources offer a wide range of information that, if used correctly, can help companies to develop innovative solutions and strengthen their market position.

Konfuzio helps companies to use these data sources efficiently by offering advanced technologies for extracting and processing unstructured data. This enables companies to gain valuable insights from a variety of data sources and improve their decision-making processes.

Use case - Event-Driven Financial Data Analytics

One specific example of the application of AI-supported data analysis is event-driven financial data analytics. This method focuses on analyzing market events and their impact on financial markets. Events such as political decisions, natural disasters or corporate announcements can have a significant impact on share prices and other financial instruments. The use of AI makes it possible to monitor and analyze these events in real time in order to make quick and well-founded investment decisions.

Increasing efficiency and reducing costs

The use of AI in data analysis brings significant efficiency gains. While traditional analyses often require hours of searching through reports and data sets, an AI-supported platform can complete this task in minutes. This not only reduces the time required, but also the costs considerably.

A typical data science team can cost millions annually, while an AI-powered solution requires a fraction of that and still delivers more accurate results.

Practical example - financial market turbulence during COVID-19

The importance of AI-powered data analysis became particularly clear during the COVID-19 pandemic. Traditional trading strategies often failed in the turbulent market environment, while AI-powered strategies that utilized alternative data proved to be more resilient. For example, hedge funds that relied on AI-powered analytics were able to recognize market changes faster and adjust their positions accordingly, resulting in better performance.

Market potential and future prospects

The market for alternative data is growing rapidly. The Alternative Data Market Size & Trends Study expects the global market for alternative data to reach a value of USD 17.35 billion by 2027, with an annual growth rate of 40%. These figures underline the increasing importance and acceptance of data analytics technologies in the financial industry.

Conclusion - A must for modern asset managers

The integration of AI-supported data analysis into the financial strategy is no longer a luxury, but a necessity. It offers not only a competitive advantage, but also a way to operate flexibly and effectively in a rapidly changing market environment. For asset managers, this means they need to rethink their traditional approaches and harness the power of AI to ensure their long-term success.

The use of AI in data analysis not only increases efficiency and reduces costs. AI also enables deeper insights and more precise predictions. It is therefore crucial for companies in the financial sector to invest in these technologies and integrate them into their business processes. This is the only way they can survive in an increasingly data-driven world and secure their competitiveness.

Take Contact to our team of experts and explore your options together. We look forward to hearing from you.