What is market data / traditional data?

Traditional data has been a cornerstone of decision-making for decades, forming the foundation of how businesses, investors and researchers understand markets, companies and economies. At its core, traditional data refers to structured information that is widely accessible, collected from established sources and is often regulated. Traditional data is sometimes referred to as market data. Market data is a sub-section of traditional data, but the two terms are often used interchangeably.

Why traditional data matters?

Data drives decisions. Traditional datasets, such as financial filings or stock exchange feeds, are highly structured and accessible due to regulation or convention. These datasets have long been the backbone of financial markets, helping professionals assess company performance, track market trends and manage risk.

Consider this: equity pricing data provided by exchanges offers precise, real-time insights into stock movements. This data is essential for traders, portfolio managers and analysts aiming to stay ahead in competitive markets. Traditional data’s consistency and reliability make it indispensable for many industries.

Who uses traditional data?

While traditional data has long been a staple for investment professionals, its applications have grown significantly. Today, key users include:

Corporations

To benchmark performance, understand markets and inform strategy.

Investment managers

For portfolio tracking, market analysis and risk management.

Researchers

To study macroeconomic trends, market behaviors and industry shifts.

Its accessibility and reliability make it a universal tool for organisations of all sizes.

Characteristics of traditional data

Conventional sources

It originates from established entities, such as stock exchanges, government agencies or large aggregators.

Highly structured and regulated

The data is often standardised, either by industry convention or legal requirements, making it easy to integrate and analyse.

Unlike alternative data, traditional data is not defined by the sophistication of its use. Even highly complex trading strategies leveraging nanosecond-level trading data rely on traditional datasets.

Types of traditional data

Traditional data spans a wide range of categories, each serving specific purposes. Some key categories include:

Company-reported data

Financial statements, corporate filings and event notifications.

Business relationships

Data on supply chain dynamics and partnerships.

Trading volumes and flows

Data on fund, asset or retail flows, providing insights into investment patterns.

Human capital data

Information on directors’ dealings and corporate leadership.

Risk and compliance data

Insights into global sanctions, cyber risk and regulatory compliance.

Economic data

Commodity prices, trade balances and other macroeconomic indicators.

Private markets data

Pricing and performance data from non-public markets.

Data management

Corporate hierarchies and security master files, ensuring seamless data organisation.

Geolocation data

Geographic revenue breakdowns, highlighting corporate regional performance.

News data

Financial reporting coverage, offering context to market movements.

Why is traditional data relevant to businesses now?

As technology advances and financial markets evolve, the demand for structured, reliable data is increasing. Innovations in fixed-income markets, for instance, have driven greater adoption of systematic trading strategies, underpinned by robust datasets. The electrification of bond markets and algorithmic trading advances have further amplified the role of traditional data in today’s economy.

How Neudata can help

Neudata is at the forefront of data, connecting data buyers with the right datasets to unlock value. Whether you are an experienced data user or new to this world, we provide the expertise and platform to help you navigate the complexities of sourcing, analysing and utilising traditional data.

Our flagship products, such as Scout and Ranger, are designed to streamline the process of discovering and utilising alternative and
traditional data.