Contemporary business is inseparable from data and data technology. Multiple types of data are being utilized for various business purposes. In order to get the information they need, companies must turn to many different data sources.
Simply put, a data source is exactly what it sounds like—a source of data, may it be a computer file, a database, or a web service.
Data sourcing is a crucial part of modern business that enables firms to get the informational assets they need.
What is data sourcing?
Data sourcing is the process by which companies extract and integrate data from multiple internal and external sources. This procedure creates the firm’s data infrastructure that is used for handling daily workflows and achieving various business objectives.
As such, this process is an integral part of doing business in the heavily data-based markets of today.
Data source examples: computer file, database, web service, etc.
Data sources
Data sources can assume broader or more specific meanings in different contexts (i.e., primary and secondary vs. file data sources and machine data sources). In this article, we will focus on the primary and secondary data sources.
First-party data
Primary data is generated by the company itself through questionnaires, surveys, interviews, and other direct methods of data collection. This type of data is highly valuable because it is specifically designed to address a particular business problem or research objective, such as understanding customer preferences, evaluating product-market fit, improving service delivery, or assessing brand perception. By targeting specific questions and collecting context-rich responses, primary data enables organizations to make informed decisions based on insights that are directly aligned with their strategic goals and operational needs. First-party data is often collected through various digital touchpoints and systems, including:
- Website Analytics: Businesses track visitor behavior, session durations, click-through rates, and engagement levels using tools like Google Analytics. This helps companies optimize their websites and marketing campaigns based on real-time user interactions.
- Customer Relationship Management (CRM) Systems: CRM platforms store valuable data on customer interactions, purchase history, service requests, and support tickets. Companies can use this data to enhance customer experience and build personalized engagement strategies.
- Customer Feedback: Gathering direct feedback through surveys, online reviews, and direct interactions allows businesses to gauge customer satisfaction, identify pain points, and improve products or services based on user preferences.
- Transactional Data: Purchase records, order histories, and subscription details help companies analyze buying behavior and predict future trends.
Second-party data
Secondary data is generated by someone else and can be extracted and used for specific purposes. Unlike primary data, which is collected firsthand by a company, secondary data is obtained from external sources and can help businesses gain insights without conducting new research. Sources of secondary data vary widely and include government institutions, market research firms, websites, books, articles, industry reports, and publicly available datasets. This type of data is often used to support decision-making, analyze trends, and validate findings from primary research.
Secondary data can be classified into different categories based on its source and purpose:
- Publicly available data – Includes government reports, economic statistics, regulatory filings, and census data. Examples: World Bank reports, SEC corporate filings, and census bureau data.
- Academic and industry research – Research papers, whitepapers, and case studies from universities, research institutions, and think tanks provide valuable insights into market trends and emerging technologies.
- Market research reports – Businesses often purchase reports from research firms like Nielsen, Gartner, or Forrester to gain a deeper understanding of industry benchmarks, consumer behavior, and competitor performance.
- News articles and media publications – Business publications like Bloomberg, Forbes, and The Wall Street Journal provide market insights, financial news, and competitor analysis.
While secondary data is useful for saving time and costs, companies must ensure data relevance, credibility, and accuracy before relying on it for business decisions.
Third-party data
Third-party data refers to information that is collected, aggregated, and sold by external organizations or data providers rather than being generated by a business itself. This type of data is typically gathered from multiple sources, anonymized, and categorized before being sold to companies looking for insights into consumer behavior, market trends, or industry benchmarks. Since it comes from external sources, third-party data is often broader in scope and is used to complement first-party and second-party data for a more comprehensive data strategy.
Third-party data is commonly obtained from data brokers, research firms, and online marketplaces that specialize in large-scale data aggregation. Some of the most common sources include:
- Data brokers – Companies like Coresignal specialize in collecting, aggregating, and selling large-scale datasets, including demographic, financial, employment, and consumer behavior data. Coresignal gathers information from multiple sources, and packages it for businesses looking to enhance market research, competitive intelligence, and targeted marketing strategies.
- Market research firms – Some organizations conduct large-scale studies and sell reports on consumer trends, business intelligence, and industry forecasts.
- Advertising & social media platforms – Companies purchase aggregated audience data from platforms like Facebook, Google, and professional network to enhance ad targeting and customer segmentation.
Publicly available data
Publicly available data refers to information that is freely accessible to the public and can be used for various research, business, and analytical purposes. This type of data is typically provided by government agencies, non-profit organizations, and open data initiatives, ensuring transparency and enabling businesses, researchers, and policymakers to make informed decisions. Unlike proprietary or paid datasets, publicly available data can be accessed without direct cost, making it a valuable resource for company background verification, market analysis, and economic research.
Public data is often structured and maintained by authoritative sources, including:
- Government datasets – Many governments publish open data on economic indicators, corporate registrations, financial disclosures, and trade activity.
- Regulatory and compliance databases – Businesses use databases such as AML (Anti-Money Laundering) lists, sanctions lists, and financial compliance reports to verify business legitimacy.
- Open data portals – Platforms like data.gov (USA), the EU Open Data Portal, and the UK’s Companies House provide datasets on business registrations, environmental policies, healthcare trends, and more.
- Academic and research data – Universities and research institutions publish extensive scientific, technological, and economic reports that companies can use for analysis and innovation.
Web scraping & APIs
In today’s data-driven world, businesses rely on various methods to extract and utilize valuable information from the web. Two common techniques for automating data collection are web scraping and APIs (Application Programming Interfaces). These methods allow companies to gather, structure, and analyze online data for market research, competitor analysis, price tracking, and more.
Web scraping is the process of automatically extracting data from websites using specialized software or scripts. This method enables businesses to collect large amounts of publicly available data efficiently without manual intervention. Web scraping tools scan web pages, extract relevant data, and store it in a structured format (e.g., CSV, JSON, or databases).
An alternative to web scraping is using APIs, which allow businesses to access and retrieve structured data directly from a website, platform, or service provider without the need for scraping. APIs act as a bridge between different software applications, enabling them to communicate and exchange data securely. Many organizations offer official APIs that provide real-time, accurate, and legally compliant data access.
When deciding between web scraping and APIs, businesses should consider data availability, legal implications, technical complexity, and cost factors. Below is a comparison table highlighting the pros and cons of each method.
Both methods have their strengths, and in many cases, businesses combine web scraping and API integration to maximize data collection efforts.
Relying on data providers
From the sourcing perspective, choosing a data provider is a lot about accessibility options that are offered. Many providers will allow accessing their data through APIs in which case it is advisable to look at the filtering solutions offered. The best providers will also offer convenient data formats suitable for the data being shared.
Additionally, it is always good to look for providers that offer many different data types collected from multiple sources.
For example, Coresignal offers three different API solutions: Company API, Employee API, and Jobs API. The options are abundant, all you need to do is choose what you need.
Prioritizing data quality
Data quality has to be included among the factors by which to choose the right provider. Generally, when talking about acquiring and utilizing data to its full potential, quality is the key. On the one hand, various statistics show the multimillion-dollar cost of poor data quality and, on the other, that companies still struggle to ensure that the data they use is of optimal quality.
Thus, when sourcing data, quality must be prioritized. Companies should look closely at metrics such as the age, consistency, and validity of data should be scrutinized as much as possible. Although it is not always easy to make sure that the data used conform to high-quality standards. But there are still some things one can do. For example, data suppliers can be asked about data gathering methods and the age of data. And, of course, after trying multiple providers, those whose data show signs of the highest quality should be preferred.
Best practices for effective data sourcing
Effective data sourcing is crucial for businesses that rely on data-driven decision-making. Whether collecting first-party, second-party, or third-party data, companies must follow best practices to ensure they acquire high-quality, reliable, and compliant datasets. Below are key best practices to optimize data sourcing strategies.
Ensure data quality & accuracy
High-quality data is the foundation of effective decision-making, but raw data is often incomplete, inconsistent, or inaccurate. Ensuring data quality requires rigorous data cleaning and validation processes to eliminate errors, inconsistencies, and redundancies. Poor data quality can lead to faulty insights, financial losses, and operational inefficiencies, making data cleaning a critical step in data sourcing and management.
Data cleaning and validation are essential processes for ensuring accuracy, consistency, and usability in datasets. Data cleaning involves detecting and correcting errors by removing duplicates, handling missing values, standardizing formats, and resolving inconsistencies, which helps businesses prevent misleading analyses and improve data reliability. Data validation further ensures accuracy by cross-referencing information, detecting errors in real time, applying logical constraints, and conducting manual audits. Together, these processes help businesses maintain trust in analytics, reduce operational risks, and comply with data regulations, ensuring that their data remains a valuable asset for decision-making.
To sustain long-term data integrity, organizations should establish clear governance policies, automate data cleaning, and continuously monitor data quality. Investing in data validation and management tools ensures that businesses can rely on accurate and actionable insights for strategic decision-making.
Compliance with data regulations
As businesses increasingly rely on data for decision-making, ensuring compliance with data regulations is more important than ever. Data privacy laws such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States impose strict guidelines on how organizations collect, store, process, and share data. Non-compliance can result in hefty fines, legal action, and reputational damage, making it essential for businesses to implement strong data governance practices.
One of the key aspects of compliance is obtaining proper user consent before collecting personal data. Companies must ensure that users are fully informed about how their data will be used and provide an option to opt out if they choose. Additionally, organizations must limit data collection to only what is necessary and ensure that personally identifiable information (PII) is securely stored and protected from unauthorized access.
To maintain ongoing compliance, organizations should regularly audit their data practices, train employees on data privacy laws, and implement security measures such as encryption, anonymization, and access controls. By prioritizing compliance, businesses not only avoid legal risks but also build trust with customers, partners, and stakeholders, reinforcing their reputation as a responsible data-driven organization.
Selecting the right data providers
Selecting the right data provider is crucial for ensuring data accuracy, reliability, and compliance with industry standards. Businesses should evaluate providers based on data quality, update frequency, transparency in data collection methods, and compliance with privacy regulations such as GDPR and CCPA. It is essential to assess whether the provider offers fresh, structured, and relevant data that aligns with business needs while also considering their reputation and customer reviews. Requesting sample datasets and conducting trial analyses can help verify data integrity before making a long-term commitment. Ultimately, partnering with a trusted and compliant data provider ensures that businesses can make data-driven decisions with confidence while minimizing legal and operational risks.
Automating data collection with AI & machine learning
Automating data collection with AI and machine learning enhances efficiency, accuracy, and scalability by eliminating manual processes and reducing human errors. AI-driven tools can scrape, process, and analyze large volumes of data in real time, allowing businesses to extract actionable insights faster. Machine learning algorithms improve data quality by detecting patterns, correcting inconsistencies, and identifying anomalies automatically. Additionally, AI-powered natural language processing (NLP) can extract valuable information from unstructured sources such as text, images, and social media. By leveraging AI and machine learning for data collection, organizations can optimize decision-making, streamline operations, and gain a competitive edge in an increasingly data-driven world.
Why do companies need to source data?
The world now is more connected than ever before due to the growing reach of the internet and numerous devices capable of sharing and storing information. To put things in perspective, a total of 90% of the world's data was generated during the period of 2016-2018 alone.
Sourcing data offers multiple benefits for your operations.
- Big data analytics allows measuring every aspect of internal and external environment. The knowledge that comes with data analysis turns to competitive advantage and opportunities for new daring decisions in business management.
- Data sourcing is a pivotal procedure that allows companies to get the information they need in the correct conditions. Different companies and financial firms have varying data sourcing purposes. These include such objectives as portfolio management, lead generation, designing marketing and management strategies.
- Data sourcing is especially important for B2B marketing and sales. In fact, a survey has shown that in 2021 B2B marketers were making database acquisition and data quality their top priority, as the percentage of marketers lacking database strategies went down from 50% to 28% in a year. The main explaining factor behind such increased attention to data sources has to be the value that skillful sourcing of information brings to B2B marketing.
As data is crucial both in finding leads and in preparing the sales approach, it comes as no surprise that B2B sales require thorough research. And since there is more to know about firms than most companies are able to gather on their own, multiple sources are often employed for data collection.
To source data skillfully and unleash its maximum potential, it is suggested to follow the best data sourcing practices routinely. As sourcing data has become such an important part of modern business, a lot of useful knowledge about it has been accumulated.
Data sourcing challenges and concerns
Quality issues
Uneven quality in itself is one of the challenges of data sourcing that could be mitigated in the ways mentioned above and constant quality control efforts.
Data collected manually is liable to breaches, inconsistencies, and duplication. Even worse, one mistake is all it takes for your data to contradict actual data. Data quality issues should not be taken lightly.
If you have the ability, you should always buy data from professional sources. After all, low-quality, inaccurate data can potentially cost you more than investing in a data vendor.
Legal issues
Another major group of challenges is the legal rules of data management. Business is an increasingly global phenomenon, with international markets opening up more and more. And digital data admits no state borders. Yet, data governing still lacks the global character that would suit the contemporary business reality, with guidelines varying in different states or regions.
Security issues
Security of sensitive information is another challenge related to the aforementioned. Implementing sophisticated data security measures is required to prevent unauthorized access and ensure that sensitive information remains protected and unbreached.
Overall
Hopefully, with international communities employing their greatest legal minds to solve the legal issue and the greatest computer scientists for security, these challenges will be mitigated as much as possible in the near future.
Conclusion
Data sourcing, if once it was the professional interest of only programmers and data scientists, now is at least part of most entrepreneurs’ professional interest. It can greatly improve business intelligence, decision-making, and even allow you to create a useful data warehousing project for later use that could help you with both everyday decisions and long-term business strategies.
As always, when data is in question, the key things about sourcing are related to the quality, accessibility, and diversity of the data. All these aspects come down to choosing the right provider and getting the most value of the data that is used.
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