Successful Data
Sourcing 2025: Best Strategies and Insights from Industry Leaders
Data is essential for solving the biggest challenges in business, science, and society. With the advent of generative AI, access to data has taken center stage for leaders and innovators around the world. Data-driven decision-making is increasingly what sets successful enterprises apart, and today, it’s imperative that companies leverage not only their own data but also equip themselves with relevant external sources.
However, finding the right external data is where many companies hit a wall. In this article, we’ll break down what data sourcing really means and share expert tips on how to do it right, drawn from Datarade's Successful Data Sourcing 2025 guide.
In simple terms, data sourcing is the process whereby companies collect data from various sources. This may include internal data from their own records, data collected directly from customers or other sources, or data obtained externally, from outside companies.
For the purpose of this article, we will focus on data acquired externally, sourced from data-providing companies. This may include raw or aggregated data types, sourced as first-, second-, or third-party data. The chart below represents various data types to take into consideration:
Internal data is extremely valuable. It can be used to gather information regarding an enterprise’s own operations, efficiency and history. Common data points to look at are past performance, sales and transaction reports, and marketing and customer details.
Well-structured records of internal data in the form of databases, CRMs, spreadsheets, and other internal documents, can increase its usability and value to the company. When companies change or implement processes or collect new data, considering how to effectively track and store the data for future use should be top of mind.
External data can take on different forms and support a company in a variety of ways. From enriching internal data for business intelligence, to deriving insights using fresh data in order to enhance a company’s position. Below are some benefits of using external data:
When acquiring external data, companies may face certain challenges along the way. Anticipating and planning for these challenges can make the data procurement process smoother, faster, and more efficient. Four common data sourcing challenges are:
There are a number of reasons why enterprise companies should be leveraging external data into their operational toolbox. Below, we will dive deeper into some key benefits of external data acquisition for enterprises, with some real-world examples.
As you can see, the sky is the limit when it comes to the applications of data for companies, and with advancements in the fields of artificial intelligence and machine learning, the use cases will continue to increase at a rapid pace.
While it may at first appear daunting to understand what type of data you need, there are a few key steps you can take to ensure you approach your data acquisition journey in an efficient and well-structured way.
The following chapters will provide guidance regarding considerations to make throughout the data sourcing journey, from determining the data you need, to evaluating various data sources, to strategies for building strong relationships with data providers.
When considering leveraging external data to strengthen your business, a key step is aligning your data needs with your business needs. Keep the following suggestions in mind as you move forward.
One of the first steps to take in your procurement journey is determining who will be responsible for the data procurement process with external vendors. This process includes finding, benchmarking, evaluating and choosing the best data partners.
Ideally this team should be cross-functional. Members could include someone in charge of finding and connecting with the data providers, data scientists or engineers who can assist in the evaluation of data samples, and someone who can oversee the commercial and legal aspects of the data partnership. Ensuring that multiple relevant stakeholders are involved in the process will help ensure that the most relevant data is procured.
You may not know exactly what data you need from the outset, but it is key to know what challenges or questions the data should answer, and what your use case is. Then, you can approach data marketplaces or providers, give them the end-to-end picture, and lean into their expertise on how to get there.
Make the objectives you’d like to achieve with this data measurable or comparable to a baseline metric, in order to quantify the success of your procurement.
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Evaluate the current data assets that your company has in order to make your data procurement journey as valuable as possible. Firstly, this will reduce the likelihood of data duplication by acquiring data that you already have. Secondly, focusing on current data type, storage, refresh rate, and other applicable factors, is important, so that any newly obtained data can fit the conditions of the assets currently in-house. Finally, having a clear understanding of your current stock and sources of data will help you ensure that any newly obtained data will complement the existing data.
Connected to the idea of taking stock of current data assets, you should also ensure that the data you will acquire will not exist in a silo, meaning that it is not accessible by the people that need to make use of it. For example, ensuring data compatibility in terms of type or delivery is key to ensure that everyone who needs to use the data can access it, or can connect it to other data sources.
To avoid data silos, you may ask the following questions:
It’s imperative that you clearly outline the data types, sources, and quality standards that best align with your business needs and goals. For example, do you need raw or aggregated data? Do you require a database or platform solution, or would you prefer to receive the data in flat file form? Understanding the current data capabilities of your organization will help you determine the best solution for future data procurement.
It’s important to be realistic during your data sourcing journey, considering resource constraints such as team capacity and budget. Data can be expensive, and depending on the type of data you acquire, it may require substantial storage and processing resources. For example, storing raw, unprocessed data can take up to twice the storage space while yielding 50% less usable data compared to processed data.
Understanding your team's technical capabilities and realistically estimating the time required to evaluate and process potential data sources is crucial. Allocating extra time for data assessment can help prevent delays and ensure smooth integration.
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When securing data partnerships with parties providing external data, companies should proactively consider how they will smoothly integrate and manage said data into their current systems, ensuring efficiency, accuracy, quality, management, and proper access.
When integrating data into existing systems, ensuring that the integrated data is accurate and complete will allow you to maintain a high standard of data quality. This process will occur after successfully integrating your new data into your systems or during the Proof of Concept (PoC) stage.
For companies acquiring external data for integration into existing systems, it's important to ensure that the new data is compatible with current systems and tools. Companies should also choose solutions with the capacity to grow in terms of volume and capacity, complexity, and supporting data from various sources of multiple data types.
This process should be appraised before the procurement process, in order to know what is required of the new data, rather than after the procurement process is done. This helps avoid costly mistakes.
To ensure new data is compatible, consider the following steps:
In the world of data, compliance is becoming an increasingly relevant topic. ‘Compliance’ is an umbrella term that may refer to rules and regulations around:
When engaging in data sourcing, it is advisable to benchmark a few different sources before determining which one(s) will be the best fit for your needs. There is no exact number, as this depends on the use case and specific requirements.
Do a quick background search online of your potential new data partner. You can check Datarade for customer reviews and insights into data providers. Do they have positive reviews from current and past customers? During the evaluation process, were they easy to reach and helpful in answering business and technical questions you had? Did they respond in a timely manner?
Leverage data marketplaces, which may be able to provide customer reviews, or give feedback on business experiences with data vendors.
Data accuracy is of the utmost importance to ensure the use case that it will be supporting will be successfully met. Most data providers will provide sample data for you to evaluate. While this is not necessarily 100% reflective of the entire dataset, it will give you a sense of the data points available, and provide data for you to compare—either with other data sources or with an internal benchmarking dataset.
Example:
If evaluating employee or company data, test the sample against a list of contacts or companies which you know are 100% accurate.
Key Questions to Ask During Evaluation:
Datasets may contain various levels of comprehensiveness, covering different attributes, time periods, or geographic regions. Determine what data must be included as a baseline, then use that baseline to compare the data received from data providers.
Example:
If you're evaluating real estate prices in a city, completeness may refer to:
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Consider the time frame of the data you will need—whether historical, present, or future. Additionally, understand how frequently the data is updated, and what the latency is.
Latency refers to how long it takes for the data to be collected, processed, and delivered to its final destination.
When receiving data samples from multiple providers, try to obtain comparable samples (e.g., same location, time frame, attributes). This allows you to:
The above considerations are a starting point. There are hundreds of types of data, and depending on the use case, various ways to interpret them for your organization’s needs. In addition, keep in mind how you will track the data evaluation from various providers.
Example:
If evaluating Point of Interest (POI) or footfall data from multiple providers, ask for a sample covering the same location and time period to ensure fair comparison.
If you're evaluating firmographic or B2B contact data, compare it with your own verified dataset to see:
Beyond just the data, you should evaluate providers on a wide range of factors. Create a comparison matrix like the one above to:
When considering a partnership with a data provider, keep in mind that there are various types that exist. Depending on the specific needs you have and how you intend to use the data, it will be important to understand the various types, and how to approach your procurement journey. Below are some common types of data partnerships:
Companies that specialize in collecting, analyzing, and selling data are called syndicated data sources. These vendors aggregate data from multiple sources, standardize it, and package it into reports or subscriptions that businesses can purchase. Examples include market research firms like Nielsen and Gartner.
In exclusive partnerships, companies agree to share data exclusively with each other, typically for a specific purpose or within a certain market. This arrangement provides a competitive edge by granting access to unique data and insights that competitors cannot obtain. Example: A healthcare provider may share patient data exclusively with a pharmaceutical company to aid in drug development.
Open data collaborations involve partnerships where companies, governments, non-profits, and research institutions share data freely and transparently. These collaborations are often designed to drive innovation, research, or public benefit.
In this model, companies license their data to other organizations for a specific purpose or time period. This allows the licensee to access and use the data without full ownership. Example: A mapping company licensing its geospatial data to a ride-sharing app to enhance navigation services.
While sourcing the right data for your company is of the utmost importance, other aspects of the data partnerships should also be seriously considered when choosing data providers to work with. It’s equally important to consider factors beyond the dataset itself, including vendor credibility, responsiveness, and reputation within the industry.
During the initial discussion, data evaluation, or POC phase, did the data provider provide a strong level of support? When faced with questions about processes and the data, were they responsive and helpful, taking time to explain any questions or concerns?
During the “get to know each other” phase, any lapses or misunderstandings in communication should be addressed early—because if left unchecked, they may cause larger problems later on, when your systems may be more reliant on their data.
Also, in today’s global landscape, cultural norms may differ between organizations. Rather than seeing this as a blocker, acknowledge and adapt accordingly.
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It’s critical that your data providers can disclose how they collect their data. This might require signing an NDA (Non-Disclosure Agreement) or MNDA (Mutual NDA), depending on your use case.
You should also:
When forming a data partnership, it’s essential to establish clear contractual terms. These ensure that both parties are aligned on expectations. Consider the following:
When choosing a data provider, assess whether they are adaptable and innovative.
Key questions to ask:
Avoid providers who jump on every new trend without demonstrating consistency and reliability.
As AI transforms procurement and external data sourcing becomes a business necessity, your organization must make its strategy adaptable, scalable, and sustainable.
The businesses that will thrive are those that invest in people, technology, and flexible models.
Data is essential for solving the biggest challenges in business, science, and society. With the advent of generative AI, access to data has taken center stage for leaders and innovators around the world. Data-driven decision-making is increasingly what sets successful enterprises apart, and today, it’s imperative that companies leverage not only their own data but also equip themselves with relevant external sources.
However, finding the right external data is where many companies hit a wall. In this article, we’ll break down what data sourcing really means and share expert tips on how to do it right, drawn from Datarade's Successful Data Sourcing 2025 guide.
In simple terms, data sourcing is the process whereby companies collect data from various sources. This may include internal data from their own records, data collected directly from customers or other sources, or data obtained externally, from outside companies.
For the purpose of this article, we will focus on data acquired externally, sourced from data-providing companies. This may include raw or aggregated data types, sourced as first-, second-, or third-party data. The chart below represents various data types to take into consideration:
Internal data is extremely valuable. It can be used to gather information regarding an enterprise’s own operations, efficiency and history. Common data points to look at are past performance, sales and transaction reports, and marketing and customer details.
Well-structured records of internal data in the form of databases, CRMs, spreadsheets, and other internal documents, can increase its usability and value to the company. When companies change or implement processes or collect new data, considering how to effectively track and store the data for future use should be top of mind.
External data can take on different forms and support a company in a variety of ways. From enriching internal data for business intelligence, to deriving insights using fresh data in order to enhance a company’s position. Below are some benefits of using external data:
When acquiring external data, companies may face certain challenges along the way. Anticipating and planning for these challenges can make the data procurement process smoother, faster, and more efficient. Four common data sourcing challenges are:
There are a number of reasons why enterprise companies should be leveraging external data into their operational toolbox. Below, we will dive deeper into some key benefits of external data acquisition for enterprises, with some real-world examples.
As you can see, the sky is the limit when it comes to the applications of data for companies, and with advancements in the fields of artificial intelligence and machine learning, the use cases will continue to increase at a rapid pace.
While it may at first appear daunting to understand what type of data you need, there are a few key steps you can take to ensure you approach your data acquisition journey in an efficient and well-structured way.
The following chapters will provide guidance regarding considerations to make throughout the data sourcing journey, from determining the data you need, to evaluating various data sources, to strategies for building strong relationships with data providers.
When considering leveraging external data to strengthen your business, a key step is aligning your data needs with your business needs. Keep the following suggestions in mind as you move forward.
One of the first steps to take in your procurement journey is determining who will be responsible for the data procurement process with external vendors. This process includes finding, benchmarking, evaluating and choosing the best data partners.
Ideally this team should be cross-functional. Members could include someone in charge of finding and connecting with the data providers, data scientists or engineers who can assist in the evaluation of data samples, and someone who can oversee the commercial and legal aspects of the data partnership. Ensuring that multiple relevant stakeholders are involved in the process will help ensure that the most relevant data is procured.
You may not know exactly what data you need from the outset, but it is key to know what challenges or questions the data should answer, and what your use case is. Then, you can approach data marketplaces or providers, give them the end-to-end picture, and lean into their expertise on how to get there.
Make the objectives you’d like to achieve with this data measurable or comparable to a baseline metric, in order to quantify the success of your procurement.
Evaluate the current data assets that your company has in order to make your data procurement journey as valuable as possible. Firstly, this will reduce the likelihood of data duplication by acquiring data that you already have. Secondly, focusing on current data type, storage, refresh rate, and other applicable factors, is important, so that any newly obtained data can fit the conditions of the assets currently in-house. Finally, having a clear understanding of your current stock and sources of data will help you ensure that any newly obtained data will complement the existing data.
Connected to the idea of taking stock of current data assets, you should also ensure that the data you will acquire will not exist in a silo, meaning that it is not accessible by the people that need to make use of it. For example, ensuring data compatibility in terms of type or delivery is key to ensure that everyone who needs to use the data can access it, or can connect it to other data sources.
To avoid data silos, you may ask the following questions:
It’s imperative that you clearly outline the data types, sources, and quality standards that best align with your business needs and goals. For example, do you need raw or aggregated data? Do you require a database or platform solution, or would you prefer to receive the data in flat file form? Understanding the current data capabilities of your organization will help you determine the best solution for future data procurement.
It’s important to be realistic during your data sourcing journey, considering resource constraints such as team capacity and budget. Data can be expensive, and depending on the type of data you acquire, it may require substantial storage and processing resources. For example, storing raw, unprocessed data can take up to twice the storage space while yielding 50% less usable data compared to processed data.
Understanding your team's technical capabilities and realistically estimating the time required to evaluate and process potential data sources is crucial. Allocating extra time for data assessment can help prevent delays and ensure smooth integration.
When securing data partnerships with parties providing external data, companies should proactively consider how they will smoothly integrate and manage said data into their current systems, ensuring efficiency, accuracy, quality, management, and proper access.
When integrating data into existing systems, ensuring that the integrated data is accurate and complete will allow you to maintain a high standard of data quality. This process will occur after successfully integrating your new data into your systems or during the Proof of Concept (PoC) stage.
For companies acquiring external data for integration into existing systems, it's important to ensure that the new data is compatible with current systems and tools. Companies should also choose solutions with the capacity to grow in terms of volume and capacity, complexity, and supporting data from various sources of multiple data types.
This process should be appraised before the procurement process, in order to know what is required of the new data, rather than after the procurement process is done. This helps avoid costly mistakes.
To ensure new data is compatible, consider the following steps:
In the world of data, compliance is becoming an increasingly relevant topic. ‘Compliance’ is an umbrella term that may refer to rules and regulations around:
When engaging in data sourcing, it is advisable to benchmark a few different sources before determining which one(s) will be the best fit for your needs. There is no exact number, as this depends on the use case and specific requirements.
Do a quick background search online of your potential new data partner. You can check Datarade for customer reviews and insights into data providers. Do they have positive reviews from current and past customers? During the evaluation process, were they easy to reach and helpful in answering business and technical questions you had? Did they respond in a timely manner?
Leverage data marketplaces, which may be able to provide customer reviews, or give feedback on business experiences with data vendors.
Data accuracy is of the utmost importance to ensure the use case that it will be supporting will be successfully met. Most data providers will provide sample data for you to evaluate. While this is not necessarily 100% reflective of the entire dataset, it will give you a sense of the data points available, and provide data for you to compare—either with other data sources or with an internal benchmarking dataset.
Example:
If evaluating employee or company data, test the sample against a list of contacts or companies which you know are 100% accurate.
Key Questions to Ask During Evaluation:
Datasets may contain various levels of comprehensiveness, covering different attributes, time periods, or geographic regions. Determine what data must be included as a baseline, then use that baseline to compare the data received from data providers.
Example:
If you're evaluating real estate prices in a city, completeness may refer to:
Consider the time frame of the data you will need—whether historical, present, or future. Additionally, understand how frequently the data is updated, and what the latency is.
Latency refers to how long it takes for the data to be collected, processed, and delivered to its final destination.
When receiving data samples from multiple providers, try to obtain comparable samples (e.g., same location, time frame, attributes). This allows you to:
The above considerations are a starting point. There are hundreds of types of data, and depending on the use case, various ways to interpret them for your organization’s needs. In addition, keep in mind how you will track the data evaluation from various providers.
Example:
If evaluating Point of Interest (POI) or footfall data from multiple providers, ask for a sample covering the same location and time period to ensure fair comparison.
If you're evaluating firmographic or B2B contact data, compare it with your own verified dataset to see:
Beyond just the data, you should evaluate providers on a wide range of factors. Create a comparison matrix like the one above to:
When considering a partnership with a data provider, keep in mind that there are various types that exist. Depending on the specific needs you have and how you intend to use the data, it will be important to understand the various types, and how to approach your procurement journey. Below are some common types of data partnerships:
Companies that specialize in collecting, analyzing, and selling data are called syndicated data sources. These vendors aggregate data from multiple sources, standardize it, and package it into reports or subscriptions that businesses can purchase. Examples include market research firms like Nielsen and Gartner.
In exclusive partnerships, companies agree to share data exclusively with each other, typically for a specific purpose or within a certain market. This arrangement provides a competitive edge by granting access to unique data and insights that competitors cannot obtain. Example: A healthcare provider may share patient data exclusively with a pharmaceutical company to aid in drug development.
Open data collaborations involve partnerships where companies, governments, non-profits, and research institutions share data freely and transparently. These collaborations are often designed to drive innovation, research, or public benefit.
In this model, companies license their data to other organizations for a specific purpose or time period. This allows the licensee to access and use the data without full ownership. Example: A mapping company licensing its geospatial data to a ride-sharing app to enhance navigation services.
While sourcing the right data for your company is of the utmost importance, other aspects of the data partnerships should also be seriously considered when choosing data providers to work with. It’s equally important to consider factors beyond the dataset itself, including vendor credibility, responsiveness, and reputation within the industry.
During the initial discussion, data evaluation, or POC phase, did the data provider provide a strong level of support? When faced with questions about processes and the data, were they responsive and helpful, taking time to explain any questions or concerns?
During the “get to know each other” phase, any lapses or misunderstandings in communication should be addressed early—because if left unchecked, they may cause larger problems later on, when your systems may be more reliant on their data.
Also, in today’s global landscape, cultural norms may differ between organizations. Rather than seeing this as a blocker, acknowledge and adapt accordingly.
It’s critical that your data providers can disclose how they collect their data. This might require signing an NDA (Non-Disclosure Agreement) or MNDA (Mutual NDA), depending on your use case.
You should also:
When forming a data partnership, it’s essential to establish clear contractual terms. These ensure that both parties are aligned on expectations. Consider the following:
When choosing a data provider, assess whether they are adaptable and innovative.
Key questions to ask:
Avoid providers who jump on every new trend without demonstrating consistency and reliability.
As AI transforms procurement and external data sourcing becomes a business necessity, your organization must make its strategy adaptable, scalable, and sustainable.
The businesses that will thrive are those that invest in people, technology, and flexible models.
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