Data sourcing is a key part of a lengthened process of data analytics
Data sourcing is a key part of a lengthened process of data analytics

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Data Sourcing With 6 Conclusive Steps

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Today, data and information have become crucial to the success of modern businesses. To retrieve information, companies must turn to many different data sources. A data source could be a computer file, a database, or a web service.

However, businesses need good data sources to get insightful and actionable data. Data sourcing is a large field encompassing many forms of data in the business analytic process involving data cleaning, data analysis, and reporting.

In this article, I'll be taking you through what data sourcing is, the types of data sources, and the steps to data sourcing.


What Is Data Sourcing?

Data sourcing is the process of extracting data from multiple internal and external sources and transforming it into analysis that fulfils business purposes like creating a firm's data infrastructure for daily workflows and other business objectives.

Data sourcing is also synonymous with data collection, data harvesting, and data gathering, which involve gathering quality and quantitative information, storing them in data warehouses, and later processing them to glean actionable insights into the market.

Data sourcing has become an integral part of doing business with the market being so data-driven today.

Types Of Data Sources

Types of Data Sources

Primary & Secondary Data

  • Primary data - This type of data sourcing is also known as first-party data. It is data obtained directly from the source by the individual or organization that intends to use them. If you've ever filled out surveys, questionnaires, interviews, or looked up information on someone via email or profile search on Linkedin, which we all have done at some point in our lives, then you've been part of the primary data sourcing process of a company.

  • Secondary data - This form of data sourcing is also called second-party data. Secondary data can come from within an organization but more commonly originate from an external source. Its sources include health records, web searches, GPS data, census data, and many more. Secondary data sources allow researchers and data analysts to build quality databases to solve business problems.

External & Internal Data

  • External data - As the name suggests, this data source originates from outside an organisation, generally from other organisations or individuals. This data sourcing type is also called administrative data. Analysts use this data source to create detailed segment profiles on a particular region, trend, market, or demographic. Popular sources of external data are public sector organizations, educational institutions, private companies, and the rest.

  • Internal data - This form of data sourcing comes from within an organization from internal primary sources. These primary sources lie in CRM and historical records that have already been collected by the company and consist of customer data, transactions, and other customer records. Internal data are rarely available outside the organization unless the organization is mandated to produce such reports.

Other Sources

Nowadays, data can be extracted from the most unusual sources, from credit card purchases to location and even profile pictures. With the proliferation of smart devices, there are tons of data to be harvested, especially with alternative data being so big right now. There are many types of alternative data provided by diverse data vendors and providers.

Common Challenges of Data Sourcing

3 key common challenges of data sourcing

1. Freshness of Data

Data freshness, sometimes called data up-to-dateness, is one of the many elements of data quality. We live in a fast-paced world where information is released every second, hence data that gives us insight into the world right now and possibly the future can be termed fresh.

Data recency or freshness can be tough to achieve because information gets stale or outdated quickly. Obtaining fresh data regularly is hard work and takes resources, this is why some companies opt for selling databases and data that are outdated.

Data freshness is key in the data analytics process and its impact on businesses, thus it is crucial to seek data sources that are updated frequently.

2. Quality of Data

Data quality is essential for accurate analysis, you want to avoid data with the potential of inconsistency, duplication, and breach. Issues bordering on data quality should not be taken lightly.

Quality also means data sources that provide data with high quality and substantial data points, allowing you to do high-level analysis.

Keep in mind that, low-quality, inaccurate data can cost you more in the long run than investing in a data provider.

Data sourcing that involves the data of individuals is very sensitive. Although there have been consumer data policies and privacy regulations protecting our personal data, globally there is still a lack of common infrastructure that governs the collection and use of data.

You don’t want to embroil yourself in data legal issues just because your data sources are not compliant.

6 Steps To Data Sourcing

6 key steps to data sourcing

1. Understand Your Business Needs

Data is everywhere and it's important but not every piece of data is beneficial to your business. In this step, identifying your business goals and objectives can not be overemphasized, this knowledge is important in dictating your business data requirements.

This objective may be even further refined into very specific statements that lend themselves to analytical solutions.

2. Decide Resources For The Data Sourcing Stage

Now you've understood your business goals, it's time to decide the data sources that match your data requirements, the timeframe for data sourcing, does it have to be recurring or one-off, the intended data provider, the budget, and the amount of data needed.

3. Set Up Structures

This step is crucial for data cleaning and analysis in later stages, you want to avoid being bombarded by tons of data.

Also, set up structures for data optimization which is important for reporting and analysis. Decide on a format that the data needs to be in, and how the data will be utilized later in the next steps.

4. Look For Data Sources

Once structures for managing your data have been put in place, start looking for data providers that address the data sourcing challenges above. Proxycurl is an excellent data provider, our form of data sourcing is powered by APIs, which means it isn't manually prepared data, and chances of compromise on data quality and accuracy are reduced.

5. Start Collecting Data!

Congratulations! You made it to this step. You've found a data provider that understands your data requirements. It's time to begin data sourcing.

6. Monitor And Rectify

The final step in the data sourcing process is monitoring. After you've found a data source, it is important you monitor closely and periodically check back with your data provider to see if outcomes are as expected.

By monitoring, problems can be detected early on like lapses, and irregular data points that can brand your project a disappointing failure or worse make you spend too much time and resources fixing issues later.

Get Data Sourcing Right

Data sourcing is an integral part of data analytics, which in itself is part of a multi-process endeavour. If done right, it can be a powerful boost to your data analytics process which translates to business success. Insights you generate from accurate raw data can significantly increase the growth of your business. If you're ready to take the next step in data sourcing the right and efficient way, send us an email to get started.

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