What is internal data?

Internal data is private or proprietary data that your organization owns, controls or collects.  Examples of internal data include sales data, website data, customer information and financial data.

Since you’re responsible for collecting, maintaining and storing internal data, it’s more reliable, accurate and credible as compared to external data. However, you may require a lot of resources to collect and manage data internally.

Collecting internal data may also require professionals with in-depth knowledge of research methodologies and data collection techniques.

How can you collect internal data?

You can collect internal data from a wide variety of sources such as your website, CRMs, customer records and apps. You can also create it yourself.

1. Secondary data

When you collect from sources that someone else owns, it's called secondary data.

For example, when you use data on your organization from Google Analytics (website), Salesforce (customers) or HubSpot (leads and customers), you're using secondary data.

2. Primary data

If the data that you want doesn’t exist, you can create your own data. That's called primary data.

For example, if you create surveys for your customers or interview people to gather feedback on your product, you're using primary data.

To collect primary data, you can use standard data collection techniques such as:

  1. Observation: Collect the required data by making direct observations.
  2. Questionnaires: Collect data by surveying your sample subjects using questionnaires.
  3. Interviews: Collect data by interviewing your sample subjects to uncover deep insights.
  4. Focus group discussions: Collect data by conducting a moderated discussion among your sample subjects on a particular subject.

Each method has its own advantages and disadvantages. Choose a method according to your requirements, abilities and available resources.

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See also

Articles you might be interested in

  1. The complete guide to data collection [Ebook]
  2. 4 data collection techniques
  3. Primary and secondary data: Definition and example
  4. Types of data sources