Coresignal logo
Back to blog

A Guide to Secondary Data: Analysis, Benefits, Importance, Sources, and More

Coresignal

Coresignal

September 20, 2021

Competently using data has proven to be the path towards success for many entities across different fields. In business, it meant competitive advantage, innovation, and profit. However, in order to achieve all these benefits, companies need to understand and take advantage of different kinds of data analysis and handling practices. One important distinction to be aware of is between primary data analysis and secondary data analysis. The importance of collecting new data is often and rightly stressed. So, let’s look closer at why it’s vital to utilize secondary data as well, and what benefits can come from analyzing secondary data.

What is secondary data?

As mentioned, when businesses collect data themselves, it’s considered primary data. So, what makes up secondary data? Simply because of the fact that it has already been collected by a primary source and is now being used by someone else (a secondary source) for their own purposes. 

Likewise, primary research is when the data is collected by researchers themselves and is essentially new data. Conversely, secondary research or secondary data analysis is when analysts utilize data from previous research or outside primary sources instead of collecting data themselves.

Therefore, secondary data is any data that is already available before the research begins. Secondary data collection involves getting data that has already been produced or recorded, instead of producing new data. More specifically, secondary data is information originally created and used by a primary source for a specific purpose that is then collected and analyzed by a second party. 

Secondary data sources

Primary research is done with the data collected from authentic sources. This means that, for example, researchers conduct interviews or carry out field tests to get the data for the analysis.

Sources of secondary data, on the other hand, don’t need to be authentic. Any source information collected for whichever purpose can be a source for secondary data analysis. Naturally, this means that there are many such sources.

For businesses and other organizations, all these sources can be divided into internal and external. Internal sources are those that come from within the organization. For example, researchers may use existing data from accounting, customer feedback, or operational reports when doing marketing research to improve a firm’s marketing strategies. This data is still secondary as it was originally recorded for other purposes, but as it originates within the same company as the marketing research itself, it’s internal data.

All other sources, those that are outside of the organization, are external sources of secondary data. Of course, this group of sources is extensive and varies immensely. Here are some of the most common examples of such sources.

  • Public legal sources and government publications (including public libraries and their sources for administrative data, as well as census data)
  • Media (either broadcasted, printed, or otherwise released by TV, newspaper, or information from other media companies)
  • Literature and literature review (including releases from academic publishers, like Cambridge University Press or Sage Publications)
  • Industry reports and other published market or industry research
  • Professional data providers
  • International organizations 

Primary data vs. secondary data

The difference between primary and secondary data is not only source type or whether they have been used before. These two types of data usually differ in their features which have important implications when choosing which type of analysis to conduct.

Data collected for primary research is raw data that can be structured according to the goals of the analysis. Secondary data usually has already been structured or processed, often more than once, thus at first, it is presented for analysis in a form that was meant to suit something else.

Qualitative data is more often used in primary research. Secondary research is more associated with quantitative data, such as administrative data or census data, often studied by social scientists. However, there are also valid qualitative data research methods that can be applied for secondary data in marketing research or other business-relevant analysis. Here are some advantages and disadvantages of secondary data analysis as compared to primary research

Advantages of secondary research

Saving time and effort

Collecting secondary data for research is much faster and easier than primary data collection. This allows researchers to save time by going straight to the analysis process. Additionally, researchers stay focused on the research goals without having to worry about finding and utilizing primary sources, which can be a lot of work on its own.

Cost-effectiveness

Secondary research is generally the cheaper option. It is quite costly to organize focus groups, hire people to question persons of interest, or build and maintain various sensors able to record large amounts of data. Meanwhile, secondary data may cost next to nothing to get as all the data one could use is already available and often easily accessible from free institutions like public libraries. Even when such data is not enough and one has to turn to data providers or otherwise spend money to acquire secondary data, it’s still cheaper than primary data collection.

Cleaned and structured data

Secondary data has often been cleaned before using it for primary purposes. This means that the data already ascends to at least some data quality standards. There may be many quality issues with just gathered primary data. Thus researchers have to put additional resources to clean it. Additionally, secondary data is usually structured, which, as mentioned, may not suit the particular requirements of secondary research at hand, but it does bring some organization and readability, which can prove time-saving.

The large volume of data

Finally, there’s only so much primary data that researchers can collect before having to start the actual analysis. With secondary data, there’s no such limit. There is more information available in secondary sources than one could handle in a lifetime of data analysis. Thus, secondary data researchers certainly don’t have many restrictions on what sources to choose from.

Disadvantages of secondary research

Differing requirements

The biggest among the disadvantages of secondary data research is that one can’t quite be sure that the data will suit the goals of the research exactly. Primary data analysts can gather exactly what they need. Secondary researchers, on the other hand, work with what they were able to find from what is available.

Control over the collection process

Secondary data analysts can’t be completely sure that the data was collected according to rigid standards and therefore is valid and representative. They may check the source and try to find out as much about the collection as possible, but there will always be a degree of uncertainty.

Lacking uniqueness

Primary researchers work on unique data that no one else has had before. Therefore they have a greater chance of arriving at unique insights. Secondary data analysis can be unique too, but only for as long as no one else uses the same data for the same research purposes.

Five Metrics for evaluating and analyzing secondary data

The first step of secondary data analysis is the evaluation of data. Although, as mentioned, it’s impossible to have complete quality control over secondary data, researchers can still exercise some control. The following criteria are crucial when evaluating secondary data in order to determine their suitability for the analysis at hand.

  1. Reliability of the source

How trusty is the data source? Is it a reputable data provider or an established publisher? Researchers should also check to find out as much as possible about the circumstances of data collection.

  1. Relevance

Not all trustworthy information is relevant data for a particular analysis. Researchers must first establish clear analysis goals to determine data relevance and then check what kind of information particular data sources hold.

  1. Overall quality

Of course, analysts need to pay attention to any errors, redundancies, or other possible issues with the data they’re considering for usage. Poor data quality costs businesses between $9.7 million and $14.2 million every year. 

  1. Freshness

How new is the data? When was it last updated? Outdated information may no longer answer the questions raised by the analysis goals.

  1. Accessibility

The format of the data and how it is accessed are also pivotal for data analysis. The easier it is to access data, the more efficient and reliable secondary research will be.

The importance of secondary data analysis in business

For years business heads and data analysts have been lamenting the fact that most data never get to be analyzed. For example, a few years ago, it was estimated that only about 0.5% of all data is ever analyzed and utilized.

Having this in mind, one can’t help but wonder whether it’s worth spending money on additional data production when so much existing data never gets used. Of course, primary research is often necessary, for example, when new qualitative data is required, but it is equally important not to overlook the potential of secondary data.

Especially when it comes to secondary quantitative data, the large volumes of alternative data already available would suggest first going for secondary research. Thus, combining the two research methods is the surest way for businesses to benefit from data analysis.

Wrapping up

Researchers can either collect new data for analysis or get secondary data from some of the many diverse sources. Whichever path is chosen, the key to success and business benefits is, as always, attention to data quality and choosing the right method for the right goals.

Related articles

Susanne Morris

March 03, 2021

A Quick Guide to Data Interpretation

Data interpretation is a data review process that utilizes analysis, evaluation, and visualization to provide in-depth findings...

Read more

Coresignal

August 05, 2021

What is Data Intelligence?

Data intelligence refers to all of the tools and informational assets that companies leverage to help derive insights with the...

Read more

Coresignal

June 07, 2021

Data Sourcing: Benefits, Source Types, Providers, Challenges, and More

Data sourcing is how companies extract and integrate data from multiple internal and external sources. This process creates a...

Read more

Coresignal's fresh web data helps companies achieve their goals. Let's get in touch.

Contact us

Use cases

LinkedInTwitter

Coresignal © 2021 All Rights Reserved