5 Key Questions for Evaluating External Secondary Data

5 Key Questions for Evaluating External Secondary Data

These days, information is everywhere. With just a mobile device and an internet connection, everything from maps to reviews to news articles can be accessed with a mere tap of the finger. In fact, data just might be the most valuable resource in the world at this point.

However, there’s a pretty significant catch to this all-you-can eat information buffet. Data can be uploaded as easily as it can be accessed, which leaves its consumers vulnerable to unverified, biased, and at times demonstrably false assertions hiding under a veneer of credibility. And unfortunately, it seems like unreliable secondary data is everywhere.

When bad data informs business decisions, the costs can be extremely high. But with a little know-how, it’s possible to rise above the statistical noise. Employing a multi-factor process to evaluate secondary data is one way to determine that the information you’ve found is factually sound and suited to your needs. Here are five questions to ask yourself the next time you come across a tempting data tidbit that seems — and might just be — too good to be true.

1. Who Is the Data Provider? (And What Is Their Goal?)

When it comes to secondary data, brand recognition counts for quite a bit. Just as consumers tend to trust well-known brand names over generic labels, familiarity with your data source goes a long way in understanding whether or not you can trust the information they provide. For example, data from a government agency is typically a safe bet — you know where and who it’s coming from, and it’s generally unbiased.

However, with regard to market research reports, figuring out who’s who can be a little trickier. With so much business conducted over the internet, it’s easy for anyone to publish information and pass it off as factual. When you see something from an unfamiliar publisher, be sure to dig deep and investigate just who it is you might be working with. Are they cited in independent, well-known publications? Do they have a broad and established clientele? Do they have a LinkedIn page and verifiable employees?

Additionally, consider whether or not the source is unbiased. Data from an organization with a vested stake in the results — like a corporation or a political party — is almost certain to be slanted in favor of its own interests. If a food processing company releases a study touting the health benefits of a snack they manufacture, chances are quite high that either the methodology was skewed or the data snapshot was selective.

2. When Was the External Secondary Data Collected?

Good data can, at times, have a very short shelf life. Mega-mergers and demergers occur. Regulations change (sometimes frequently). The economy takes a nosedive. Congress passes a major tax overhaul.

Data sources that are several years old are not going to be able to take those things into account, so it’s important to make sure the information you’re working with is as current as possible. Verifying the publication date and the year-to-year scope of the projection or forecast will help you understand whether or not the data has incorporated the time-sensitive variables that can make or break a source’s accuracy.

3. What Kind of Data Was Collected?

Sometimes, a secondary data point that seems promising may not end up being applicable to your research aims. Data that is too broad (or too specific) will hamper your ability to make sound deductions, and potentially lead to erroneous conclusions.

Working with a market research company can help you determine the customized data targets that will best suit your needs and provide a valuable leg up over the competition. Data measurements from firms like The Freedonia Group vary from industry to industry — from dollars, to pounds, to squares, to square feet, and more — based on historical knowledge of what is commonly used in that market and which units best meet client expectations.

4. How Was the Data Collected?

While perhaps a lesser understood component of data gathering and analysis, the method through which secondary data is collected is important as well. For instance, in the age of call screening, a phone survey probably isn’t going to do the trick if you’re looking to gauge millennial attitudes on a topic or brand. However, an online survey might net you more relevant results.

To understand whether the information you’ve retrieved is optimized to your needs, take a look at how it was collected. And when in doubt, ask — a legitimate data source should be able to provide you with a credible explanation of their methodology and results.

5. Consistency Is Key: How Does the Data Measure Up?

When you’re using secondary data to inform decisions, the chances are high that you won’t be looking at one single point, but many. Faced with results that might vary from source to source, it will take a little more elbow grease to understand which piece of information is most accurate and why. Assess the reliability of your source — if an outlier data point is coming from a historically well-regarded firm or organization, investigate to determine whether or not they’ve accounted for something the other sources failed to. If you can’t determine the validity of the secondary data or the source, it might need to be discarded.

By working with a top-flight firm, you’re ensuring that the data you’re working with is top flight as well, and that it will serve as the best starting point to solve business problems and inform future decision-making.

To Find Out More

Understanding how to evaluate secondary data is just one step toward making fully informed business decisions.  To learn how to use data to estimate market sizing, download “The 5 Keys to Estimating Market Size for Strategic Decision Making”, the latest white paper from Freedonia Custom Research.

About the Author:

Alecia Mouhanna  is a Corporate Analyst at The Freedonia Group, where she researches and writes about a diverse range of topics, including construction and building materials, chemicals, packaging, and more.   

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