How to assess basic data analysis and decision-making skills in candidates the right way

What does basic data analysis even look like?

Basic data analysis isn’t really “basic.” It involves the foundational skills that enable employees to work effectively with information in any business context.

 These foundational skills appear across introductory data analytics course curricula and syllabi because they provide the groundwork for more advanced analytics and decision-making.

 According to Dogukan Ulu, Senior Data Analyst at TestGorilla, basic data analysis involves these four steps:

1. Understanding the question/problem

This involves clarifying what you’re trying to measure, track, or improve, and framing it in a way that can be answered with data. 

Skills required for this step: critical thinking, problem definition, and basic business logic

2. Collecting and cleaning relevant data

This includes gathering information from datasets across common sources like spreadsheets, databases, or business software, then removing duplicates, fixing formatting issues, and handling missing values. 

Skills required for this step: data collection, data cleaning, ensuring data quality and data integrity, and spreadsheet/database literacy

3. Summarizing and exploring the data

Ulu says this involves “[Modifying] the raw data and [calculating] descriptive stats (averages, medians, counts), visualizing trends, [and] spotting anomalies.” 

Skills required for this step: analytical reasoning, basic statistics, data visualization, and the use of common data analysis tools like Excel, Google Sheets, or SQL workbenches

4. Interpreting results

Ulu calls this “Telling a story with the numbers.” It includes explaining to stakeholders what the results mean, why they matter, and what action they suggest. 

Skills required for this step: interpretation, contextual reasoning (the ability to apply data insights within the specific business context), and clear communication of findings

Pro tip: Juan Montenegro, a senior business analyst and founder of WalletFinder.ai, emphasizes the importance of cognitive skills like reasoning over technical skills like tool knowledge: “I’ve hired across roles where data-driven thinking is essential, and one thing I’ve learned is that tools come and go but clear reasoning doesn’t.”

What does decision-making look like when it’s informed by data analysis? 

Candidates can use basic data analysis for data-driven decision-making, a skill which IBM defines as “an approach that emphasizes using data and analysis instead of intuition to inform business decisions.” This “involves leveraging data sources such as customer feedback, market trends and financial data to guide the decision-making process. By collecting, analyzing and interpreting data, organizations can make better decisions that more closely align with business goals and objectives.”

Data-driven decision-making isn’t reserved for executives or data scientists. Even roles requiring basic data analysis and decision-making must turn everyday data – like budgets, schedules, or customer feedback – into reliable decisions that help the business.

Why do these skills matter? Here’s the proof.

As we mentioned above, companies that know how to use their data stay ahead. Here’s some proof: 

• According to various resources, including Deloitte, companies that use analytics strategically gain a sustainable competitive edge by making sharper, more precise business choices. 

• This is backed up with research. For instance, one study shows that stronger data analytics competency – which can include foundational skills like cleaning, summarizing, and interpreting data – significantly improves decision quality. 

• A slew of other studies show that data-driven companies do better than competing companies. 

Use multi-measure evaluations to assess data analysis and data-driven decision-making in candidates

Making the most of your business data means hiring employees who actually possess the skills needed for basic data analysis and data-driven decision-making. But this is easier said than done. 

According to our recent research, 63% of employers say it’s harder to find great talent than it was in 2024. But some of these employers aren’t using skills-based hiring (where they evaluate candidates based on their skills).

For instance, we found that only 67% of those employers that don’t use skills-based hiring are satisfied with their hires. Compare this with those that do use skills-based hiring: 82% of them are satisfied with their hires. 

Our research also found that a high percentage (91%) of employers specially using a multi-measure approach are making quality hires. 

This is why we recommend taking a multi-measure approach to skills-based hiring. This might mean you:

• Combine multiple assessment methods, including practical skill tests

• Look at multiple “measures” (like data analysis and decision-making, and perhaps even other measures like software knowledge or cognitive skills) 

This approach will enable you to gain comprehensive insight into each candidate’s abilities. 

To help you out, here’s our handy three-stage process for doing this.

Step 1. Start by using data analysis and decision-making tests 

Skills tests are one of the most effective ways to quickly assess candidates’ skills. Plus, our research shows that 84% of employers feel satisfied with the hires they’ve made using skills tests.

With TestGorilla, for instance, you can ask all your candidates to complete these quick tests:  

• Working with Data test: Assesses their understanding of data handling concepts, alongside their abilities to perform basic data analysis and interpretation and work with charts and graphs

• Data-Driven Decision-Making test: Looks at their ability to collect and organize data, make decisions based on criteria, communicate data, and evaluate data decisions

Both tests will give you an objective baseline of candidates’ data and decision-making skills. 

You can combine them into a single assessment, and you can even add other role-relevant tests, such as software skills tests (for assessing their proficiency with data analysis tools) or soft skills and cognitive ability tests (for assessing must-have skills like communication and reasoning).

TestGorilla will automatically benchmark candidates against one another, ranking the most proficient to the least proficient and highlighting those most suitable for interviewing. This vastly speeds up the assessment process and enables you to assess a large number of candidates. 

You can then shortlist the top-scoring candidates and move on to the interview stage.

Step 2. Create a standardized interview process with meaningful questions

Once candidates have been screened for proficiency and shortlisted, it’s time to move on to the interview. The key here is to use behavioral interview questions, which can help determine how candidates have behaved in previous work situations and how they might perform for you in the future. 

Kristin Tara Horowitz, CEO & Co-Founder of The Pad Climbing, recommends this question, which her team often uses:

“Tell me about a time when the data suggested one thing, but your gut said another. What did you do and why?”

She says,  “We listen for how [candidates] balance intuition with analysis, how they communicate uncertainty, and whether they check their biases. A strong candidate will show humility and the ability to adapt. Lack of that showing means they won't work with us in our organization well: we are always learning and adapting and 'best practice’ is always something meant to be tested – that's how we find the best!”

Wynter Johnson, Founder and CEO of Caily, recommends having a conversation involving actual data:

“If you want to test your candidates' data skills, you've got to give them data. We'll usually use actual data from our marketing or development teams…. We'll start by asking them for their initial insights, then start talking through our analysis process and asking for input.

“It's a great approach,” she adds, “because it not only tests their data skills, but also how well they communicate and collaborate.” 

Whatever questions you decide to ask or conversations you decide to have, keep interviews structured, with pre-set questions and topics and consistent scoring. This way, every candidate is evaluated fairly. 

You can use interview scorecards to do this. Try adapting the scorecard below to give your scoring some structure and consistency.

→ Discover more interview questions for assessing data-driven decision-making. 

Step 3. If needed, use practical tasks to find your top pick

By this point, you’ll likely have a few front-runners for the role. Using automatically scored skill tests and a scorecard should eliminate bias and give you a fair comparison of your candidates. Simply combine the scores, and a clear winner should emerge.

However, if two (or more) of your candidates have scored within the same range, you may also want to consider a final tie-breaker exercise. This not only helps you differentiate between equally strong applicants but also gives you insight into the full spectrum of their abilities. 

Consider something practical and different from the previous tasks – something like a case study presentation or problem-solving task that’s highly relevant to your role.

Dogukan Ulu, our Senior Data Analyst, likes to use the following tasks to assess his candidates for positions at TestGorilla (where, if hired, they’ll be working with candidate test data):

• Scenario-based data challenge. He offers some anonymized candidate test data (scores, demographics, hiring outcomes) and asks, “The client complains the test is unfair. Can you investigate and recommend actions?” This measures the candidates’ data cleaning skills, SQL knowledge, pattern recognition, and ability to communicate findings clearly.

• A/B result interpretation. He shows them results from two different test formats and asks them to determine if one performs significantly better and why.

• Root cause analysis. He gives them a dashboard showing a sudden drop in test pass rates and asks them to find likely causes.

Using highly role-specific tasks, like Ulu does, can give you even more confidence that the person you hire will be able to apply their data analysis and decision-making skills in the real situations they’ll face on the job.

Find candidates who possess both data analysis and decision-making skills with TestGorilla

Assessing basic data analysis and decision-making skills the right way means assessing these skills together using a multi-measure approach. 

This involves using screening tests like our Working With Data test and Data-Driven Decision-Making test, asking behavioral interview questions, and implementing scenario-based tasks (if necessary). 

Ready to find a candidate who possesses these skills and can help your company gain a competitive advantage? Book a free demo or start your free TestGorilla plan today.