What to Look for When Hiring a Data Analyst

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Read more about author Rohail Abrahani.

Data analysts are specialists in statistics, mathematics, and computer science, enabling them to serve in a variety of departments, including operations analysis, financial analysis, and marketing analysis. A data analyst interprets data to influence business strategy, permitting firms to make data-driven pricing, scheduling, and other operational choices.

Why Hire a Data Analyst?

As declining margins, increased commoditization, and rising costs exert more pressure on virtually all industries, organizations must research more to identify new sources of income and competitive advantage.

The large quantity of data that organizations own represents the greatest untapped opportunity. However, organizations frequently lack the technical know-how, experience, and strategy required to utilize their data. This is when data analysts come to the rescue.

4 Steps to Find a Good Data Analyst

Your firm needs the expertise of a data analyst to make sense of the ever-increasing volume of data. If this is your first time employing a data analyst, it might be difficult to do so. 

The position of a data analyst is one of the most difficult to recruit for. Not only is there a shortage of talent in general, but it can also be challenging to assess potential candidates. Because the criteria are particularly specific to the needs of your firm, a method that takes the “one-size-fits-all” approach is unsuccessful. Let’s look at some of the things you should consider before hiring a data analyst. 

1. Evaluate Education Level

Formal education in the field of data analytics is not compulsory. However, employers should prefer that the candidate has completed some form of education or hands-on experience in the relevant field.

A degree in data analytics, statistics, or computer science is required to become a data analyst. However, an associate’s degree combined with relevant work experience should be enough to finalize the candidate.

2. Search Networking Groups

LinkedIn is one of the most robust online networks for data analysts. If you want to flourish in this field, then you should join a LinkedIn group. While LinkedIn group participation might be limited, you can still view the profiles of contributors and active members. By citing a shared topic or piece of information, you may transform your cold audience into a warm audience when reaching out to members of these communities.

On websites like Quora and Cross Validated, inquisitive and enthusiastic analysts may also be found. These websites provide analysts with a platform to be recognized as thought leaders by giving back to the community of aspiring analysts and anyone seeking a data-related position.

The data analyst community attends and presents at data analyst meetings offline. These events are excellent chances for sourcing and recruiting junior and senior analysts.

3. Prioritize Projects Over Resumes

How can you determine whether a candidate with pivot tables or descriptive analytics on their resume can implement these skills? Projects can help achieve this. They demonstrate a data analyst’s passions and ability to use his or her talents.

Data analysts utilize numerous available data sets to improve their abilities and portfolios. Projects let you differentiate between applicants. These projects can range from robust data science competitions on Kaggle, in which firms pay you to answer their data science problems, to personal studies of data from Foursquare and Fitbit.

If you have a sufficiently big data collection that you are willing to make public, sponsoring a Kaggle tournament can benefit your organization and be an excellent source of highly qualified incoming applicants. 

If you are interviewing a junior applicant who may not have much published work online, you might outline an analysis conducted by you and the model you are developing to assess their comprehension. You are searching for data problem-solving responses that demonstrate knowledge of various data kinds, statistics, analytical methodologies, and basic programming.

4. Look for Storytellers

Stakeholders are won over by data analysts who can play with data through narratives. Simply discussing unrelated data items will not get you ahead. Telling the story that the data tells is a vital ability. It takes knowing the facts of your market and business.

A variation in the data should communicate a feeling. A significant aspect of decision-making is persuading others to embrace your perspective. Occasionally, concrete numbers are sufficient. More often than not, an emotional connection between the audience and the facts is required to obtain buy-in.

Narratives add this feeling to data. Storytelling allows you to discuss the way data connects to individuals and situations. You can generate fresh ideas and rally support for a purpose.

Request that applicants illustrate a story using a chart or graph. Consider how much they emphasize quantitative data and how much they provide context to aid comprehension of the larger picture.

The story of the background of the data provides context and the emotion necessary to take action. It is a talent required of all data analysts.

5 Skills to Look for in a Good Data Analyst

Hiring data analysts with the right skills and mindset will assist them in utilizing their data to open new business doors. Let’s look at some of the skills which are needed by data analysts.

1. Data Preparation and Cleaning

An estimated 19% of a data scientist’s time is spent acquiring data, while another 60% is spent organizing and cleaning it. This shows that the data analyst hired by you will spend (on average) around 80% of their efforts preparing data to analyze. Therefore, they are required to be smart at it.

The process includes gathering data from multiple sources, such as software, databases, third-party data sets, surveys, etc., before performing the cleaning, which entails filling in any data gaps and removing inconsistencies and abnormalities. Frequently, datasets are required to be changed to be compatible with one another.

Your applicant should have experience dealing with various data types, such as structured and unstructured. They should also be able to efficiently clean and convert these datasets, including knowledge of ETL (extraction, transformation, and loading) methodologies.

2. Data Analysis

After data preparation, the next crucial skill is the ability to analyze the data. Here, original data is transformed into actionable business insights.

Strong statistical and mathematical skills are required for in-depth data analysis, as are hands-on experience with popular data analysis tools such as Jupyter Notebook, Tableau, and Microsoft Power BI.

Working with remarkable modern approaches to data analysis would be an added bonus. For instance, the knowledge graph is maturing as a method for collecting data from different sources and then forming connections between these data sources in order to acquire new knowledge from these connections. AI (artificial intelligence) and ML (machine learning) are also effective data analysis techniques.

3. Data Visualization

The true business value is not realized when data is looked over and insights are obtained. However, when they are effectively conveyed to other business stakeholders, they abruptly become valuable to the company.

This is why visualizing data is such an essential skill: the ability to take difficult insights and data and show them utilizing visuals, graphs, charts, etc., using remarkable techniques that can be easily understood by the audience.

To grasp what the statistics are saying and create a story about it that resonates with the audience requires a mix of abilities in data analysis on the one hand and storytelling and presenting skills on the other.

Familiarity with potent intelligence tools, such as Tableau and Power BI, is also highly advantageous, as these enable the user to effectively represent data intelligence and insights.

4. SQL (Structured Query Language)

SQL (Structured Query Language) is the original language for relational databases. Recent research indicates that SQL is the most in-demand skill for data analysts, making it essential for candidates to possess. 

Your applicant must understand the principles of SQL, such as tables, relational databases, SQL syntax, conditional filters, and indexes. Joins, subqueries, supporting programming languages such as PHP, and associated systems such as PostgreSQL and MySQL are intermediate abilities that would benefit an analyst’s arsenal.

5. Programming Languages (Python or R)

Programming languages related to statistics are utilized for predictive analytics and sophisticated data analysis. They enable data analysts to go beyond spreadsheets and even SQL, offering more in-depth and rapid analysis considerably.

Your applicant should have a grip on at least one language. Python and R meet the industry standards for programming languages. These easy-to-learn programming languages are quite alike in many aspects. Python is a programming language developed for general use, whereas R was designed specifically for statistical analysis.

Python is often utilized for data manipulation, machine learning, and analysis. However, R focuses on data purification, preparation, and visualization.


Remember, if you are looking for a data analyst, you need to check their projects first. After evaluating their projects, if you think the candidate is suitable, then don’t take a long time to hire them. Do it instantly – because such candidates are hard to find. Anybody can write skills on their resume, but it takes hard work to prove it. It can also give you an idea that the candidate knows how to work, and what level of projects they can make. We hope this guide will help you hire an amazing data analyst!

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