Data analysts are essential in transforming data into actionable insights for informed decision-making, optimising operations, and gaining a competitive edge. Hiring the right person is crucial, requiring technical skills in data manipulation, statistical analysis, and visualisation. Domain expertise, problem-solving abilities, ethical considerations, and effective communication are also important. The right data analyst can unlock the full potential of data, driving data-driven decision-making for business success.
Technical Skills to Look For on a Digital Strategist’s CV
The technologies and tools you look for in your ideal data analyst may vary depending on your organisation and its data ecosystem. Therefore, hiring managers should consider the specific technical requirements of the role and the organisation’s technology stack when assessing a candidate’s technical skills on their CV. The below is not an exhaustive list of requirements, nor will candidates have expertise in every skill on this list.
Candidates applying for more senior or specialized data analyst positions are more likely to possess programming skills, while at lower levels, a basic understanding of coding principles and a willingness to improve is a more appropriate expectation.
- SQL (Structured Query Language): Used for data extraction, manipulation, and querying relational databases.
- Python: A versatile programming language commonly used for data manipulation, statistical analysis, and building data pipelines, as well as website and software development.
- R: A programming language designed for data analysis, known for its statistical analysis, data visualization, and machine learning capabilities.
- SAS (Statistical Analysis System): Widely used for statistical analysis and data modeling.
Candidates with more experience or those from data-focused educational backgrounds are more likely to be exposed to these tools. However, the increasing availability of online resources, tutorials, and open-source software has made it easier for individuals to gain familiarity with these tools independently.
- Excel: Proficiency in spreadsheet functions, formulas, and data analysis capabilities.
- Pandas: A Python library for data manipulation and analysis.
- NumPy: Python library for numerical computing and handling large arrays of data.
- MATLAB: Tool for data analysis, visualisation, and mathematical modelling.
- Apache Spark: Framework for big data processing and analysis.
Proficiency in statistical analysis methods and techniquesis is considered a fundamental requirement for this role. Statistical analysis forms the basis for deriving insights, identifying patterns, and making data-driven decisions. Even for entry-level positions, hiring managers should expect a basic understanding of statistical analysis.
- Hypothesis Testing: Knowledge of statistical tests such as t-tests, chi-square tests, and ANOVA.
- Regression analysis: Understanding linear regression, logistic regression, and other regression models.
- Time series analysis: Ability to analyse and forecast time-dependent data.
- Experimental design: Familiarity with design principles for controlled experiments.
It is essential that all applicants have proficiency in SQL and relational databases as this enables efficient data manipulation, transformation, and insights extraction. These skills are crucial at all experience levels, ensuring accurate analysis and diverse data handling.
- SQL: Proficiency in writing complex queries to extract and manipulate data from relational databases.
- NoSQL databases: Familiarity with non-relational databases such as MongoDB or Cassandra.
At all levels, data analysts must be proficient in data cleansing and preprocessing techniques. Whether it’s entry-level or more senior roles, the ability to identify and resolve errors, inconsistencies, missing values, and outliers in the dataset ensures data accuracy and quality.
- Data Cleaning Techniques: Identifying and handling missing values, outliers, and inconsistencies.
- Data transformation: Manipulating data formats, scaling variables, and handling categorical variables.
The necessity for applicants to have machine learning expertise depends on the specific requirements of the role and how keen the organisation is to focus on predictive modeling or advanced analytics. ML skills may be more appropriate in more specialised or senior data analyst positions that involve machine learning applications.
- Supervised Learning: Understand classification, regression, and model evaluation.
- Unsupervised learning: Knowledge of clustering, dimensionality reduction, and anomaly detection.
Soft Skills To Look For In Data Analysts
By possessing the below soft skills, data analysts can not only perform data analysis effectively but also communicate insights, collaborate with stakeholders, and contribute to the business’s overall success.
Data analysts should possess strong analytical thinking skills to approach problems logically and structured. They need to break down complex problems, identify patterns, and draw meaningful insights from data.
Effective data analysts are skilled problem solvers. They can identify business challenges, formulate analytical questions, and design appropriate approaches to find solutions. They are resourceful and can think creatively to overcome obstacles.
Data analysis requires meticulous attention to detail. Data analysts must be thorough in ensuring data accuracy, identifying outliers, and spotting any inconsistencies or errors that may impact the analysis. They should have a keen eye for identifying data quality issues.
The ability to communicate effectively is crucial for data analysts. They must translate complex technical concepts and data insights into clear and actionable messages for stakeholders. Clear and concise communication is essential when presenting findings, collaborating with teams, or explaining analytical methodologies.
Data analysts often collaborate with cross-functional teams, including business stakeholders, data engineers, and data scientists. Collaborating, listening actively, and contributing to a team environment is essential for successful project outcomes.
The best data analysts have a solid understanding of the business context in which they operate. They should be able to connect data insights with business objectives and provide meaningful recommendations to drive decision-making. Developing domain expertise helps them interpret data to align with the organisation’s goals.
Data analysis is a field that continuously evolves, with new tools, techniques, and technologies emerging. A robust data analyst is naturally curious, enthusiastic about learning, and stays up-to-date with the latest trends and advancements in the field. They are willing to explore new methodologies and technologies to enhance their skills and adapt to evolving data requirements.
Data analysts often work with tight deadlines and handle multiple projects simultaneously. Strong time management and organisational skills are essential to prioritise tasks, meet deadlines, and deliver high-quality results on time.
Data analysts deal with sensitive and confidential data, so ethical considerations are crucial. They should understand and adhere to data privacy and security regulations, ensuring the responsible use of data and maintaining data integrity.
Adapting to changing requirements, handling ambiguity, and quickly learning new tools or techniques is vital in a dynamic data environment. Data analysts should be comfortable with uncertainty and be able to adjust their approaches as needed.
Must Data Analysts Be Degree Qualified?
While many hiring managers prefer to see degree qualifications for data analyst roles, it is not an absolute requirement. Many employers prefer candidates with a degree as it provides a foundation in analytical skills and demonstrates a commitment to learning. However, the data analysis field is evolving rapidly, and alternative paths to gaining relevant skills, such as self-study, bootcamps, and practical experience, are becoming more prevalent. Employers can access a wider talent pool and tap into diverse perspectives by focusing on skills, experience, and demonstrated proficiency rather than solely on formal education. Hiring managers should consider the specific requirements of the role and assess candidates based on their abilities, practical experience, and problem-solving skills to find the best fit for the organisation.
Challenges Employers Face When Hiring Data Analysts
Employers who snap up the best available talent in this field are those that offer training and development programs, partner with educational institutions, offer competitive compensation packages, fostering a data-driven culture, and actively engage with the data science community. These options are often only available to larger organisations with massive budgets. Smaller companies often struggle to compete for the following reasons.
There is a significant demand for skilled data analysts but a shortage of qualified candidates. The competition for top talent in this field can make it difficult for employers to find suitable candidates with the required skills and experience.
The field of data analysis is rapidly evolving, with new tools, techniques, and technologies emerging regularly. Employers must keep up with these changes and accurately identify the skills and competencies needed for their specific business requirements.
Employers often have high expectations for data analysts, seeking individuals with a broad skill set, including strong technical expertise, domain knowledge, problem-solving abilities, and effective communication skills. Finding candidates who possess a combination of these qualities can be challenging.
Data analysts heavily rely on the availability and quality of data. Employers may face challenges ensuring data is accurate, consistent, and accessible. Without reliable data, data analysts cannot perform their work effectively.
Hiring and retaining skilled data analysts can be costly for organisations. The demand for these professionals often results in higher salary expectations, and attracting and retaining talent can be challenging within budget constraints.
Data analysts need not only technical skills but also the ability to collaborate with cross-functional teams, understand business needs, and effectively communicate insights. Ensuring a cultural fit and finding individuals who can work well within the organisation’s team dynamics can be challenging.
How We Can Help
Finding the right candidate for a data analyst role is crucial to drive successful data-driven initiatives and make informed business decisions. Leveraging the expertise of a specialist recruitment agency can help employers secure the right talent, maximise their chances of success, and achieve their data analysis goals effectively. Here’s how we do it.
We understand the specific skills, qualifications, and industry trends relevant to data analysis. Our in-depth field knowledge enables us to accurately assess candidates’ technical abilities and match them with our client’s unique requirements.
We handle the entire hiring process for you, including sourcing candidates, screening resumes, conducting interviews, and managing the logistics. Allowing us to handle these more mundane tasks saves saves you valuable time and effort in finding and evaluating candidates.
We have up-to-date insights into the data analytics job market, including salary benchmarks, skill demands, and emerging trends. This valuable information enables us to guide our clients on competitive compensation packages and market dynamics.
Our team are experts when it comes to assessing candidates’ skills, experience, and cultural fit for our clients. We can conduct comprehensive interviews online assessments, to ensure that candidates have the necessary qualifications and match your requirements.
We can enhance your employer brand by promoting your company’s unique selling points to potential candidates. We understand how to best market your job opportunity and highlight your organisation’s culture, benefits, and growth opportunities.
We can assist in developing retention strategies to ensure long-term engagement and satisfaction of hired data analysts. Provide insights and recommendations based on industry best practices often helps our clients retain top talent.
The information on this page is correct at the time of publication. However, as technology evolves so rapidly, the requirements of this role may change. For tailored hiring advice unique to your circumstances, please speak to our expert team of IT, Digital and Marketing recruiters.