Whether you’re an employee looking to hire IT talent to bring meaning to your data, or a graduate or professional looking to enter the field, there can be confusion around the difference between a Data Analyst vs Data Scientist.

While they sound similar on the surface, and they both work with data, their roles do differ significantly. So, to help you understand which data professional is the right fit for your needs, in this blog we’ll discuss what they do, why they’re different and the value they can add to a business.

What is a Data Analyst?

What was once referred to as ‘business analytics’ has today evolved into ‘data analytics’ with the advent of computer software providing the perfect springboard. Programs such as Excel allowed analysts to mine more data and garner deeper insights. These days, data analysts are employed by many organisations and their daily tasks revolve around the collection, organisation and analysis of data from within the business and their industry.

The raw data may come from a variety of sources including transactional data or customer files. The Data Analyst is responsible for interpreting large volumes of information and extracting meaningful insights. A key aspect of their role is to report their findings to departments within the business, so that it can be used to inform better decisions, guide key operating strategies and drive growth.

Related: Why should data analytics be a priority for Hong Kong business leaders?

What is a Data Scientist?

The role of the Data Scientist is focused around developing innovative methods and tools to extract data that helps solve high-level and complex problems. Data scientists design and create tools, algorithms, predictive models and automations to address business challenges and opportunities, using their skills in statistical methods and data visualisation to derive meaningful information from complicated and unstructured data.

In essence, data scientists are essentially problem solvers who can identify the key questions that matter, and then utilise a variety of software and tools to pull and analyse the data to discover the answers.

Data Analyst vs Data Scientist: Key differences

While at times their roles may cross over, there are some important differences in the work of a Data Analyst vs Data Scientist. Where a Data Analyst makes meaning from existing raw data, generally with the aim to uncover how or why something happened, a Data Scientist is more focused on what may happen in the future, creating innovative data modelling techniques to capture and understand new data. A breakdown of their common job responsibilities highlights further the key differences.

Data Analyst: data analysis, forecasting and querying; creation of dashboards for reporting; identification of patterns and trends in datasets; perform predictive, descriptive and diagnostic analytics.

Data Scientist: data mining, cleaning and scrubbing; statistical analysis using algorithms; developing data infrastructures; data visualisation; creating programming and automation techniques to improve processes.

What is driving the demand for these roles?

Data-driven skills are becoming increasingly in demand as organisations rely more and more on analytics and insights on big data to inform and guide their business operations and direction. The Future of Jobs Report 2023 backs this up, with big data analytics cited as one of the biggest drivers of job growth, with 80% of companies surveyed saying they are likely to implement big data analytics over the next five years. The flow on effect of this will be a spike in the demand for both data analysts and data scientists.

Related: Check out our expert management advice

Top tips for hiring a Data Analyst

If you need someone to make sense of the data in your business, a Data Analyst is the right person for the job. They can bring valuable insights to the table that will allow you to make informed decisions about key areas that impact your growth and bottom line, including operations, pricing and scheduling.

So what are the key skills and attributes you should look for when hiring a Data Analyst?

According to Victoria Tsang, specialised IT recruiter at Robert Half, there are some essential skills and qualifications the candidate must have to be considered.

"When hiring a Data Analyst, look for someone with ideally three years minimum experience in extract/transform/load (ETL) design and data visualisation, with proven SQL / Python skills and an ability to communicate technical information effectively via presentations and reports to business stakeholders and management. They are also required to have basic knowledge on visualisation tools such as Tableau / Power BI / Qliksense. If they also have experience with database design, programming and statistical analysis, that is big tick."

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Top tips for hiring a Data Scientist

If you need an expert to interpret data, both within your business and the wider industry, and to develop innovative models to collect, clean and validate complex data sets that will allow you to fine-tune your business strategies and take your growth to the next level, a Data Scientist is the ideal choice.

Essential skills to look for in candidates include five plus years of experience in data science, advanced data mining, mathematics and statistical analysis skills, high level of proficiency with Excel, PowerPoint, SQL and programming languages software, and experience working in a fast-paced team environment. In addition to these attributes, a master’s degree in a related field such as statistics or mathematics along with relevant professional certifications should be highly regarded.

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Talk to our experienced team today

At Robert Half, we can help you understand if a Data Analyst or Data Scientist is the best fit for your needs, and help you find the right person. Or if you are a candidate looking to launch your career in these exciting fields, we can help you find the ideal role. Talk to our experienced Hong Kong recruitment team today or upload your resume online and we’ll be in touch.