10 Essential Skills Every Data Analyst Should Master

Data analysis is playing an increasingly vital role in today’s business world. Organisations rely on data to make informed decisions and are slowly realising the value that data professionals can offer in terms of innovation and profitability.

Against this competitive backdrop, it is becoming increasingly challenging to stand out as a data candidate. So what skills are the most essential for a data analyst to master in order to remain competitive?

1. Strong analytical skills and critical thinking

A large chunk of a data analyst’s role centres on analysing large sets of data and drawing meaningful insights from them. This demands strong analytical skills, such as the ability to identify patterns and trends in the data, and the means to draw predictions and recommendations based on these findings.

A data analyst’s role is to see what others may not and to challenge sometimes conventional thinking. This use of critical thinking means not being afraid to ask questions and refusing to accept things at face value, choosing instead to dig deeper.

Data analysts need to be able to approach a piece of analysis with an open mind and without preconceived ideas about the data they’re examining.

This allows them to consider a piece of analysis from several perspectives, working to the principle that any one problem might have several practical solutions. Cultivating such an approach can take time and practise.

2. Logic and problem-solving skills

In this same vein is the ability to problem solve. There is a perception that a data analyst needs to have exceptional maths skills, but while being statistically minded does help, thinking logically and being investigative is far more valuable.

Analysts not only need to be able to identify patterns in the data and interpret what they mean but they may also be expected to put forward potential solutions. Having a passion for problem-solving, along with a sense of curiosity about how things work, likely means that you will be more interested in getting to the bottom of an issue.

3. Attention to detail

The job of a data analyst is like searching for a needle in a haystack, so having a good eye for detail and being able to identify the smallest clues within a big data set are fundamental.

Accuracy is also crucial. A small error in your coding is enough to compromise an entire workflow. As such, you always need to be eagle-eyed when handling data, sensitive to even tiny mistakes that could lead to larger problems, such as inaccurate conclusions and recommendations.

4. Technical prowess

Programming languages such as Python and R are useful to clean the data and ensure it’s ready for analysis, and this skill is super important as good data is more likely to result in good analysis.

Many of these languages can be grasped at a basic level via online courses, which, if pursued independently, can go a long way to demonstrating a candidate’s commitment and passion for the field.

5. Strong communication skills

Whilst technical capabilities form the bedrock of the job, they can only take you so far and are rendered redundant if results cannot be conveyed to others. Data analysts must be able to communicate often complex information in a jargon-free manner that non-technical audiences, such as stakeholders, can digest and understand.

This requires an unshakable grasp of technical concepts, but also strong people skills and patience. As with all communication, understanding your audience is key, and the way in which you convey information will need to be adapted depending on the audience’s knowledge level, interest and priorities.

6. Storytelling and Data Visualisation

The strength of a person’s communication partly relies on their ability to tell a compelling story with data. This means getting your point across clearly whilst keeping your audience engaged. If your findings can’t be easily and quickly identified, then you’re going to have difficulty securing ‘buy-in’ from others.

Data visualisation plays a significant part in this – being able to present information in a visually appealing and concise way via high-quality charts and graphs is crucial to getting the message across. There is a plethora of software and technology able to help with this, so having a familiarity with tools such as Tableau, Microsoft Power BI, Qlik Sense, and Python libraries such as Matplotlib and Seaborn will be hugely beneficial.

7. Business acumen

Strong business acumen is the final complementary piece to the above skills. It’s all very well being able to present data in a compelling way, but you also need to be sure of the point that you are making and its relevance to the business.

In recent years data analysts have been expected to take an increasingly strategic role within an organisation, which means supporting its aims and overall mission. The context behind the data is what gives it meaning, so analysts need to have a comprehensive understanding of how the business operates, its goals and its service offering so that they can demonstrate the ways in which the data underpins these objectives.

8. Ability to collaborate

Communication skills are crucial, as is the willingness to share ideas and work as part of a team. Data analysis is not a solitary profession, instead, it involves engaging with a wide variety of colleagues across all levels of a business, which could include finance, marketing, operations, and senior leadership teams.

This ability to work with people across the business will not only help to establish trust between departments and ensure that everyone is pulling in the same direction but will also improve your knowledge of how the business functions and the role each department plays in the success of the company.

9. Adaptability

The field of data analysis is constantly evolving, so data analysts must be able to adapt to new technologies, methodologies, regulations, and shifting business priorities. This requires a willingness to learn, a desire to stay up to date with industry developments, and a readiness to be open to new concepts and approaches.

10. Ethical considerations

Data analysts also need to be considerate of the ethical implications of their work, such as being aware of issues around privacy, data security and bias. Understanding and following industry best practices and regulations is crucial, but so is the inclusion of different perspectives to ensure a balanced view, which is why having a diverse data team is fundamental.

Looking to break into the data industry and develop your data analyst skill? Learn about Rockborne’s data training programme.